esa-sceva/satcom-chunk-collection huggingface dataset:
# Solar Irradiance Forecasting with Transformer Model
## 3 Results  
Figure 12 shows the solar irradiance prediction for 7 days ahead. The orange points represent the predicted solar irradiance values by the model, and the green points depict the measured values from the dataset. As can be seen in the graph, the model is, on the whole, accurate in predicting the future. It was also found that it is not advisable to make longer predictions than the specified window size of the model.  
Figure 11: Significance of the hyperparameters and their correlation with the best validation mean squared error metric.  
Figure 12: _Cont._The results show that the smallest average solar irradiance prediction error is about 131.49326 Wh/m\({}^{2}\)/day, which is a good result when the average daily solar irradiance is 5.3070946 kWh/m\({}^{2}\)/day (the error is less than 2.5% of the mean solar irradiance). The MAPE error results are presented in Table 1.  
As shown in Table 1, the model proposed in this paper achieved a smaller worst-case error than those of the competing models. It must be emphasized that for the proposed model, Table 1 shows the result for the region, where the solar irradiance proved to be the most difficult to predict. It is the densely populated Chennai Metropolitan Area located on the east coast of India with a tropical savanna climate where the temperature reaches 45 \({}^{\circ}\)C in extremes. When comparing results across the various areas predicted by the proposed model, Bitta Solar Power Plant, located in a sparsely populated area in western India off the Arabian Sea coast, proved the easiest to predict with a MAPE error of only 1.86%.
# A Minimum Mean-Square-Error (MMSE) Decoder for Quasi-Orthogonal Space-Time Block Code
## 3 Proposed Algorithm
### Reducing the Computational Complexity  
To obtain the diagonal matrix from the detection matrix, \(\mathbf{K}\), the proposed algorithm is derived using four elementary matrices. By pre-multiplying \(\mathbf{K}\) by \(\mathbf{E}_{1}\) to eliminate \(k_{31}\), where \(k_{ik}\) means the \((i,k)\) entry of \(\mathbf{K}\), a matrix is obtained as  
\[\mathbf{E}_{1}\mathbf{K}=\begin{bmatrix}\kappa&0&\beta&0\\ 0&\kappa&0&\beta\\ 0&0&\kappa-\beta\theta&0\\ 0&\beta&0&\kappa\end{bmatrix}, \tag{13}\]  
where  
\[\mathbf{E}_{1}=\begin{bmatrix}1&0&0&0\\ 0&1&0&0\\ -\theta&0&1&0\\ 0&0&0&1\end{bmatrix},\ \ \theta=\frac{\beta}{\kappa}.\]  
By using the above procedure, i.e., by pre-multiplying (13) by \(\mathbf{E}_{2}\), \(\mathbf{E}_{3}\), and \(\mathbf{E}_{4}\) to remove \(k_{42},k_{13}\), and \(k_{24}\), respectively, the diagonal matrix is finally given as  
\[\delta\mathbf{I}=\mathbf{E}_{4}\mathbf{E}_{3}\mathbf{E}_{2}\mathbf{E}_{1} \mathbf{K}=\mathbf{E}\mathbf{K} \tag{14}\]where \(\delta=\kappa-\frac{\beta^{2}}{\kappa}\),  
\[\mathbf{E}_{4} = \begin{bmatrix}1&0&0&0\\ 0&1-\theta^{2}&0&-\theta\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}, \tag{15}\] \[\mathbf{E}_{3} = \begin{bmatrix}1-\theta^{2}&0&-\theta&0\\ 0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix},\] (16) \[\mathbf{E}_{2} = \begin{bmatrix}1&0&0&0\\ 0&1&0&0\\ 0&0&1&0\\ 0&-\theta&0&1\end{bmatrix},\] (17) \[\mathbf{E} = \mathbf{E}_{4}\mathbf{E}_{3}\mathbf{E}_{2}\mathbf{E}_{1}=\begin{bmatrix} 1&0&-\theta&0\\ 0&1&0&-\theta\\ -\theta&0&1&0\\ 0&-\theta&0&1\end{bmatrix}. \tag{18}\]  
Note that \(\mathbf{E}\) is similar to \(\mathbf{K}^{-1}\), but it is easily obtained by using \(\theta\), i.e., the proposed algorithm does not require any matrix inversion but can be calculated using \(\theta\) and (18).  
