esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Processing and Uncertainty Analysis  
The heat transfer coefficient (\(h\)) was calculated as:  
\[h=\frac{q^{\prime\prime}}{\Delta T_{sat}}=\frac{q^{\prime\prime}}{T_{w}-T_{sat}} \tag{1}\]  
where \(q^{\prime\prime}\) is the heat flux, \(\Delta T_{sat}\) is the superheat, \(T_{w}\) is the temperature of the heat sink, and \(T_{sat}\) is the saturation temperature of liquid nitrogen.  
On the basis of the law of conservation of energy, the heat flux was calculated using the expression:  
\[q^{\prime\prime}=\frac{(q_{in}-q_{loss})}{S}=\frac{(UI-q_{loss})}{S} \tag{2}\]  
where \(q_{in}\) is the input heat flow, \(q_{loss}\) is the heat loss of the experiment, \(S\) is the area of the heat sink surface, \(U,~{}I\) are the output voltage and current of the simulated heat source, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & & & & & & \\ \cline{3-10} \multicolumn{1}{c}{**G (kg\(\cdot\)h\({}^{-1}\))**} & \multicolumn{5}{c}{**10.3**} & \multicolumn{5}{c}{**15.5**} & \multicolumn{5}{c}{**20.7**} & \multicolumn{5}{c}{**25.9**} & \multicolumn{5}{c}{**34.5**} \\ \hline  & 12 & 16.00 & 34.01 & 18.56 & 32.18 & 14.76 & 34.36 & 13.51 & 32.11 &  
The heat loss was calculated as:  
\[q_{loss}=q_{side}+q_{bottom}=\sum A\frac{\lambda_{0}}{\delta}\Delta T \tag{3}\]  
where \(q_{side}\) and \(q_{bottom}\) are the heat loss of the side wall and bottom of the simulated heat source, respectively, \(A\) is the area of the heat loss, \(\lambda_{0}\) is the thermal conductivity of PTFE, which is 0.27 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\), \(\delta\) is the thickness of the side/bottom wall of the simulated heat source, \(\Delta T\) is the temperature difference between the simulated heat source and the spray chamber.  
In the experiment, on the basis of the thickness of the PTFE base (23 mm on the side and 44 mm at the bottom) and the temperature difference between the heat source simulation device and environment, the maximum system heat loss was calculated to be 0.204%.  
The heat flux and heat transfer coefficient in the experimental results were determined from the calculated parameters. The accuracy of the T-type thermocouple was \(\pm\)0.5 K, and the position accuracy of the thermocouple was determined from the processing technology to be \(\pm\)0.1 mm. Furthermore, the uncertainty in the spray distance was \(\pm\)0.1 mm, the accuracy of the current and voltage was 0.2%, and the accuracy of the flowmeter was \(\pm\)0.2%. The uncertainty was calculated as:  
\[R_{N}=\left\{\sum_{i=1}^{N}\biggl{(}\frac{\delta N}{\delta X_{i}}R_{i}\biggr{)} ^{2}\right\}^{\frac{1}{2}} \tag{4}\]  
In the experiment, the uncertainties in the heat flux and comprehensive heat transfer coefficient were found to be \(\pm\)4.48% and \(\pm\)5.75%, respectively.
