esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Internet of Things (IoT) is a revolutionary advancement that automates daily tasks by interacting between digital and physical realms through a network of mostly Low-Power IoT (LP-IoT) devices. For an IoT ecosystem, reliable wireless connectivity is essential to ensure the optimal operation of LP-IoT devices, especially considering their limited resource capacity. This reliability is often achieved through channel estimation, an essential aspect for optimising signal transmission. Considering the importance of reliable channel estimation for constrained IoT devices, we developed two lightweight yet effective channel estimation models based on Random Forest Regressor (RFR). These two models are namely classified as Feature-based RFR(F) and Sequence-based RFR(S) methods and utilise Received Signal Strength Indicator (RSSI) as a fundamental channel metric to enhance efficiency for the reliability of channel estimation in constrained LP-IoT devices. The models' performance was assessed by comparing them with the state-of-the-art and our previously developed Artificial Neural Network (ANN)-based method. The experimental results show that the RFR(F) method shows approximately 39.62% improvement in Mean Squared Error (MSE) over the Feature-based ANN(F) model and 37.86% advancement over the state-of-the-art. Similarly, the RFR(S) model shows an improvement in MSE of 24.9% compared to the Sequence-based ANN(S) model and an 80.59% improvement compared to the leading existing methods. We also evaluated the lightweight characteristics of our RFR(F) and RFR(S) methods by deploying them on Raspberry Pi 4 Model B to demonstrate their practicality for LP-IoT devices.  
Keywords:wireless channel estimation; random forest regressor; RSSI; low-power IoT; wireless communication +
Footnote †: journal: Applied sciences
## 6 Conclusions
In addition, uncertainties, such as those related to fluctuating energy demand, unpredictable weather conditions, and degradation of the ESS, pose a considerable obstacle to the practical application of optimization techniques. These uncertainties could lead to suboptimal decisions if not adequately considered in the microgrid's planning and operational phases. This review highlighted the advantage of developing methodologies that integrate the sizing and energy management stages and address them simultaneously, which could provide more accurate solutions. Effective integration of the sizing and energy management stages, with the use of advanced methodologies and emerging technologies, will significantly improve the efficiency and reliability of microgrids. Integrating technologies such as electric vehicles and smart grids into the planning and operation of microgrids is a promising avenue for energy efficiency and sustainability. Future research could focus on improving the robustness of optimization algorithms to handle uncertainties better, ensuring that real-time control systems can respond dynamically to changing conditions without compromising the stability or efficiency of the microgrid. Research in this area could provide opportunities for microgrid planning and energy management optimization. Also, upcoming works could address multi-objective optimization, including cost minimization, CO2 emission reduction, and autonomy. Advanced multi-objective energy management techniques could significantly improve energy planning. Conceptualization, R.R. and B.C. ; funding acquisition, F.L. and M.S. ; methodology, B.C., F.L. and M.S. ; resources, F.L. and M.S. ; supervision, B.C., F.L. and M.S. ; writing--original draft, F.A.K. ; writing--review & editing, R.R. and B.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This work has been achieved within the framework of EE4.0 project (Energie Electrique 4.0). EE4.0 is co-financed by the French State and the French Region of Hauts-de-France. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not Applicable. **Conflicts of Interest:** The authors declare no conflicts of interest.
# A Scalable Solution to Detect Behavior Changes of Elderly People Living Alone
## 3 Method and Datasets
### Filtering False Alerts  
In real life, there are situations when quite normal activities (e.g., cleaning the house, spending more time in the kitchen to cook a special dinner, receiving visitors, etc.) are reflected by the sensors as deviations from the long term activity patterns. To prevent the system from generating false alerts in these situations, we have used a method to estimate the probability of the alert, based on fuzzy logic, starting from the values of \(AL\) and \(AD\), computed with (1) and (2). Since false alerts are usually associated with higher activity levels, while health issues are reflected by a decrease of the activity level, we have considered a set of rules of the following type:  
_IF (the activity level (\(AL\)) is HIGH) AND (the activity deviation (\(AD\)) is HIGH), THEN (the probability of a false alert (PFA) is HIGH)._  
The entire rule-base is shown in Table 1.  
