esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Metrics and Baselines  
The evaluation metrics we applied for the performance of the models were mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Suppose that \(x_{1},x_{2},\ldots,x_{N}\) are the actual values and \(\hat{x}_{1},\hat{x}_{2},\ldots,\hat{x}_{N}\) are the corresponding prediction values. The formulations of MAE, MAPE, and RMSE are as follows:  
\[MAE(x,\hat{x})= \frac{1}{N}\sum_{i\in[1,N]}|x_{i}-\hat{x}_{i}| \tag{13}\] \[MAPE(x,\hat{x})= \frac{1}{N}\sum_{i\in[1,N]}|\frac{x_{i}-\hat{x}_{i}}{x_{i}}|\] \[RMSE(x,\hat{x})= \sqrt{\frac{1}{N}\sum_{i\in[1,N]}(x_{i}-\hat{x}_{i})^{2}}\]  
To conduct the comparison experiments, we selected the following proposed models:  
* FC-LSTM. A network that combines long- and short-term memory networks with full connection [38].
* STGCN. Spatio-temporal graph convolutional network, a network that integrates graph convolution with one-dimensional convolutional units [30].
* DCRNN. Diffusion convolutional recurrent neural network, a network that integrates an RNN with diffusion convolution [16].
* ASTGCN. Attention-based spatio-temporal graph convolutional network, a network that adopts spatio-temporal attention for spatial-temporal prediction [32].
* GraphWaveNet. A network that proposes an adaptive adjacency matrix with 1-D diffusion convolution in graph convolution [31].
* STTNs. Spatial-temporal transformer network, a network which is based on the self-attention mechanism and makes prediction by stacked spatial-temporal blocks [19].
* ASTGNN. Attention-based spatial-temporal graph neural network, a network that adopts dynamic GCN and a trend-aware attention mechanism [20].  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Dataset** & **Number of Sensors** & **Time Range** \\ \hline PEMS03 & 358 & 1 September 2018–30 November 2018 \\ PEMS04 & 307 & 1 January 2018–28 February 2018 \\ PEMS08 & 170 & 1 July 2016–31 August 2016 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset descriptions.  
FOGS. First-order gradient supervision, a spatial-temporal model that utilizes first-order gradients to train and a learning-based spatial-temporal correlation graph to make predictions [39].
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 4 5G Functions and Features to Support the Information Flow within Industry 4.0
### Programmable Logic Control (PLC) and Human Machine Interface (HMI)
Using an effective physical layer protection method [132] to secure the transmission of OFDM-based waveform, the security and reliability of 5G's uRLLC services is enhanced.
## 3 Methodologies
### Logistic Map
#### 3.1.1 Lyapunov Exponent of the Logistic Map  
Let us consider two iterations of the logistic map starting from two closely spaced values, namely, \(x_{0}\) and \(x_{0}+\delta x_{0}\). These values will become \(x_{1}\) and \(x_{1}+\delta x_{1}\),..., \(x_{n}+\delta x_{n}\). The expansion of \(f(x)\) for \(x_{n}\) yields  
\[\delta x_{n}=f^{\prime}(x_{n-1})\delta x_{n-1} \tag{2}\]  
assuming that \(\delta x_{n}\) is very small. Thus, the separation distance between the two values after \(n\) steps, \(\delta x_{n}\), is related to their initial separation \(\delta x_{0}\) as  
\[\left|\frac{\delta x_{n}}{\delta x_{0}}\right|=\prod_{i=0}^{n-1}\left|f^{ \prime}(x_{i})\right| \tag{3}\]  
For large values of \(n\), Equation (3) is expected to vary exponentially, like  
\[\left|\frac{\delta x_{n}}{\delta x_{0}}\right|=e^{\lambda_{L}n}(\text{for large $n$}) \tag{4}\]  
and therefore, the Lyapunov exponent \(\lambda_{L}\) is defined as  
\[\lambda_{L}=\lim_{n\rightarrow\infty}\frac{1}{n}\sum_{i=1}^{n}\ln\left|f^{ \prime}(x_{i})\right| \tag{5}\]  
Figure 2: The embedding (**a**) and extraction algorithm (**b**).  
If \(\lambda_{L}>0\), then the two values diverge from each other for large \(n\), indicating chaos. However, if the two values converge to a fixed point or a limit cycle, they approach each other, corresponding to \(\lambda_{L}<0\).  
Therefore, a system can be considered chaotic or not by examining the sign of the Lyapunov exponent. For \(0\leq r\leq 4\), the graph of the Lyapunov exponent for the logistic map is as follows.  