Since the proposed algorithm is derived not from a transformation of the channel matrix but from a change of the detection matrix, the encoding matrix is expressed as [4]  
\[\mathbf{X}=\begin{bmatrix}x_{1}&x_{2}&x_{3}&x_{4}\\ -x_{2}^{*}&x_{1}^{*}&-x_{4}^{*}&x_{3}\\ x_{3}&x_{4}&x_{1}&x_{2}\\ -x_{4}^{*}&x_{3}^{*}&-x_{2}^{*}&x_{1}^{*}\end{bmatrix}. \tag{19}\]  
From (14), the estimate of \(\mathbf{x}\) is obtained as  
\[\mathbf{\hat{x}}=\mathbf{E}\mathbf{H}^{H}\mathbf{r}=\mathbf{E}\mathbf{H}^{H} \mathbf{H}\mathbf{x}+\mathbf{E}\mathbf{H}^{H}\mathbf{w}. \tag{20}\]  
To sum up, the MMSE decoder is similar to the proposed algorithm. The main difference between the two algorithms is whether the noise variance is known or not. In other words, the MMSE decoder is not available in practical applications, because the noise variance is unknown [11]. On the other hand, the proposed algorithm can be used in real applications, because the noise variance is estimated in (12). In addition, the computational complexity of the conventional algorithm is higher than that of the proposed algorithm. The reason is that while the computation load of the conventional algorithm is 64 multiplications for the detection matrix and \(O(n^{3})\) for matrix inversion, that of the proposed algorithm is 16 multiplications each time.
## 3 Results
### Effects of VH Condition, OS, and Gender on VOT, fo, and SPL
#### 3.2.3 Gender  
Gender had a highly significant influence on both _f_o and SPL, with a moderate effect size for _f_o and a small effect size for SPL (\(p\)\(\leq\) 0.001, \(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.30 and \(p\) = 0.01, \(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.05,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Overall Severity of Dysphonia** & **Men (_n_)** & **Women (_n_)** & **Total (_n_)** & **\%** \\ \hline Mild & 8 & 11 & 19 & 61.29 \\ Moderate & 1 & 7 & 8 & 25.81 \\ Severe & 1 & 3 & 4 & 12.90 \\ Total & 10 & 21 & 31 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Distribution of overall severity of dysphonia by gender.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Gender** & **_n_** & **Mean** & **SD** & **Range** & **Minimum** & **Maximum** \\ \hline Men & VOT & 40 & 19.39 & 8.18 & 37 & 0 & 37 \\  & _f_o & 40 & 134.74 & 26.07 & 103.0 & 78.0 & 180.0 \\  & SPL & 40 & 55.81 & 5.17 & 20.6 & 45.03 & 65.63 \\  & CV\_f_o & 40 & 1.24 & 0.26 & 1.30 & 0.53 & 1.83 \\ Women & VOT & 83 & 18.62 & 7.93 & 36 & 0 & 36 \\  & _f_o & 83 & 185.07 & 39.32 & 200.0 & 80 & 281 \\  & SPL & 84 & 58.51 & 4.32 & 19.84 & 48.36 & 68.20 \\  & CV\_f_o & 83 & 1.28 &.342 & 1.80 &.22 & 2.02 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Descriptive results for VOT (ms), _f_o (Hz), SPL (dB(A)), and CV_f_o (%), split by gender.  
respectively). SPL and _f_o were greater for the women group than for the men group. No gender effects were found for VOT and CV_f_o. All results are described in Table 6.