First, we give an example to prove that global parameter sharing may lead to extremely bad local optimality in the physically heterogeneous MARL problem without properly handling the action semantic conflict, which provides the necessity and motivation of our research. Second, we introduce the Generalized Action Space (GAS), which is the union set of all agent actions with different action semantics. Heterogeneous agents can be distinguished through different available action masks (AMs). All agents first calculate their unified representation in the GAS, and then generate their heterogeneous action policies using different AMs. Third, in order to further improve the training of GAS parameters and the cooperation between heterogeneous groups, we introduce a Cross-Group Prediction (CGP) loss to predict other groups' action policies with their own group's trajectory information. Finally, we combine the GAPO with a policy-based algorithm (MAPPO, [32]) and a value-based algorithm (QMIX, [31]) as implementing examples to prove the effectiveness and universality of our GAPO method. G-MAPPO and G-QMIX successfully leverage the advantages of parameter sharing while also avoiding the action semantic conflict and reaching a high winning rate (WR). In this paper, we conduct experiments mainly in the Starcraft Multi-Agent Challenges (SMAC) environment [34], which is a popular yet powerful research environment. Results in the SMAC environment prove the effectiveness of the two proposed algorithms compared with several state-of-the-art MARL methods. Several validating tests are conducted in the Multi-agent Particle Environments (MPE) [35; 36; 37; 38; 39], the Multi-Agent MuJoCo (MAMuJoCo) environment [40], and the Resource Preemption Environment (RPE) [41] to study the effectiveness of our algorithm in other types of heterogeneous environments. The main contributions of our paper are as follows:  
* To the best of our knowledge, we are the first to point out the impact of action semantic conflict under the global parameter-sharing constraint and propose a theoretical analysis of the impact. * We propose the GAPO method to solve the action semantic conflict and propose two practical algorithms, G-MAPPO and G-QMIX. * We conduct comparison and ablation experiments in the SMAC environment and conduct validating tests in the MPE, MAMuJoCo, and RPE environments to prove the effectiveness of the GAPO method and G-MAPPO and G-QMIX algorithms. The rest of the content is as follows. We summarize some related works in Section 2; we give the definitions and the example of the action semantic conflict under a bad parameter-sharing structure in Section 3; we provide details about the GAPO methods and the G-MAPPO and G-QMIX algorithms in Section 4; we present detailed environmental design and discuss results of our experiments in Section 5; and finally, we draw some conclusions in Section 6.
# Efficient and Secured Mechanisms for Data Link in IoT WSNs: A Literature Review
## 13 Conclusions  
WSNs and the IoT have been rapidly and enormously evolving. These technologies have revolutionized many aspects of life and can be observed throughout the world, independently or in collaboration. A WSN is a wireless sensor cluster that communicates and functions together. Industrial connectivity technologies have already been advancing for many years to help meet evolving needs in traditional application scenarios, such as factory automation and distributed process control systems, or to cope with continuous demands for improved efficiency. The Fourth Industrial Revolution and the IIoT plainly illustrate that this evolutionary shift must be accelerated and expanded to encompass growing research disciplines and severe technical problems, since neither new demanding communication needs nor creative applications can be fulfilled while solely depending on the assistance provided by today's communication technologies. This article covered all the aspects of WSN from its architecture and security requirements to its applications and implementations as well as the issues and challenges it faces. Such information can help to clarify a research perspective on WSN in IoT systems. Because little work has been undertaken on the data link layer and its relationship to WSN infrastructure, and due to existing major data link layer issues, its use has not been adequately supported or considered, resulting in a lack of innovative and efficient solutions within WSN applications. There is a significant research gap in the development of solutions to data link layer issues, including QoS, security, and flow control, which requires the immediate attention of researchers to develop modern, effective, and efficient solutions that support the issues, challenges, and limitations of the data link layer protocol. Current active security algorithms; QoS and flow control improvement techniques; and efficient implementation and deployment schemes for the domain of the data link layer in WSN frameworks represent future goals. Future work should address the problems of WSN area coverage and develop efficient solutions to existing WSN issues and challenges. Broadening the implementation and application of WSN in the IR4.0 and IIoT is a significant goal required to classify the issues and challenges facing the IR4.0 and provide effective solutions.
# Achieving Sender Anonymity in Tor against the Global Passive Adversary
## 5 Introducing Fault Tolerance into the System
We assume \(p\) is the same for each node. Therefore, the probability that, given a layer of \(j\) nodes, at least one node of the layer is alive is \(p^{\prime}=1-(1-p)^{j}\). To guarantee reliability (i.e., the communication is not lost), at least one node per level (for the \(k\) levels) has to be alive. Therefore, the probability that the communication succeeds is \(p^{\prime\prime}=(1-(1-p)^{j})^{k}\). Clearly, it decreases as \(k\) increases and increases as \(j\) increases. Suppose now we set the reliability threshold to a given value \(\tau\). Then, \(j\) must set in such a way that \(j>\frac{log(1-e^{\frac{log(j)}{k}})}{log(1-p)}\). In Figure 8, we set \(\tau=0.999\) and show how the ratio \(\frac{j}{k}\) varies for different values of \(p\) and \(k\). Observe that the exemplified value chosen for \(\tau\) refers to a very reliable system. Indeed, according to the standard IEC 61508, this value falls into the range of probability of failure on demand (PFD), classifying the system as reliability class SIL 3, which is the second most-reliable class. As expected, for high values of \(p\), the number of nodes \(j\) (and then the ratio \(\frac{j}{k}\)) required to obtain \(\tau=0.999\) decreases. Regarding \(k\), as \(k\) increases the absolute value of \(j\) increases but slower that \(k\). Therefore, the ratio \(\frac{j}{k}\) increases with \(k\). To give a practical example, with \(k=100\) and \(p=0.9\), we obtain a ratio \(\frac{j}{k}=0.05\), which means that each layer of the ring has to contain only five nodes.
