esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
## 3 Analysis of Equalization Methods  
As mentioned in the last section, the main restriction in the transmission data rate in the integrated VLC system was caused by the limited bandwidth. To extend the bandwidth, a single equalization method was not enough, so various equalization methods were combined to corporately contribute to the signal transmission. In the proposed VLC system, both passive equalizer and NN-based FFE algorithms were adopted at the transmitter, while an RBFNN-enabled equalization algorithm was utilized in the receiver.
## 2 Methodology
### Evaluation Metrics
#### 2.5.2 Viewing of Snow Classification Coverage Percentage  
In addition to the total viewed snow coverage, the second metric explores the efficacy of each sensor configuration to view a specific snow classification as:  
\[P_{j}=\frac{\bigcup_{i=1}^{n}(A(\vec{x},t)_{viewed,i}\times W_{i,j})\cap A_{ snow}(\vec{x},t)\cap A_{j}(\vec{x},t)}{\int A_{j}(\vec{x},t)d\vec{x}}\times 100\%, \tag{3}\]  
where \(A_{j}(\vec{x},t)\) refers the area of snow class \(j\), and \(P_{j}\) is the percentage of the viewed snow-covered area of the \(j\)-th class. \(W_{i,j}\) represents the weight of the efficacy of a given sensor \(i\) on the snow class \(j\). The snow classification system proposed by Reference [55] is employed here (see Figure 1). Snow is categorized into six different classes based on the physical properties and includes: (1)undra, (2) taiga, (3) alpine, (4) maritime, (5) ephemeral, and (6) prairie classes; ice is not discussed in this paper. Table 3 shows the assigned weight matrix \(W_{i,j}\) used in this study. It provides a first-order estimate of the sensor efficacy on specific snow classification according to the assumptions as follows: (1) PMW sensors do not work well for snow under dense forest (taiga), deep snow (maritime), and snow over complex terrain (alpine) [12,17]; (2) SAR sensors do not work effectively for snow under dense forest (taiga); and (3) LiDAR sensors are affected by cloud attenuation [40]. The situation in the real world is complex; hence, the weight applied here is somewhat subjective over large areas for each class. However, these values are useful in specifying a reasonable estimation of each sensor's efficacy and allow for a relatively transparent understanding of the assumptions made.  
Figure 1: Map of snow cover classification based on Reference [55].
## 3 Improved Multiobjective Particle Swarm Optimization Algorithm
### Algorithm Improvements
#### 3.2.2 The Search Strategy Based on Crossover and Mutation  
The genetic algorithm optimizes the objective by simulating the law of survival of the fittest in the biological evolution in nature [35]. In the genetic algorithm, two individuals are randomly selected from the population as parents, crossover operation is performed with a higher probability, mutation operation is performed with a lower probability for each individual, then the better individual is selected to enter the next generation, and the optimal solution is found through such iterative process [36]. Inspired by the above method, this paper proposes a search strategy based on Simulated Binary Crossover (SBX) and Polynomial Mutation (PM).  
In each iteration, in order to make better use of the dimensional information of \(p_{best}\), the external archive is used to store the generated \(p_{best}\), and then the \(p_{best}\) in the external archive is applied to generate new particles through the SBX criterion. The new particles merge all dimensional information of \(p_{best}\) generated by each iteration, which enhances the communication of all dimensional information among particles and effectively improves the ability of particles to jump out of the local optimal solution.  
If a population stops evolving (i.e., the velocity of all particles in the population is almost equal to zero), the global optimal solution will not be obtained. In this article, polynomial mutation is used to generate a new global optimal particle (\(g_{best}\)), which makes the current population jump out of the local optimal state. Different from the usual mutation strategy (i.e., one particle mutates with a certain probability), the strategy we propose is as follows: if the \(g_{best}\) of the particle has not been improved for several consecutive generations, the newly generated \(g_{best}\) is mutated by polynomial.  