Figure 7: The proposed method to detect deviations from the previously recorded activity patterns: (**a**) Choose a reference interval (e.g., 7–12 days) with normal activity and create activity maps for each one hour time slice; (**b**) Plot the centroid points of the activity maps for each time slice across the reference interval; (**c**) Find the smallest circle that bounds the centroid points for the reference interval. The area delimited by this circle defines normal or typical activity patterns; (**d**) Check if the centroid of the current activity maps falls inside the normal activity area.  
We have used the Sugeno fuzzy inference method, described in Ref. [46], with three fuzzy domains: LOW (L), MEDIUM (M), and HIGH (H), and triangular membership functions, as shown in Figure 8.  
For each rule in Table 1, the truth value of the antecedent is:  
\[Z_{i}=\min(\mu_{AL},\mu_{AD}) \tag{6}\]  
Since PFA is a probability, the output domain is the interval [0,1], so we can choose the constant values, called singletons (\(Si\)), for the output fuzzy domains in this range, e.g., \(\text{S}_{\text{LOW}}=0\), \(\text{S}_{\text{MEDIUM}}=0.5\), \(\text{S}_{\text{HIGH}}=1\).  
With these notations, the crisp value of the PFA is computed with (7):  
\[\text{PFA}=\frac{\sum_{j=1}^{9}Zi*Si}{\sum_{j=1}^{9}Zi} \tag{7}\]  
The value of PFA computed this way is compared with an empirically determined threshold and the alert is only reported if the PFA is lower than the threshold. To minimize the number of false alerts, the value of this threshold should be tunable for each participant. The process of tuning starts by choosing a relatively high value for the threshold (e.g., 70%), then iteratively decrement this value until the number of false alerts drops to a value close to zero.
# Improving Lossless Image Compression with Contextual Memory
## 4 The Proposed Method-Contextual Memory
### Description of the Contextual Prediction
#### 4.2.3 Updating the Model  
In order to pass mixing information to the weak learners, we propose a dual objective minimization function (as depicted in Figure 5):  
* In respect to the output of the network-global error
* In respect to the output of the individual nodes (side predictions)-local error  
Like PAQ8, we use reinforcement learning. Since we do not know the true value of the probability that a bit is 0 or 1 in a given context, we cannot use supervised learning. We backpropagate the binary outcome in the network and try to minimize the cumulative logistic loss in an online manner. The square loss can be also used, but we are trying to minimize the wasted coding space.  
For minimizing the logistic loss, the formula we used for the global error is  
\[E_{g}=\beta_{g}(p-y) \tag{10}\]  
If we wanted to minimize the square loss, the formula would be  
\[E_{g}=\beta_{g}(p-y)*p*(1-p) \tag{11}\]  
Figure 5: Block scheme for the proposed update algorithm.  
with \(E_{g}\) as the global error, \(\beta_{g}\) the global error learning rate, \(p\) is the output probability of the entire network, and \(y\) the binary ground truth.  
The local error is computed for each _memory value_ in a similar fashion to the global error.  
\[E_{l}=\ \beta_{l}*(p_{i}-y),\ \ p_{i=}\sigma(k_{p}*v_{i}) \tag{12}\]  
with \(E_{l}\) as the local error, \(\beta_{l}\) the local error learning rate, \(p_{i}\) is the output probability for the i-th context (side prediction) multiplied by an ad-hoc value \(k_{v}\), computed as the sigmoid of the _memory value_\(v_{i}\), and \(y\) is the binary ground truth.  
All the \"memory values\" are then updated by subtracting the local and global errors:  
\[v_{i}=v_{i}-E_{l}-E_{g} \tag{13}\]  
Instead of updating weights of the mixture, we update directly the values that contribute to the average. We have no layer to separate the context weights from the input probabilities, making the method different from the context mixing algorithm.
## 5 Conclusions  
The conclusions of this study are as follows:  
1. Among the 27 preprocessing combinations used in this study, the SG-1 DR method effectively extracts data features and reduces noise interference. The heatmaps for the four mathematical transformations--1 DR, 2 DR, (LogR)', and (1/R)'--exhibit rich correlation variations and relatively uniform color distributions. Data based on these four transformations effectively present the correlation characteristics between spectral band reflectance and SOM content. Through modeling comparisons, it was found that the first derivative transformation after SG denoising yielded the best results, demonstrating the highest feature expression capability for spectral data.