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 2 Preprocessing Chain for SoNaR Signals
### Data Preprocessing Stages  
In the preprocessing flow diagram presented in Figure 2, data discretization involves converting continuous data into discrete data, which is essential for machine learning classification. Data transformation consists of converting data from one format or structure to an alternative format that is more suitable for analysis, storage, or processing. This process includes normalization, scaling, coding, filling in missing data, and identifying features from the original data. Data cleaning aims to correct or remove incorrect, corrupted, incorrectly formatted, or duplicate data from a dataset. If the data are noisy, then the models and results may be unreliable. Data cleaning techniques were employed depending on the dataset and noise characteristics.  
The signal-processing chain is shown in Figure 2, which follows [23] with some adaptions to certain characteristics of our dataset. A pipeline without decimation was implemented in this work, which attempted to retain more signals and aggregate frequency information. The signal was captured by a hydrophone with intrinsic noise from the environment and equipment. Signals could be recorded in either mono or 2-channel stereo. The average of the channels was computed for the stereo signals. In the first pipeline, the signals were decimated by a factor of four, which reduced the time resolution. The signals were then converted to a lower sampling rate while preserving their main characteristics. In the second pipeline, the signals were not decimated. This approach was used to evaluate the impact of decimation on a smaller resolution in the spectrum. The acoustic signature was recorded in a 22 kHz audio format and then reduced to 5.5 kHz or recorded at 16 kHz and reduced to 4 kHz by decimation.
# Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection
## 3 Materials and Methods
### Theory
#### 3.1.2 Otsu  
The OTSU algorithm, also known as the Nobuyuki OTSU method, is considered the best algorithm for threshold selection in image segmentation. Because OTSU is simple to compute and is independent of image brightness and contrast, it has been widely used in digital image processing [28; 29; 30; 31; 32]. It divides the image into two parts, background and foreground, according to the grey-scale characteristics of the image. The larger the interclass variance between the background and foreground, the greater the difference between the two parts that make up the image. When part of the foreground is divided into the background or part of the background is divided into the foreground, both result in a smaller difference between the two parts [33]. Therefore, the segmentation that maximizes the interclass variance means the smallest probability of misclassification [34]. Let the grey scale image grey level be L, then the grey scale range is [0,L-1]. The optimal threshold for calculating the image using the OTSU algorithm is shown in Equation (4).  
\[t=Max[w_{0}(t)*(u_{0}(t)-u)^{2}+w_{1}(t)*(u_{1}(t)-u)^{2}] \tag{4}\]  
The variables are stated as follow: when the threshold for segmentation is t, \(w_{0}\) is the background scale, \(u_{0}\) is the background mean, \(w_{1}\) is the foreground scale, \(u_{1}\) is the foreground mean and \(u\) is the mean of the whole image. The t that maximizes the value of the above equation is the optimal threshold for segmenting the image.  
Where the mean value u of the whole image is calculated, as shown in Equation (5), and can be simplified to Equation (6) when Equation (4) is more computationally intensive:  
\[u=w_{0}(t)*u_{0}(t)+w_{1}(t)*u_{1}(t) \tag{5}\]  
\[t=Max[w_{0}(t)*w_{1}(t)*(u_{0}(t)-u_{1}(t))^{2}] \tag{6}\]
## 2 System Model
### Antenna Model
#### 2.2.1 Element Pattern  
By denoting \(\theta\) and \(\phi\) as the zenith and azimuth angles in the local coordinate system centered at the center of the antenna array, respectively, and defined as \(0^{\circ}<\theta<180^{\circ}\) and \(-180^{\circ}<\phi<180^{\circ}\), we can express the radiation pattern \(A_{E}\) of an antenna element as  
\[A_{E}(\theta,\phi)=-\text{min}[A_{E,V}(\theta)+A_{E,H}(\phi),\ A_{m}] \tag{4}\]  
where \(A_{E,V}\) and \(A_{E,H}\) are the vertical and horizontal radiation patterns of the array element, expressed as  
\[A_{E,V}(\theta)=-\text{min}\bigg{[}12\bigg{(}\frac{\theta-90^{\circ}}{\theta_ {3\text{dB}}}\bigg{)}^{2},SLA_{V}\bigg{]} \tag{5}\]  
\[A_{E,H}(\phi)=-\text{min}\bigg{[}12\bigg{(}\frac{\phi}{\phi_{3\text{dB}}} \bigg{)}^{2},A_{m}\bigg{]} \tag{6}\]  
where, \(\theta\) and \(\phi\) are the zenith and azimuth angles respectively in the local coordinate system centered at the center of the antenna array and defined as \(0^{\circ}<\theta<180^{\circ}\) and \(-180^{\circ}<\phi<180^{\circ}\). \(\theta_{3\text{dB}}=\phi_{3\text{dB}}=65^{\circ}\) are the 3 dB vertical and horizontal beamwidths of the antenna, \(SLA_{V}=SLA_{H}=30\) dB are the vertical and horizontal sidelobe attenuation levels, respectively, and \(A_{m}=30\) dB is the antenna's front-to-back ratio.