SBX operator allows elite solutions to exchange useful gene segments, while PM operation adds a small turbulence to search local region. In order to execute SBX and PM operators on the external archive \(A\), an elite subset \(E\) is firstly selected from \(A\), which contains a number of non-dominated solutions with bigger crowding distance in \(A\). Generally, the size of \(E\) is smaller than that of \(A\), which is set as half of \(\mid A\mid\) in this paper. For each solution \(A_{i}\) (\(i=1,\ 2,\ \cdots,\ \mid A\mid\)), a random integer \(j\) is generated in \([1,\ |E|]\). Then, \(A_{i}\) and \(E_{j}\) are used as the parent solutions for performing SBX operator. A child solution is randomly selected from SBX operator, and then the PM operator is proceeded to perform.
# A New Spatial Distance Metric for Verification of Precipitation
## 2 Materials and Methods
### Other Distance Measures  
The behavior of the new method was compared to the behavior of four other distance measures. Since we focus more on their behavior, as opposed to how they function, only a very short summary of the measures is provided here. More detailed descriptions are available in [12] and the publications we referenced below. The Centroid Distance (CDST, [4]) is a straightforward measure defined as the distance between the centers of mass of the two input fields. It can be used with binary as well as non-binary input fields. If any of the two fields contain only zeros, the CDST value is undefined.  
The Baddeley's \(\Delta\) (BDEL; [5]) and Mean-error distance (MED; [5,6]) both rely on distance maps and can only be used with binary input fields. A distance map is a field that gives the distance to the nearest non-zero point in a particular field. The distance maps are calculated separately for each input field. There are multiple options on how BDEL can be defined (e.g., [12]). Here BDEL was defined as the absolute value of the difference of the two distance fields averaged over the whole domain. MED is also based on distance maps and we used the averaged variant of the MED defined as \(1/2(MED(a,b)+MED(b,a))\) (e.g., [12]). \(MED(a,b)\) represents the distance to the nearest non-zero point in \(f_{\text{a}}\) averaged over all the non-zero points in field \(f_{\text{b}}\). \(MED(b,a)\) is defined identically to \(MED(a,b)\) but with \(f_{\text{a}}\) and \(f_{\text{b}}\) switched. If any of the input fields contain only zeros, the distance map is undefined--thus also BDEL and MED will be undefined.  
The Fractions Skill Score displacement (dFSS; [3]) is based on the Fractions Skill Score (FSS; [7,8]). Of the four metrics, the dFSS is most similar to PSD. It provides an estimate of displacement by determining the spatial scale at which the FSS value exceeds the value of 0.5. Like BDEL and MED, it can only be used with binary fields and is not defined if any of the two input fields contain only zeros. It has an additional limitation--if the bias is large (the sum of values on one field differs considerably from the sum in the other field), the dFSS value is undefined.
## 3 Memory Management in MIROS
### Performance Evaluation
With this approach, the memory fragments can be assembled when needed. Consequently, the allocation failure resulting from the memory insufficiency problem can be eased. As for the MIROS proactive SF approach, its drawback is high de-allocation overhead, but its advantage is fast allocation time and defragmentation support. Therefore, it is suitable to be used for the real-time WSN applications in which the nodes are constrained in the memory resources (e.g., the MicaZ) but sufficient in the energy resources. Nevertheless, the design and implementation of a new allocation mechanism that is fast in allocation time, low in allocation overhead, yet high in memory utilization efficiency still requires further study.
# A Hybrid Ontology-Based Recommendation System in e-Commerce
## 3 The Hybrid Ontology-based Approach
### The ontology model
#### 3.2.1 The domain and scope of the ontology model  
The main purpose of creating and using an ontology in the recommendation process is to implement a data model that can describe and represent all the concepts that concern to the active user, his neighbours and the products purchased by the latter, which are the ones that can be recommended to the active user. Therefore, one of the main applications of this ontology will be e-commerce platforms.  
In order to define the domain and scope of the ontology model, we create a list of questions that help find if the ontology model contains enough information to describe the domain of the recommendation process. The list of the defined question is divided into two categories, namely the questions that concern the active user and the respective neighbours and also the questions that concern the products purchased by the neighbours.  
The questions made to the active user and his neighbours are the following:  
* Who is the active user?
* Which products and respective categories has the active user purchased?
* What was the average amount spent by the active user as well as the maximum and minimum amount?
* Who are the neighbours of the active user?
* What is the relationship between the active user and each one of the neighbours?
* Who are the neighbours that have made purchases in different product categories as those of the active user?  
The second part of the questions, which concern to the products purchased by the neighbours are the following:  
* Can the product match the active user's preferences?