2. In the preprocessing and modeling comparison experiments, it was found that deep learning methods have a superior ability to extract spectral data features compared to machine learning methods.
3. After combining CNN with SVM, the evaluation metrics improved compared to using CNN alone. Specifically, RMSE decreased by 0.423, R\({}^{2}\) increased by 3.1%, MAE decreased by 0.229, MARE differed by only 0.001, and RPIQ decreased by 1.244. Thisindicates that for predicting SOM, it is feasible to use CNN for feature extraction followed by SVM for classification and regression.
* The CNN-SVM network improved by the Black-winged Kite Algorithm showed significant enhancements compared to the original CNN-SVM network: RMSE decreased by 1.599, R\({}^{2}\) increased by 9.2%, MAE decreased by 1.487, MARE decreased by 0.078, MBE decreased by 0.482, and RPIQ decreased by 2.316. This makes it the best among the five compared networks. Therefore, the BKA-CNN-SVM model is highly effective in predicting the SOM content of soil in Guangxi forest farms.  
Conceptualization, L.X. and Y.D.; methodology, L.X.; software, Y.S.; validation, Y.D., L.X. and Y.S.; formal analysis, Y.D.; investigation, Y.S.; resources, L.X.; data curation, Y.D.; writing--original draft preparation, L.X.; writing--review and editing, L.X.; visualization, Y.D.; supervision, Y.D.; project administration, Y.D.; funding acquisition, Y.D. and Y.S. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Guangxi Natural Internal Medicine Foundation project (GuikeAB24010338), the central government guides local science and technology development fund projects (GuikeZY22096012), and the National Natural Science Foundation of China (32360374).  
Not applicable.  
The datasets presented in this article are not readily available because they come from state-owned forests and are restricted by privacy agreements. If you have relevant needs, you can contact the author to obtain access. Requests to access the datasets should be directed to [EMAIL_ADDRESS].  
The authors declare no conflicts of interest.
# Tensor Implementation of Monte-Carlo Tree Search for Model-Based Reinforcement Learning
## 4 Experiments
### Model of Environment  
We chose MuZero algorithm as an example of model-based RL method that utilize MCTS. Our experimental setup was inspired by the domain of Atari games. One observation (state of the environment) was represented by \(128\times 96\times 96\) tensor. The number of possible actions was set to 18, which is the maximum amount of possible actions in domainof Atari games. MuZero algorithm uses DNN to approximate representation, dynamics and prediction function. We use original MuZero's architecture [2] with a few modifications.  
The kernel size is \(3\times 3\) for all operations. The convolution operations padding is set to 1. Representation function is identical copy of MuZero's original function. It consists of:  
* 1 convolution with stride 2 and 128 kernels, output resolution \(48\times 48\)
* 2 residual blocks with 128 kernels
* 1 convolution with stride 2 and 256 kernels, output resolution \(24\times 24\)
* 3 residual blocks with 256 kernels
* average pooling with stride 2, output resolution \(12\times 12\)
* 3 residual blocks with 256 kernels
* average pooling with stride 2, output resolution \(6\times 6\)  
Output size of representation function, i.e., state size is \(256\times 6\times 6\).  
The input to the dynamics function is \(257\times 6\times 6\) dimensional tensor consisted of state and action. Action represents tensor \(1\times 6\times 6\) filled by value \(\frac{a_{i}}{count\,of\,actions}\). The dynamics function provides two outputs. The first is the new state and the second is the reward. The reward in original MuZero implementation was computed as a linear combination of categorical output. We formulated reward prediction problem as a regression task instead. The second change from the original implementation is in the structure of hidden layers of reward head as they were not described in the documentation.  
Layers of dynamics functions are:  
* 1 convolution with 256 kernels, output resolution \(6\times 6\)
* 8 residual blocks with 256 kernels, output resolution \(6\times 6\) (the last residual block is also used as a representation of the new state)
* flattening, number of output neurons is 9216
* fully connected layer with 512 neurons
* fully connected layer with 1 neuron (reward output)  
Outputs of representation and dynamics functions are used as inputs for prediction function. It also provides two outputs--probability distribution and state value. The common part is composed of flattening and linear layer with 512 neurons. Each output head consists of one linear layer with 512 neurons. Last linear layer of probability distribution has 18 neurons. As in the case of the reward head, last output layer of the state value head has one neuron. Again, the categorical task has been reformulated into a regression task.  