# Computer Vision Measurement of Pointer Meter Readings Based on Inverse Perspective Mapping
## 3 Experiments and Analysis
### Measure Two Pointer Meters
Therefore, the pixel coordinates of the critical point (including the rotation center and the endpoint of the pointer) of the meter on the left in the image can be corrected in this way, identifying the readings of the meter on the left in the image by Equation (4). Through the translation distance D, the reading of the pointer meter on the right in the image can also be detected, as shown in Figure 8b. During the experiment, the positions of the two pointer meters were changed from zero scale line to maximum scale line, and there were 8 times of measurement in each group. Under keeping that the pointer location same with the operators observing, and reading of the pointer meter is measured by the present method and the conventional method respectively. The readings of the pointer meter were compared with the reference value to calculate absolute errors, as shown in Table 8. Here, the \(g_{0}{}^{(1)}\) and \(g_{0}{}^{(2)}\) were the reference value of the meter on the left and the meter on the right in the image; \(g_{1}{}^{(1)},g_{1}{}^{(2)}\) respectively represented the results of readings of two pointer meters measured by conventional methods; \(g_{2}{}^{(1)},g_{2}{}^{(2)}\) respectively represented the results of readings of the two pointer meters measured by proposed methods. As shown in Table 9, the absolute errors \(\Delta g_{2}{}^{(1)},\Delta g_{2}{}^{(2)}\) of the measurement by proposed method were mostly between 1 kPa and 16 kPa. The maximum error of 16 kPa still did not exceed the absolute errors \(\Delta g_{1}{}^{(1)},\Delta g_{1}{}^{(2)}\) of measurement by the traditional method. Comparing with the reference value, the measured values had little change. It can be seen from the experimental results that the identification accuracy of the pointer meter calibration system is within the allowable range. Compared with the conventional measurement method, the maximum relative error is 0.064%, which meets the measurement requirements. Figure 8: (**a**) Translation distance between two pointer meters; (**b**) result of critical points of the meter on the right in the image. From the results of the experiment, there are still errors in the presented method of recognizing the readings of pointer meter. The main reason is uneven distribution of the scale lines in the pointer meter. The meter scales on the pointer meter are used as the calibration objects to obtain the extrinsic parameters of the meter plane. The error of the extrinsic parameters calibration is caused by the incomplete equal distance between the mid-points of two adjacent scales. In addition, the changes in light intensity and the distance between the camera and the meter plane would vary the quality of the image, which will also results in deviations in the extraction of pixel coordinates of t critical points in the image.
# Bi-LSTM-Augmented Deep Neural Network for Multi-Gbps VCSEL-Based Visible Light Communication Link
###### Abstract  
With the remarkable advances in vertical-cavity surface-emitting lasers (VCSELs) in recent decades, VCSELs have been considered promising light sources in the field of optical wireless communications. However, off-the-shelf VCSELs still have a limited modulation bandwidth to meet the multi-Gb/s data rate requirements imposed on the next-generation wireless communication system. Recently, employing machine learning (ML) techniques as a method to tackle such issues has been intriguing for researchers in wireless communication. In this work, through a systematic analysis, it is shown that the ML technique is also very effective in VCSEL-based visible light communication. Using a commercial VCSEL and bidirectional long short-term memory (Bi-LSTM)-based ML scheme, a high-speed visible light communication (VLC) link with a data rate of 13.5 Gbps is demonstrated, which is the fastest single channel result from a cost-effective, off-the-shelf VCSEL device, to the best of the authors' knowledge.  