As shown in Figure 3, the value of \(r\) indicating chaotic sensitive dependence is around \(r=3.5688\), as this is where the Lyapunov exponent becomes positive. After the value of \(r\) surpasses approximately \(3.5688\), the graph appears to be positive. Thus, values of \(r\) greater than that will be used as parameters for the logistic map.
# Physical and Respiratory Rehabilitation in Spinal Muscular Atrophy: A Critical Narrative Review
## 4 Discussion
### Limits and Implications  
Several limitations must be addressed. First, the high heterogeneity among included studies in terms of participants, as the most represented were people with SMA I, poses challenges in generalising findings to other SMA types. Moreover, variations in intervention modalities, intensity, and outcomes measured across studies make any direct comparison difficult. Additionally, this review included only studies published in English, which may introduce a potential language bias that future reviews could overcome by setting no limitation to a study's language. Future research with more robust methodologies and broader participant samples is imperative to elucidate the efficacy of physiotherapy and respiratory interventions in people with SMA. Specifically, future studies on aerobic training and hydrotherapy should focus on defining the optimal duration, frequency, and intensity of these interventions and assess their long-term effects on motor function and fatigue management. Regarding respiratory interventions, current research is mainly focused on SMA I, and limited studies have been conducted on SMA II and III. Future research should explore the role of respiratory interventions in preventing respiratory decline and optimising pulmonary function before severe deterioration occurs. Early rehabilitation interventions should also be planned in light of recent evidence that highlights how early interventions prevent the deterioration of motor and respiratory functions, as well as preventing scoliosis onset [72; 73]. Finally, individual interventions should be compared to combined approaches to identify the most beneficial rehabilitation strategies, as well as explore the role of assistive technologies such as power mobility [74]. Moreover, recent advances in assistive technologies and digital health solutions could open new pathways for SMA rehabilitation. The development of wearable sensors and AI-assisted rehabilitation programs could enhance the monitoring of functional progress and enable personalised interventions [75; 76].  
From a clinical perspective, aerobic training and hydrotherapy could be added to rehabilitation programs, but careful monitoring of intensity and duration is essential to prevent excessive fatigue, particularly in individuals with SMA III. Resistance training appears safe and well tolerated, especially in SMA II and III, but its effectiveness may depend on individualised protocols and its integration with pharmacological treatments such as nusinersen. Given the progressive nature of SMA, early and proactive respiratory interventions are crucial, particularly in SMA I, to prevent respiratory decline. NIV should be prioritised whenever possible, with frequent reassessment to ensure optimal parameter settings. Furthermore, a multidisciplinary approach combining physiotherapy, respiratory support, and pharmacological treatment could enhance patient outcomes.
# Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach
## 3 Machine Learning HRTF Restoration  
Research domains such as speech recognition, natural language processing and computer vision have demonstrated that a more general data-driven method often beats traditional knowledge-based signal processing methods in the long run, as the data can keep growing in the future [30]. In image processing, recent developments in machine learning have shown great potential in noise reduction in images, image inpainting, colorising old photos or videos and neural style transfer [17; 18; 31; 32; 33; 34; 35; 36; 37]. These tasks can be considered to be quite similar to restoring distorted SH-interpolated HRTFs. Some examples include variants of fully connected neural networks (NN), auto-encoder (AE) convolution auto-encoder (CAE) [36], residual network (ResNet) [38] and generative adversarial networks (GANs) [39].  
Most machine learning models require large amounts of labelled data to produce excellent results. However, currently the number of available HRTF databases is very limited. There are only a total of 233 HRTF datasets freely available combined in (Spatially Oriented Format for Acoustics) SOFA format at the time of conducting this research [40; 41]. Compared to the data size used to train image processing machine learning models, which can be in the region of hundreds of thousands of images, HRTF data are far too few to generalise well or to train sophisticated models. However even with such limited data, SH-interpolated HRTF restoration is potentially achievable using machine learning algorithms and is investigated herein. The advantage in using a machine learning model is that the result can be improved in the long run when more labelled data are available in the future. The disadvantage is that current methods are somewhat tedious in actual implementation as they require an extra step after interpolation and can take up significant processing time if the number of interpolated HRTFs is large.  