## 4 Pre-Evaluation Based on the Spatio-Temporal Model
### Accessible Path Analysis Between Origin and Destination
#### 4.3.1 Direct Path Analysis  
The key to direct path analysis is to obtain the line passing through the origin and destination stops and obtain all the stopping lines that can be reached by walking at both stops (including themselves). If there are lines passing through both the origin and destination, the ODs are considered directly reachable. According to the line model, direct path information is obtained, including stop-to-stop distance, walking distance, travel time, line, and direction.
# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 3 Proposed Congestion Control Prediction Approach
### Modeling and Mathematical Formulation
#### 3.1.1 Enhanced Slow-Start  
As data transmission begins, the phase of enhanced Slow-Start (S.S.) is initiated after a long period or under anonymous conditions. During this, the SCTP starts searching the whole network to estimate the capacity of the available bandwidth. To prevent the mechanisms which exceed the available resources, ssthresh is considered as the indicator to show the available bandwidth. The phase of slow-start can be detected by a retransmit timer and occurs after the reform of packet loss. When the cwnd is less than or equal to ssthresh, the cwnd will be increased after each acknowledgment. Therefore, the cwnd value will be increased exponentially. In this phase, the sender should not exceed the permitted demands by the algorithm. The following description shows the exponential enhancement of slow-start:  
\[cwnd=cwnd+z,\]  
where \(z\) can be considered as either the path maximum transmission unit (PMTU) of the destination or previous outstanding data chunk(s) acknowledged (data ACKed). PMTU is the destination PMTU and ACKed is the total size of chunks of the previous outstanding acknowledged data [45; 46; 47].  
The standard equation of slow-start is shown as follows:  
\[\Delta cwnd\textit{ slow start}=min(\textit{Data acked},\textit{PMTU}), \tag{1}\]  
where the proposed equation of slow-start is written as follows:  
\[\Delta cwnd\textit{ slow start}=min(\textit{Data ACKed},\textit{PMTU})+A, \textit{ where }A=\frac{cwnd}{I}. \tag{2}\]The parameter I is an integer that ranges from 1 to 100, determined depending on several performance factors. These factors are: number of packets received per period, number of sent packets per time unit, congestion window, number of dropped packets, and network throughput. The selection process intends to find the factor with high effective performance that affects the congestion control mechanism. The optimal I is chosen by replacing the value of I with the proposed equation. The outcomes of the performance factors determine the best value of I. The ideal outcome is determined as follows: maximum congestion window, maximum utilization of queue size, maximum number of received and sent packets, maximum throughput, and least packet loss. When the value of I exceeds 9, the packet loss will be aggravated. The queue size is affected by this occurrence. Based on these considerations, the outcomes of the performance factor, and the experimental results, the value of I ranges from 1 to 100 in enhanced slow-start.
### Research Motivation  
Efficiency and security in routing are paramount for enhancing network performance. Ensuring the availability of optimal pathways in a vehicular network, preserving privacy, and safeguarding data integrity are central concerns that necessitate focusing on security matters. The operational effectiveness of VANETs is notably impacted by various security attacks, exacerbated by the absence of a precise defence mechanism. The lack of a centralized monitoring system, the implementation of cooperative algorithms, the dynamic nature of network topology, and the limited transmission range of nodes collectively give rise to an array of routing challenges. Consequently, a pressing need arises for secure and intelligent routing techniques to minimize communication delays and identify security threats.  
The primary objectives of our research are as follows:  
* The development of a cluster-based routing technique to facilitate the efficient delivery of content.
* The detection of malicious nodes in VANET through the application of deep learning.
* The establishment of optimal pathways to ensure the efficient delivery of data to their intended destinations.  