* What is the rating given by active users's the respective neighbour?
* Which are the five most common words used in all the revisions of the product?
# Smart Healthcare Applications over 5G Networks: A Systematic Review
## 3 Results and Discussion
### RQ3: What Are the Security Mechanisms or Protocols Required by Smart Health Systems Based on 5G?
#### 3.3.2 Encryption  
Note that 18.18% of the studies were based on encryption as a security protocol and focused on the use of encryption algorithms. That is, algorithms were used to encrypt the sending of information. In fact, in an implemented laparoscopic surgery system, [PERSON] et al. mentioned that the security was provided by a VPN (virtual private network) encryption system [38]. However, the authors did not define the mechanism. In [52; 59; 44] the authors briefly mentioned that their systems use data encryption, without detailing the methodologies or protocols implemented.  
In the same way, [PERSON] et al. [50] proposed an intelligent system with neural networks that stores information in a distributed database (blockchain), which uses an encryption mechanism to protect against cyber attacks. It consists of a blocks chain that contains information, in which each block is connected to the next in a mathematical way, and if an attempt is made to modify the information of any block, the chain is broken; the rest of the chain and the information contained remain unchanged. It allows digital information to be stored in a secure and fragmented manner, which allows providers to generate safer and more complete patient history storage; that is, the information is protected in such a way that it cannot be modified or deleted.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Role of Body Sensors in the loMT
#### 3.1.1 Heart Rate Monitors  
Heart rate monitors [17] can measure the heart's activity by detecting electrical sensors generated during each heartbeat. These sensors are commonly integrated into wearable devices such as smart watches and fitness trackers. They can be used to monitor heart rate variability, which provides insights into the autonomic nervous system's function and can be indicative of cardiovascular health. Heart rate monitors are valuable tools for detecting abnormalities such as arrhythmias, which can indicate underlying heart conditions such as atrial fibrillation or heart failure that signal the need for urgent medical attention. Additionally, wrist-worn heart rate monitors can enhance the rehabilitation efforts of stroke patients by monitoring exercise intensity during physical therapy sessions. The Fitbit Charge 5 Fitness and Heart Rate Tracker, Wahoo Tickr X Heart Rate Monitor Chest Strap, Garmin Forerunner 945 GPS Running Smartwatch with Heart Rate Monitor, and Apple smart watches are among the commercially available devices for heart rate monitoring.
# Wireless Energy Harvesting with Cooperative Relaying under the Best Relay Selection Scheme
## 4 Performance Analysis
### Outage Probability (OP)  
It is assumed that there are no performed transmissions below the predefined SNR threshold. The link quality is measured based on the outage probability. It is a rational measure of performance, where it is associated with the challenging slow fading channel variations [47]. The OP can be written as:  
\[P_{out}=Pr\left(\max\left(\gamma_{sd},\gamma_{brs}\right)<\Gamma^{th}\right), \tag{16}\]Assuming i.i.d. Rayleigh fading channel, (16) can be written as:  
\[P_{out}=Pr\left(\gamma_{sd}<\Gamma^{th}\right)\cdot Pr\left(\gamma_{brs}<\Gamma^{ th}\right)=P_{1}\times P_{2}, \tag{17}\]  
where the first probability:  
\[P_{1} = Pr\left(\gamma_{sd}<\Gamma^{th}\right)=Pr\left(\bar{\gamma}Y< \Gamma^{th}\right)=Pr\left(Y<\frac{\Gamma^{th}}{\bar{\gamma}}\right), \tag{18}\]  
where \(Y=\left|h_{sd}\right|^{2}d_{3}^{-n}\). According to the exponential distribution, the cumulative distribution function (CDF) can be written as:  
\[P_{1}=1-e^{-\frac{\Gamma^{th}}{\bar{\gamma}}}, \tag{19}\]  
The second probability can be calculated as:  
\[P_{2}=Pr\left(\gamma_{brs}<\Gamma^{th}\right)=Pr\left(\frac{aX^{2}Z}{\bar{p}bXZ +bXZ+cX+\bar{p}+1}<\Gamma^{th}\right), \tag{20}\]  
where \(X=\left|h_{si}\right|^{2}d_{1}^{-n}\), \(Z=\left|h_{id}\right|^{2}d_{2}^{-n}\), \(a=\eta\rho(1-\rho)\bar{\gamma}^{2}\), \(b=\eta\rho\bar{\gamma}\), \(c=(1-\rho)\bar{\gamma}\), \(\bar{\rho}=1-\rho\). The remaining derivation of the second probability is presented in the Appendix A, and the final expression of the outage probability can be written as:  
\[P_{out} = \left(1-e^{-\frac{\Gamma^{th}}{\bar{\gamma}}}\right)\times\left[1 -2e^{-g}\sum_{k=0}^{N-1}\frac{\xi^{\frac{1+k}{2}}}{k!}K_{k-1}\left(2\sqrt{\xi }\right)\right], \tag{21}\]  
where \(g=\frac{(2-\rho)\Gamma^{th}}{(1-\rho)\bar{\gamma}}\), \(\xi=\frac{(c+\bar{p}+1)\Gamma^{th}}{a}\), \(K_{\
u}(\cdot)\) is the \(\
u\)-th order modified Bessel function of the second kind.