An overview of the proposed method is shown in Figure 1. A subset of HRTFs are selected from a database to represent a sparse HRTF measurement set. These HRTFs are then interpolated in a  
Figure 2: Actual ipsilateral HRTF measurement vs. SH-interpolated HRTF (green: Actual HRTF measurement, blue: SH-interpolated HRTF), \(M=1\).  
traditional SH HRTF interpolation manner. After the interpolation, each HRTF measurement is fed into the machine learning model for restoration. The output size for this study was chosen for the interpolation process based on the number of HRTF measurements of the original dataset, which is typically over 2000 measurements (depending on the dataset). The restored HRTFs output from the machine learning model can then be easily compared with the true HRTF measurements. In this section, we first discuss the data preparation and format, and then introduce the baseline model used in this study before improving it with different enhancement techniques, including weight decay, dropout, early stopping, etc.
# Low-Cost IoT Air Quality Monitoring Station Using Cloud Platform and Blockchain Technology
## 5 Blockchain Platform Simulation  
This section explains the simulation of exchanging data between stations using the Ethereum Blockchain platform, which uses \"Ganache\" software to run Ethereum Blockchain on a local machine. It is necessary to use the plugin \"Truffle Suite Framework\" [22] for initiating, compiling, and migrating smart contracts, all within the \"Solidity\" programming language.  
The simulation tools Ganache and the Truffle Suite operated seamlessly on a personal laptop with the following main specifications: Intel Core i5 processor and 8 GB of RAM. This laptop ran on the Windows 10 operating system, with Ganache version 2.7.0. While we will not delve into a detailed code description, we will provide a brief overview of one of its critical components.  
The first line in Algorithm 1 indicates the version of the compiler used by Ganache. This smart contract Solidity code uses \"DataExchange,\" which allows users to update a string of data associated with their Ethereum addresses using the \"ExchangeData\" function. The data, which are stored in a public mapping project, are accessible to anyone who needs to read the data associated with specific addresses on the Ethereum Blockchain. The data [Message.Sender] can access the mapping project using the sender's Ethereum address (Message.Sender) as the key.  
Another important part of the code is testing a smart contract that simulates the data exchange between two IoT stations, which is described as follows in the form of a pseudo-code in Algorithm 2:  
This pseudo-code outlines the steps involved in testing a smart contract for the data exchange between two IoT stations, including the setup, simulated data exchange, and verification of the stored data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**CO\({}_{2}\) PPM (MQ135)**} & \multicolumn{1}{c}{**CO PPM (MQ7)**} & \multicolumn{1}{c}{**Temperature**} & \multicolumn{1}{c}{**Humidity**} \\  & & (\({}^{\circ}\)**C**) & **(\%)** \\ \hline  
\hline \end{tabular}
\end{table}
Table 6: Sample of the results gathered by the station during afternoon time in summer.
## 4 Deployment of Cloud-Native Core Network in ComEdge
### Evaluation Results
The advantage of deep reinforcement learning in deploying CNFs on the ComEdge platform was fully demonstrated. It is worth noting that DQN-CNFDA can predict the placement of network functions based on user's service request behavior, while R-CNFDS does not involve any learning from user behavior, leading to a significant gap between the two. Furthermore, MDCS and ENE-CNFDS each consider minimizing deployment costs and controlling the inter-node signaling quantity, which reduces the gap with DQN-CNFDA. However, due to the lack of utilization of the relationship between dynamic user requests, deployment costs, and control traffic, MDCS and ENE-CNFDS exhibited significant cost fluctuations (from 13:00 to 17:00). Therefore, using MDCS and ENE-CNFDS results in higher deployment and operational costs. Specifically, the DQN-based scheme achieved a maximum cost reduction of up to 6.9% compared to the engineering-experience-based CNFs deployment scheme. Based on the experimental results in the aforementioned platform, the proposed DQN-CNFDA better grasps the relationship between dynamic and time-varying user service requests, edge-core network deployment, and operational costs compared to engineering experience and other algorithms. Consequently, it reduces system expenses and enhances resource utilization efficiency.