A principal concern in the realm of VANET data transmission pertains to secure and efficient routing. This is a non-trivial task, primarily due to the mobility of nodes within a network characterized by both homogeneity in vehicular nodes and heterogeneity in VANET contexts. Routing efficiency is frequently compromised by packet transmission delays caused by unsecured or malicious routes, often involving multiple intermediary nodes. Thus, a compelling imperative arises for developing an effective and intelligent technology that can enhance routing efficiency by minimizing the hop count while bolstering security with minimal data loss. Consequently, our proposed technique combines secure and efficient packet routing, substantially reducing communication delays. A cluster-based secure routing approach minimizes the hops required for content delivery, while a machine learning-based method is harnessed to ensure security. This innovative approach is tailored to enhance routing efficiency and reliability. The primary objective of our proposed model is to establish a secure cluster-based routing framework within VANETs specifically designed to address secure content requests and consumption. Routing security is upheld by integrating machine learning techniques and on-demand routing, implemented through a modified AODV protocol.
## 2 Materials and Methods
### Methods
#### 2.2.3 Prediction Methods
ANN is a method created based on neurons that have the characteristic of transmitting when the size of a stimulus is greater than a specific value among the components of human nerves. DNN consists of a total of three layers connected by weights: input layer, hidden layer, and output layer. DNN consists of a forward propagation process that calculates output and a back propagation [32] process that updates weights. Forward propagation receives the value of the explanatory variable through each node of the input layer, multiplies it with an appropriate weight, and delivers it to the node of the hidden layer. In the hidden layer, the received values are transformed through linear combination and an appropriate activation function (a function that determines an output value from an input value). It is then multiplied by a weight value and delivered to each node of the output layer. Each node of the output layer finally outputs the predicted value through a linear combination of the received value and an appropriate activation function. Back propagation is a process of adjusting weights by using mathematical differentiation and chain rule to output an optimal prediction value. Figure 7 is an example of the DNN with \(n\) hidden layers of three nodes.
## Materials and Methods
### Tower-Based Measurements
#### 2.1.2 Solar-Induced Fluorescence Measurements
Through the radiometric calibrations, which used a standard calibration light in a darkroom, the digital number (DN) value (with a unit of counts) collected by the spectrometer was converted into radiance (with a unit of mW/m\({}^{2}\)/nm/sr). Thus, the system automatically and continuously acquired the vegetation-reflected and corresponding solar radiance spectra with high spectral and time resolutions. The three band Fraunhofer line depth (3 FLD) method [38] was used to extract the SIF signal from the measured solar and vegetation-reflected radiances in the atmospheric absorption bands (O\({}_{2}\)-A band at approximately 760 nm). This method has been proven to be robust and accurate for SIF retrieval in the O\({}_{2}\)-A band (the RRMSE is \(\sim\)10%) when using spectral measurements via a spectrometer with a spectral resolution of \(\sim\)0.3 nm and an SNR higher than 1000 [20; 39]. The raw SIF data, which were negative or larger than 5 mW/m\({}^{2}\)/nm/sr, were discarded in the following data process and analysis. For each time period of 30 min (T\({}_{30}\)), we calculated the half-hour SIF (SIF\({}_{\rm T30}\)) by averaging all valid raw SIF measurements (SIF\({}_{0}\)) within a time range of 15 min before and after T\({}_{30}\):  
\[\text{SIF}_{\rm T30}=\sum_{T_{30}-15\text{ min}}^{T_{30}+15\text{ min}}\text{SIF}_{0}(\text{t})/N \tag{1}\]  
where \(N\) represents the number of SIF measurements for half an hour. SIF\({}_{\rm daily}\) was calculated by integrating the SIF from sunrise to sunset for one day, as shown in Equation (2). The diurnal SZA values at the two sites were calculated using a function that includes latitude (_lat_), longitude (_lon_), date, and local time of day (_t_): \(f(lat,lon,\ date,\ t). The times of sunrise (_T\({}_{sunrise}\)_) and sunset (_T\({}_{sunset}\)_) were determined according to an SZA of less than 90\({}^{\circ}\). Setting the temporal interval at 30 min (\(\Delta t_{30}\)), the summation form of SIF\({}_{\rm daily}\) can be expressed as follows:  
\[\text{SIF}_{\rm daily}=\int_{T_{sunrise}}^{T_{sunset}}\text{SIF}(\text{t})dt \approx\sum_{T_{sunrise}}^{T_{sunset}}\text{SIF}_{\rm T30}(\text{t})\times \Delta t_{30}. \tag{2}\]  
During the operating time window of AutoSIF, as listed in Table 1, on 17 and 20 June, 7 and 22 and 12-20 July, 22-31 August, and 1-5 and 7-8 September 2017, at the DM site, spectral measurements failed due to power outages. As a result, we acquired the continuous solar irradiance, vegetation-reflected radiance spectra, SIF\({}_{\rm T30}\) and SIF\({}_{\rm daily}\) for 71 and 72 days at the XTS and DM sites, respectively.