Our goal was not to train MuZero agent but to compare the computational speed of individual MCTS implementations. Therefore, we used a randomly initialized DNN model to simulate all necessary computations.
# Deriving a Dilution of Precision Indicator for GNSS Factor Graph Optimization Solutions +
## 2 Materials and Methods
### Factor Graph Model  
In this study, a simple factor graph (Figure 1) is considered for one _scalar_ state \(x_{j}\) at different epochs indexed by \(k\in[1,K]\). \(x_{j,k}\) refers to one of the scalar state at epoch \(k\), such as the position along one axis or the receiver clock bias.  
We assume that the snapshot LS algorithm provides a solution for each of the \(K\) epochs of a trajectory. From each snapshot solution, the snapshot estimation accuracy \(\sigma_{j,k}^{2}=(\mathbf{P}_{k}^{(snap)})_{j,j}\) is used as a factor to constrain the scalar state \(x_{j}\) at epoch \(k\) and is referred to as _snapshot GNSS factor_.  
\[y_{j,k}=x_{j,k}+n_{j,k},\quad n_{j,k}\sim\mathcal{N}(0,\sigma_{j,k}^{2}) \tag{5}\]  
An additional factor is introduced to model the probabilistic constraint linking the scalar states at two consecutive epochs. It assumes that the evolution of the scalar state between 2 epochs is known, with an uncertainty assumed to be normally-distributed with a standard deviation \(q\). This constraint is referred to as a _motion factor_ in this document. This generic motion factor uses the following affine model:  
\[m_{k,k+1}=x_{j,k+1}-(x_{j,k}+b_{k+1})+n_{m_{k,k+1}},\quad n_{m_{k,k+1}}\sim \mathcal{N}(0,q^{2}) \tag{6}\]  
where \(n_{m_{k,k+1}}\) accounts for the uncertainty of the motion constraint, assumed to be a centered normal random variable with a constant standard deviation \(q\) and \(b_{k+1}\) is a known parameter, e.g., an integrated velocity coming from another sensor.  
Examples of motion constraints are the estimation of the receiver displacement by GNSS Doppler or Time Difference of Carrier Phase measurements [6], estimation of the receiver displacement by an external sensor such as an inertial measurement unit or a visual simultaneous localization and mapping solution [2], or simply a dynamic model, such as a random walk or constant velocity model with Gaussian uncertainty [3].  
Finally, it is assumed that the errors of the snapshot GNSS factors and those of the motion factors are independent between each other and across epochs. Therefore, when stacking all the factors in a single vector \(\mathbf{y}\), we obtain the following estimation problem:  
Figure 1: Factor graph considered in this study.  
\[\mathbf{y}=\left[\begin{array}{c}y_{j,1}\\ y_{j,2}\\ \vdots\\ y_{j,K}\\ m_{1,2}\\ m_{2,3}\\ \vdots\\ m_{K-1,K}\end{array}\right]=\left[\begin{array}{ccccc}1&0&0&\ldots&0\\ 0&1&0&\ldots&0\\ 0&0&1&\ddots&\vdots\\ \vdots&\vdots&\ddots&\ddots&0\\ 0&0&\ldots&0&1\\ -1&1&0&\ldots&0\\ 0&-1&1&\ddots&\vdots\\ \vdots&\ddots&\ddots&\ddots&0\\ 0&\ldots&0&-1&1\end{array}\right]\left[\begin{array}{c}x_{j,1}\\ \vdots\\ x_{j,2}\\ \vdots\\ x_{j,k}\\ \vdots\\ x_{j,k}\\ \vdots\\ x_{j,k}\\ \vdots\\ x_{j,k}\\ \vdots\\ m_{K-1,K}\end{array}\right]+\left[\begin{array}{c}n_{j,1}\\ \vdots\\ n_{j,K}\\ n_{m,2}\\ \vdots\\ n_{m,3}\\ \vdots\\ m_{K-1,K}\end{array}\right] \tag{7}\]
# Effects of Auditory Pre-Stimulation on Cognitive Task Performance in a Noisy Environment
###### Abstract  
The accident rate due to human errors in industrial fields has been consistently high over the past few decades, and noise has been emerging as one of the main causes of human errors. In recent years, auditory pre-stimulation has been considered as a means of preventing human errors by improving workers' cognitive task performance. However, most previous studies demonstrated the effectiveness of the auditory pre-stimulation in a quiet environment. Accordingly, studies on the effects of pre-stimulation in a noisy environment are still lacking. Therefore, this study aimed to empirically investigate: (1) the effects of noisy environments on the performances of cognitive tasks related to different functions of working memory and (2) the effects of auditory pre-stimulation on the performances of cognitive tasks in a field-noise environment. To accomplish these research objectives, two major