## 2 FBMC/OQAM Structure
As PAPR value increases, it indicates that the system requires high-power amplifiers (HPAs) to operate in their saturation region, which is critical because any further increase in the signal amplitude pushes the HPA to operate in its nonlinear region, eventually causing distortion to the transmitted signal. In OFDM systems, the reduction methods are separately applied over each symbol, and the PAPR of each symbol is calculated independently. Unfortunately, this does not apply to the FBMC/OQAM signal, owing to the overlapping data blocks. To accurately calculate the PAPR, the overlapping between the symbol blocks must be taken into consideration. The PAPR of pure FBMC/OQAM in dB can be expressed as follows:  
\[\text{PAPR}(\text{dB})\;=\;10\log_{10}\frac{\max_{\text{T}(\text{m}-1)\leq t \leq\text{mT}}\left|\text{s}(\text{t})_{\text{m}}\right|^{2}}{\text{E}\left[ \left|\text{s}(\text{t})\right|^{2}\right]} \tag{6}\]  
From Equation (6) the numerator represents the peak power in the duration of the \(\text{m}^{\text{th}}\) input data block, \(\text{T}(\text{m}-1)\leq\text{t}\leq\text{mT}\), and the denominator \(\text{E}\left[\left|\text{s}(\text{t})\right|^{2}\right]\) represents the average power of the FBMC signal. \begin{table}
\begin{tabular}{c c c c c} \hline \hline K & H0 & H1 & H2 & H3 \\ \hline
2 & 1 & 0.707107 & - & - \\
3 & 1 & 0.911438 & 0.411438 & - \\
4 & 1 & 0.971960 & 0.707107 & 0.235147 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PHYDYAS filter coefficients.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 3 Analysis of the Forecasting Results
### Point Forecasting Results  
#### Benchmark Models' Point Forecasts  
The benchmark models used the same set of variables selected by [PERSON] for their computation on R. The Libraries \"caret\" and \"gbm\" were used for the SGB while the \"mgcv\" library was used for fitting the GAM. A visualisation of the point forecasts produced from the test set given the training set  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Selected Variable** & **Relative Importance** \\ \hline WS\_40\_mean & 4.379310 \(\times\) 10\({}^{1}\) \\ WS\_60\_mean & 4.117128 \(\times\) 10\({}^{1}\) \\ WS\_62\_max & 1.411519e \(\times\) 10\({}^{1}\) \\ WD\_60\_mean & 4.687078 \(\times\) 10\({}^{-1}\) \\ WD\_60\_stdv & 3.892787 \(\times\) 10\({}^{-1}\) \\ WS\_60\_stdv & 6.227314e \(\times\) 10\({}^{-2}\) \\ Tair\_min & 1.768753 \(\times\) 10\({}^{-4}\) \\ Tair\_mean & 0.000000 \(\times\) 10\({}^{0}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variable selection by the SGB model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **ANN Lasso** & **ANN Lassocv** & **BMLasso** \\ \hline WS\_62\_min & 0.088897 & 0.314418 & 3.643221 \(\times\) 10\({}^{-1}\) \\ WS\_62\_max & 0.410781 & 0.337446 & 2.614169 \(\times\) 10\({}^{-1}\) \\ WS\_60\_mean & 0.253464 & 0.120781 & 1.454732 \(\times\) 10\({}^{-1}\) \\ WD\_60\_mean & 0.000287 & 0.000134 & 9.436018 \(\times\) 10\({}^{-5}\) \\ WD\_60\_min & – & 0.000068 & – \\ WD\_60\_max & \(-\)0.000591 & 0.000007 & – \\ WD\_60\_stdv & \(-\)0.015175 & \(-\)0.005907 & \(-\)4.402683 \(\times\) 10\({}^{-3}\) \\ WD\_20\_mean & 0.000834 & 0.000048 & – \\ WD\_20\_stdv & \(-\)0.001630 & — & – \\ RH\_min & \(-\)0.000793 & 0.000169 & – \\ lag1 & 0.119809 & 0.094290 & 8.940951 \(\times\) 10\({}^{-2}\) \\ WS\_40\_mean & – & 0.148194 & 1.581057 \(\times\) 10\({}^{-1}\) \\ WD\_20\_min & – & \(-\)0.000036 & – \\ WD\_20\_max & – & 0.000082 & – \\ WS\_60\_stdv & – & – & 2.898686 \(\times\) 10\({}^{-1}\) \\ Tair\_min & – & 0.000590 & \(-\)7.744566 \(\times\) 10\({}^{-5}\) \\ Tair\_mean & – & \(-\) & \(-\)2.311307 \(\times\) 10\({}^{-4}\) \\ Total No of Variables & 10 & 14 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Variables and their coefficients from the Lasso.  
using these models is shown in Figure 4. The dataset was split into training and test set in the ratio of 75%/25% for all three individual forecasting models.  
In Figure 4, the highest wind speed (at 62 m hub height of the WT) values for both the actual and forecasts hovers around 20 m/s. We can also infer that the wind speed very close to 5 m/s is the densest plot of the wind speed with a density value very close to or a bit above 0.10. In the density plot, the lines showing the actual point forecast values and the lines showing the benchmark models' point forecasts values are very close, indicating the performance of the models in forecasting. This reflects a considerable acceptable performance for the two benchmark models. Table 5 presents a summary of the accuracy metrics.