## 3 Implementation  
All computations in this section were performed using Python 3.9.19 with the NumPy 1.26.4, SciPy 1.13.1, and PyTorch 2.4 packages. To ensure experimental reproducibility, the random seed was set to 0. In the scanning method, the divergence angle was set to 70 \(\upmu\)rad.  
The FNN model converged within 200 steps, achieving a mean square error (MSE) of 0.117 \(\upmu\)rad. The accuracy of the FNN Model is illustrated in Figure 7, where \(\theta_{\text{predict}}\) denotes the predicted values and \(\theta_{\text{real}}\) represents the corresponding test set sample data. As shown, the prediction accuracy of the FNN Model exceeded 99.9%.  
The predicted errors of the FNN Model are presented in Figure 8. We can observe that most of the predicted errors in azimuth are between \(-60\)\(\upmu\)rad and \(100\)\(\upmu\)rad while the predicted errors in elevation are between \(-30\)\(\upmu\)rad and \(60\)\(\upmu\)rad. Compared with the original pointing errors, the adjusted pointing errors were reduced by an order of magnitude.  
The trajectory of the scanning step, obtained by combining the pointing error predicted by the FNN Model with an equal linear velocity spiral scanning function, is shown in Figure 9. The uncertainty cone is at 0.6 mrad while the whole pointing error is below 1 mrad. In order to show the original and adjusted scanning trajectory curves clearly, a smaller coordinate axis range is set in Figure 9.  
Figure 8: Error histogram of FNN model: (**a**) in azimuth; (**b**) in elevation.  
Figure 7: Accuracy of FNN Model.
# Machine Learning-Based Methods for Code Smell Detection: A Survey
## 2 Related Works  
Table 1 shows a comparative analysis of our study with existing literature review studies. [PERSON] et al. (2017) analyzed 238 research papers between 1990 and 2015 and discussed the detection of code smells for refactoring. [PERSON] et al. (2018) discussed which types of code smells are more extensively researched, reviewed 531 research articles between 1990 and 2017, and connected the researchers studying bad smells other than Duplicate Code using methodological aspects.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \
\begin{tabular}{c} **Sr.** \\ **No.** \\ \end{tabular} & **Author** & **Year** &
\begin{tabular}{c} **No. of** \\ **Research** \\ **Papers** \\ **Studied.** \\ \end{tabular} & **Period** & **Type** & **Considered Aspects** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{[PERSON] et al. (2019)} & \multirow{2}{*}{2011} & \multirow{2}{*}{39} & \multirow{2}{*}{2000–2009} & \multirow{2}{*}{SLR} & Code smell identification, tools, and \\  & & & & & methods to detect smells. Taxonomy of \\ \cline{1-1}  & & & & & classifying code smell. \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{[PERSON] et al. (2020)} & \multirow{2}{*}{2015} & \multirow{2}{*}{109} & \multirow{2}{*}{1999–2015} & \multirow{2}{*}{SLR} & Code smell detection, classification, \\  & & & & comparison, and evaluation & detection tools. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with existing studies.  