## 3 Model Results and Discussion
### Hyperparameter Optimization
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Statistics**} & \multicolumn{8}{c}{**Folds**} \\ \cline{3-10}  & & **1** & **2** & **3** & **4** & **5** & **STD** & **Average** & **COV\%** \\ \hline DT & & 0.9711 & 0.9255 & 0.9677 & 0.9706 & 0.9538 & 0.0173 & 0.9577 & 1.8064 \\ BME & & 0.9626 & 0.9719 & 0.9761 & 0.9489 & 0.9554 & 0.0101 & 0.9630 & 1.0488 \\ FRT & \(R^{2}\) & 0.9577 & 0.9691 & 0.9719 & 0.9576 & 0.9563 & 0.0066 & 0.9625 & 0.6857 \\ AB & & 0.9673 & 0.9562 & 0.9812 & 0.9707 & 0.9667 & 0.0081 & 0.9684 & 0.8364 \\ GTB & & 0.9650 & 0.9662 & 0.9891 & 0.9722 & 0.9759 & 0.0087 & 0.9737 & 0.8935 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cross-validation measurement results. Figure 6: Validation curve for the maximum depth of the trees: (**a**) DT, (**b**) AB and (**c**) GTB. A validation curve for the number of estimators was calculated to check the performance of the models (except for the DT since it only has one estimator). The validation curve for the \(R^{2}\) as a function of the number of estimators for the chosen hyperparameters is represented in Figure 7. As it can be observed, the \(R^{2}\) quickly reaches a plateau, and the results stop significantly improving beyond a critical number of trees. The larger the better, but also the longer it will take to compute. This way, the used values of \(n\_estimators\) were deemed as satisfactory. It can also be observed from the plot that the models are not overfitting, since the training validation scores are both high. A learning curve shows the validation and training score of an estimator for several numbers of training samples. It is used to find how much the model benefits from adding more training data and whether the estimator suffers more from a variance or a bias error. The learning curves for all the models with the respective hyperparameters chosen are represented in Figure 8. The function _learning_curve_ from the module _sklearn_model_selection_ was used. Overall, for small amounts of data, the training score is much greater than the validation score. Adding more training samples increases generalization. Figure 7: Validation curve for the number of estimators: (**a**) BME, (**b**) FRT, (**c**) AB and (**d**) CTB.
# An Estimation of Speech Privacy Class Based on ISO Parameter
## 5 Conclusions  
The authors' recent experience in acoustical consulting has highlighted the need for a numerical indicator to express the speech privacy level, reflecting the acoustic quality of apartments in multidwelling buildings. The _SPC_ parameter, as defined by the ASTM E2638 standard using an Anglo-Saxon approach, was identified as a valuable indicator for assessing speech privacy in buildings. While there is limited information about its widespread use, perhaps due to its original focus on meeting rooms, it appears to be a useful indicator for residential spaces as well. The results presented in this paper demonstrate that the same approach can be applied using ISO parameters. _SPC_ can be estimated with acceptable accuracy based on available data measured in buildings according to ISO standards, such as the single value indicator of sound insulation (sound reduction index \(R^{{}^{\prime}}\!w\)) and the equivalent noise level (\(L_{AA}\)) in dB(A). These data are commonly provided by noise and building acoustics measurement software and are widely used. This extends the application of the principles formalized by standards for assessing the state of privacy in dwellings according to European practice. In this paper, it was shown through 43 in situ measurements of the sound reduction index and ambient noise that the correlation between the _SPC_ parameter and the proposed _SPI_ parameter is very good. This indicates that the _SPI_ parameter can serve as an assessment of the state of speech privacy in residential or commercial spaces. The value of _SPI_ can be predicted in the building design stage, as well as after construction, using data routinely obtained during the building's final testing. It has been demonstrated that there is also a strong correlation between the _SPI_ and the Articulation Index (_AI_), which is commonly used in the Western Hemisphere to quantify the state of speech privacy in open-plan offices.  