visible light communication (VLC); machine learning (ML); optical wireless communication (OWC); long short-term memory (LSTM) +
Footnote †: journal:  
00 April 2022  
Accepted: 25 May 2022  
Published: 30 May 2022
# An Improved COVID-19 Forecasting by Infectious Disease Modelling Using Machine Learning
## 3 Materials and Methods
### Deep Learning Models
#### 3.3.2 Bidirectional LSTM  
The extended version of LSTM is Bi-directional LSTM (Bi-LSTM), which saves both directional state (forward and backward) information using two LSTMs models. Forward direction LSTM takes the input in the forwarding direction, and the other in a backward direction, which eventually collects the contextual information within the network and forecast more effectively. To resolve the shortcomings of the LSTM model, which was able to keep the information of the previous step but unable to keep the information of future one. The bidirectional recurrent neural networks have been proposed, containing two LSTM layers for both forward and backward with same output. In Bi-LSTM architecture, for an input sequence, the forward and backward direction weighted vector are calculated, and the final sequence is made by combining both directional vectors as the final output. Commonly accepted RNNs advance inputs in a singular course while disregarding the data acquired ahead of real time. This problem is solved by applying the bidirectional topology of LSTM. The Bi-LSTM removes the entire temporal information taking into account information before and after real-time. Moreover, hidden neurons of the standard RNN are divided into the states of forwarding and backward. This refers to when neurons of the forwarding states are not joined to the backward states and vice versa. Withoutthe backward states, the architecture is similar to the commonly accepted unidirectional RNN. Furthermore, in this framework, more time delays are not necessary as taken in the standard RNN. The same setting was used for Bi-LSTM, as described for the LSTM model.
# Enhanced Speech Emotion Recognition Using Conditional-DCGAN-Based Data Augmentation
## 4 Experiments  
### Experimental Design  
In this study, we evaluated the performance improvement of the emotion speech recognition model using both the original Emo-DB dataset and the Mel-spectrogram data generated by the proposed method. The experimental data consisted of Mel-spectrograms extracted from the original Emo-DB, with an additional 300 Mel-spectrograms for each emotional category. The data was split into training and test data in a ratio of 85:15, considering the balance of each dataset, and a 5-fold cross-validation method was applied to evaluate the generalization performance of the model. This method divides the training data into five equal parts and uses each part as a test set sequentially. For performance evaluation, a combined CNN and Bi-LSTM emotion speech recognition model was used. The Cosine Annealing Warm Restarts scheduler [41] was applied for model training, and a mini-batch approach was employed. The Cosine Annealing Warm Restarts scheduler dynamically adjusts the learning rate by gradually decreasing it and then resetting to the maximum learning rate at the end of a cycle. This approach improves the efficiency of the learning process. The optimizer used was AdamW [42], which applies weight decay during parameter updates. AdamW helps prevent overfitting and improves generalization performance by handling weight decay more effectively when the learning rate changes.  
Figure 3: Generated Mel-spectrograms.  
The initial learning rate was set to 0.001, and the first cycle was set to 15 epochs, with the cycle length doubling after each restart.  
For comparative analysis in the experiments, the results using only the original Emo-DB data were compared with those obtained by combining the original data with the augmented data generated by the proposed method. Additionally, the results were compared with previous studies that used similar methods. The performance metrics used were WA (Weighted Accuracy) and UAR (Unweighted Average Recall), considering class imbalance. WA is calculated by weighting the accuracy of each class by the number of samples in that class, while UAR is the simple average of the recall rates across all classes. These metrics provide a comprehensive evaluation of each model's performance.  
\[WA=\frac{\sum_{i=1}^{N}w_{i}\;*\;ACC_{i}}{\sum_{i=1}^{N}w_{i}} \tag{4}\]  
\[UAR=\frac{\sum_{i=1}^{N}w_{i}\;*\;Recall_{i}}{N} \tag{5}\]  
The formulas for calculating WA and UAR are given in Equations (4) and (5). Here, \(w_{i}\) represents the number of samples in class \(i\), \(ACC_{i}\) represents the accuracy of class \(i\), \(N\) is the number of classes, and \(Recall_{i}\) represents the recall of class \(i\).