experiments were conducted. In the first experiment, a total of 24 participants performed each of three basic short-term/working memory (STM/WM) tasks under two different experimental conditions (quiet-noise environment and field-noise environment) depending on the presence or absence of field noise. In the second experiment, the participants performed each of the three basic STM/WM tasks in a field-noise environment after they were provided with one of four different auditory pre-stimulations (quiet noise, white noise, field noise, and mixed (white and field) noise). The three STM/WM tasks were the Corsi block-tapping, Digit span, and 3-back tasks, corresponding to the visuospatial sketchpad, the phonological loop, and the central executive of WM, respectively. The major findings were that: (1) the field-noise environment did not affect the scores of the Corsi block-tapping and 3-back tasks, significantly affecting only the Digit span task score (decreased by 15.2%, _p_! 0.01); and (2) the Digit span task performance in the field-noise environment was improved by 17.9% (_p_! 0.05) when mixed noise was provided as a type of auditory pre-stimulation. These findings may be useful for the work-space designs that prevent/minimize human errors and industrial accidents by improving the cognitive task performance of workers in field-noise environments.  
Keywords:human error; cognitive task performance; auditory pre-stimulation; working memory +
Footnote †: journal: Applied Sciences
# Optimization of DC Energy Storage in Tokamak Poloidal Coils
## 8 Simulation Campaign
### Optimization of Input Power  
Another relevant aspect of the study was the analysis of the effect of the peak power absorption from the grid, which is useful for the design of the input charger and distribution grid. The results highlighted the following:  
* Increasing the peak power limit from 1 MW to 2 MW resulted in a substantial reduction in the required energy for DC storage. Considering the reference SC modules and cabinets, this increase could save 18 reference cabinets.
* In comparison, the increase from 2 MW to 3 MW has less impact, for instance, only six reference cabinets.  
This suggests that a peak power limit of 2 MW offers the best compromise between efficiency and practical feasibility. Details can be found in Tables 3 and 5.  
Moreover, the average power drawn from the grid does not coincide with the fixed limit. For example, Figure 16 shows the instantaneous power drawn by the PS pair PF1-PF2 with \(P_{\text{gridmax}}\) = 2 MW per coil, clarifying that the assumed peak power is not constantly required, resulting in an average power around 2.2 MW (half of the allowed peak value). This could lead to further optimization in the distribution of the tokamak loads.  
As a result, the DC storage of the whole DTT PF PS system could consist of 14 + 42 + 24 = 80 cabinets occupying about 58 m\({}^{2}\) with an input power in the order of 10 MW (6 coils with a 2 MW peak). Alternatively, it could consist of 12 + 32 + 20 = 64 cabinets occupying about 46 m\({}^{2}\) with an input power in the order of 15 MW (3 MW peak). These results are rather impressive, considering that the installed power can exceed 500 MW.
## 4 Experiments
### Comparison of Image Segmentation Effectiveness
#### 4.2.1 Effectiveness of the Improved OTSU Dynamic Threshold Calculation Strategy  
Segmentation was performed on 21,520 images with fixed threshold issues, with representative results shown below.  
The original algorithm's fixed threshold approach resulted in incomplete segmentation (Figure 8(b)) or excessive segmentation (Figure 8(a)) due to background differences. In contrast, the proposed OTSU-based method dynamically calculates the optimal thresholdbased on the grayscale distribution, minimizing global brightness differences and achieving precise segmentation (Figure 10b).  
To address shadow interference in the OTSU algorithm, which results in over-segmentation by disregarding shadow regions (Figures 11b and 12a), we introduced a shadow detection mechanism. By resetting the segmentation points to the image edges upon shadow detection, the proposed method achieved optimal segmentation results (Figure 12b), effectively mitigating shadow interference.