Conceptualization, M.M.; methodology, M.B. and D.S.P.; software, M.B.; validation, T.M. and M.B.; formal analysis, T.M. and D.S.P.; investigation, M.B.; resources, M.M.; data curation, M.B. and T.M.; writing--original draft preparation, M.B., M.M., and T.M.; writing--review and editing, M.B.; visualization, M.B.; supervision, D.S.P.; project administration, T.M.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia under contract numbers 451-03-47/2023-01/200125.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 3 The Causes of Thermal Hazards Associated with Lithium-Ion Batteries
### Electrical Factor  
External short circuit, overcharge and over-discharge are common conditions of electrical failure. Where, the external short circuit of battery occurs once electrodes with a voltage difference are connected by conductors. It usually results from the deformation of battery, water immersion, conductor aging, improper usage and long-time charging, etc. During an external short circuit, the battery is in a state of fast discharging and the discharging current may be much larger than that of normal condition. Hereafter, the battery undergoes a violent temperature rise which may lead to serious consequence.  
As the open circuit voltage of battery is charged above the cut-off voltage, overcharge occurs. The failure of battery management system is the ordinary reason of overcharge so that the charging of battery will proceed continuously. As a result, the internal pressure of battery increases, the deformation of battery and leakage of electrolytes occur, and the performance of battery also significantly decreases. Besides, severe heat and gas generation can also be seen during overcharge process. Compared to normal charging process, the heat generation behavior during overcharge will be much greater as a result of the side reactions inside battery and the increased internal resistance. In addition, excessive loss of lithium ions at the cathode during overcharging will lead to structural collapse of the cathode and subsequent oxygen release. The released oxygen accelerates the decomposition of electrolyte, and thereafter, gases are generated. Consequently, the thermal hazard associated with an overcharged battery is greatly heightened compared to that of a normal battery due to the excessive energy stored in the overcharged battery.  
Similarly, when the open circuit voltage of battery is discharged below the cut-off voltage, over-discharge occurs and the failure of battery management system is also a typical reason for over-discharge. Over-discharge causes an excessive loss of lithium ions on the anode which will destroy the stable structure of the anode and cause irreversible damage. Meanwhile, gases such as CO and CO\({}_{2}\) can also be generated, resulting in battery swelling. Furthermore, over-discharge will cause the dissolution of copper collector. The dissolved copper migrates and deposits onto the surface of the anode, which will pierce the separator, causing a short circuit to form. Consistent with the other abusive conditions, substantial heat release also occurs upon over-discharge of the LIB.
## 4 Experiment and Result Analysis
### Data Analysis and Evaluation Metrics  
In order to verify the prediction performance of the proposed method, the root mean square error (_RMSE_) and mean absolute error (_MAE_) are used as the evaluation indexes:  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{(y_{i}-\hat{y}_{i})^{2}}}} \tag{11}\]  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}{|y_{i}-\hat{y}_{i}|}} \tag{12}\]  
where \(y_{i}\) and \(\hat{y}_{i}\) represent the actual and predicted traffic flow, respectively; \(n\) is the number of traffic zones.  
Several prediction models are used for comparison, HA, ARIMA, LSTM, ConvLSTM, and STDN (Spatiotemporal Dynamic Network). These methods can be divided into three categories: (1) traditional statistics-based time series prediction model (HA, ARIMA); (2) time series prediction model based on deep learning (LSTM); (3) deep learning network for traffic flow prediction based on spatiotemporal feature mining (ConvLSTM, STDN). The details of these comparison methods are described below.