Figure 1: Systematic literature review steps.  
[PERSON] et al. [23] reviewed 445 papers that reported code smell identification tools, techniques, characteristics, and the impact of smells on productivity between 1999 and  
* Note: NA means ‘Not Available’.  
\end{table}
Table 1: _Cont._2016. [PERSON] et al. [13] conducted an SLR with the help of ML algorithms and emphasized commonly used methods of independent and dependent variables for code smells. [PERSON] et al. [14] performed an SLR using ML algorithms and emphasized improvement in software quality. [PERSON] et al. [15] discussed ML algorithms, visualization-based approaches, 3D techniques, and interactive ambient visualization and reviewed 83 relevant papers between 2000 and 2019. [PERSON] et al. [16] reviewed 17 articles between 2005 and 2018, studied 27 code smells, and discussed 16 ML and ensemble techniques. The earlier SLRs covered different ML algorithms; however, no class-wise classifications exist. This SLR provides a unique perspective by showing the distribution of subclasses of various ML algorithms, the number of studies in each class, and the correct proportion for each research. The researchers provided and discussed many SLRs, datasets, and performance analysis tools. However, they did not address the language used by the datasets (mentioned in Section 4.2.6).
# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
## 5 Experimental Analysis
### Performance Comparison of the Decision Logics  
Firstly, we study the performance of the multi-factor metric based Bayesian inference decision logic proposed in Section 4.2. In order to verify the validity of our scheme, we compare four types of decision logic commonly used at present, including majority vote (MV), most trusted vote (MTV), traditional Bayesian inference (TBI), and D-S theory (DST) [14]. There are two kinds of information in the experiment, namely, the trusted report and false report. The false report includes all untrustworthy reports, no matter if they are due to malicious or communication faults. We use the attack success rate (ASR) as the performance indicator, which is defined as:  
\[ASR=\frac{\#\,of\,false\,report\,non-detected}{total\,\#\,of\,false\, report} \tag{20}\]  
That is, the rate at which the cooperative verification node can detect whether the report content is false. We compare the changes in the ASR under different percentages of false reports to verify the feasibility of the five decision logics. The results are shown in Figure 4.  
The total number of reports is set to 200, and the average reputation of malicious vehicles is less than that of normal vehicles, that is, the average trust of false reports is less than that of trusted reports. The percentages of false vehicles ranges from 0% to 100%. The experiment compares the variation of the ASR for each decision logic. As can be seen from Figure 4, when the false ratio is small, all decision logics can effectively resist false reports except MTR. MTR relies only on the report given by the node with the highest reputation. Therefore, even if the average reputation of malicious vehicles is relatively lower, the vehicles with the highest reputation still have the possibility of being malicious. Although the resistance of MTR to attacks is higher, overall, the ability to resist attacks is poor. As the attacker ratio increases, the performance of DST and MV decrease rapidly because both logics are based on most observation results, while logics based on BI are more resilient to false results. As for the DBREMM, due to the additional reference of the multi-factor metric in the calculation, normal vehicles are more sensitive to the report given by malicious vehicles. Compared to TBI, our scheme has a better accuracy in the logic decision of trust.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Number of vehicles \(N\) & 200 \\ Number of malicious vehicles & [10\%, 40\%] \\ Initial reputation value \(R_{n}^{1}\) & 0.6 \\ Rounds of simulation iterations & 300 \\ Minimum step size \(\varphi_{path}\) & 0.01 \\ Ceiling of cooperative certification reward \(Total(\varphi_{path})_{max}\) & 0.2 \\ Attenuation factor \(\mu_{dec}\) & 0.3 \\ Initial \(R^{social}\) value & N\(\sim\)(0, 0.03) \\ Period of \(R^{social}\) reassessment & Every 5 iterations \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the Simulation.