## 2 System Model and Problem Formulation
### Markov Decision Process  
In general, an MDP can be expressed as the tuple \(\{S,A,R,P,\gamma\}\), where \(S\) is the state space, \(A\) is the action space, \(R\) is the reward function, \(P\) is the state transition probability, and \(\gamma\) is the discount factor.  
(1) State space, \(S\): Each state, \(s_{t}\), comprises two elements and is expressed as  
\[s_{t}=\{x(t),y(t),\frac{R_{1}(t-1)}{R_{1}(t)},\ldots,\frac{R_{k}(t-1)}{R_{k}( t)},\ldots,\frac{R_{K}(t-1)}{R_{K}(t)}\}. \tag{11}\]  
When \(t=0\), \(s_{0}=\{x(0),y(0),0,\ldots,0\}\), where \((x(t),y(t))\) is the projection of the UAV's coordinate on the XY plane, and \(\frac{R_{k}(t-1)}{R_{k}(t)}\) is the ratio of the signal strength received from the \(k\)-th SN at the current time to the previous time. Instead of the instantaneously  
Figure 2: Composition of mission completion time.  
received signal strength in each time slot, we use its ratio to express the system state because the ratio can implicitly indicate the change in distance from the UAV to each SN.  
(2) Action space, \(A\): The UAV has four actions to choose from, \(a_{t}\in\{north,east,south,west\}\). The UAV moves the same distance every time, that is, the side length of the cell is \(l\). We also set some restrictions so that the UAV cannot leave the square grid world. For example, the UAV will not be able to select certain actions at the boundary.  
(3) Reward function, \(R\): The reward function maps status action pairs into reward values, which represent task objectives, and the reward function, \(R\), is composed of the following parts:  
* \(r_{dc}\) is the reward for data collecting. Every time the UAV collects data carried by IoT devices, it will obtain a fixed reward, \(r_{dc}\).
* \(r_{inc}\) is the constant movement reward, which is applied to actions with increased signal strength from all devices.
* \(r_{dec}\) is the constant movement punishment, which is applied to actions with decreased signal strength from all devices.
* \(r_{b}\) is the punishment for the UAV trying to fly away from the region boundary.  
(4) State transition probability, \(P\): \(P\) is represented by \(P(s_{t+1}\mid s_{t},a_{t})\), expressed as the probability distribution of the next state, \(s_{t+1}\), given the current state, \(s_{t}\), and the action, \(a_{t}\), taken and represents the dynamic characteristics of the entire environment.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 6 Numerical Results
### Network Access Ratio Performance
Compared with the experimental group, it is still consistent with the situation just now, and it can be seen that the method we proposed has obvious advantages in the number of resources from 600 to 1000. Especially when the number of resources is relatively short compared to the number of access users, our decision-making method can see a rapid increase in the access success rate, which shows that the method has excellent robustness in the face of sudden large access.
# A Semi-Supervised Lie Detection Algorithm Based on Integrating Multiple Speech Emotional Features
## 3 Experimental Section
### Comparative Experiments  
To further verify the effectiveness of the proposed algorithm, two sets of comparative experiments were designed in this chapter. The specific settings are as follows:  
1. SS-AE: Utilizes a semi-supervised auto encoder [22] network for speech lie detection. This network extracts latent representations of unlabeled data using an encoder and then reconstructs features using a decoder to compute the reconstruction loss between the original and reconstructed data. This loss is utilized as an unsupervised loss to optimize the model's generalization ability on unlabeled data.
2. Mean-teacher: Based on the Mean-teacher model [12; 23], which utilizes a combination of the reconstruction loss from the autoencoder network and the consistency regularization loss from the Mean-teacher model to optimize the decision boundary, enhancing the classification ability of the semi-supervised model.  
From Table 2, it can be observed that the proposed algorithm achieved accuracies of 63.70%, 63.30%, and 65.74% for label counts of 200, 600, and 1200, respectively. These accuracies are significantly higher than those of the first comparative experiment SS-AE and the second comparative experiment Mean-teacher. These research findings indicate that the proposed method can learn the structure and features of unlabeled data and achieve better results.  
In addition, we quantified the computational efficiency of the proposed algorithm. When the number of labels was set to 200, the total runtime of the model was 87.14 s, with an inference time of 6.22 s before the first iteration began. When the number of labels was set to 600, the total runtime was 204.33 s, and the inference time was 6.13 s. With 1200 labels, the total runtime increased to 402.52 s, with an inference time of 6.06 s. All experimental data are averages, and the results indicate that as the number of labels increases, the total training time of the model also increases, while the inference time shows no significant change.