esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
This value generates in simulations the same gain-to-directivity drops as in the measurements (0.3 dB average in the frequency range). Therefore, it can be considered as the most suitable value to represent the ohmic and roughness losses of the alloy material for the additive manufacturing process. Accordingly, the overall measured realized gain varies from 21 dBi to 21.7 dBi (dotted black line) while the measured directivity is from 21.6 dBi to 21.8 dBi, which means that the antenna radiation efficiency (which contains system losses) varies from 85% (39 GHz) to 97.7% (37.9 GHz). When the radiation and aperture efficiencies are both considered some authors name it as  
Figure 9:1 Simulation and measurement results of the normalized radiation pattern in polar plot in azimuth plane (\(\varnothing=0\)°) at different frequencies: (**a**) 37.5 GHz, (**b**) 38 GHz, (**c**) 38.5 GHz, and in elevation plane (\(\varnothing=90\)°) at different frequencies: (**d**) 37.5 GHz, (**e**) 38 GHz, (**f**) 38.5 GHz. Figure 9:2 Simulation and measurement results of the Total Aperture Efficiency (%), realized gain, and directivity related to the Sum (\(\Sigma\)) radiation pattern. total aperture efficiency [62] (Sections 2.8 and 14.5), the measured total antenna efficiency is higher than 60% up to 38.5 GHz, and always better than 50%. This is shown in Figure 9:3  
Therefore, based on all the above explained information, the simulation and measurement results show an excellent match, demonstrating the suitability of the proposed novel antenna concept. Table 1 presents a comparison between the results achieved by the proposed antenna and those obtained in other state-of-the-art works within the same \(k_{a}\) frequency band (27-40 GHz). The introduction of a fully metallic antenna showcases commensandable directivity, particularly when considering its compact size (4.84 \(\lambda_{0}\times\) 4.84 \(\lambda_{0}\times\) 13.9 \(\lambda_{0}\)). Notably, exceptionally high radiation efficiency has been achieved through the implementation of a fully metallic structure.
## 3 Methods
Out of Distribution (OOD): OOD refers to Identifying outliers in test data, such as data samples that do not stem from the distribution of the training data. To evaluate OOD, we utilized the Cleanlab framework [18], which leverages the \"Principle of Counting\". This principle uses the model's predicted probabilities (the confident joint) to estimate the number of examples in each class. By applying the Principle of Counting to these probabilities, we identified examples that fell outside the expected distribution, designating them as cleanlab Out of Distribution (cleanlab-OOD) instances. Margin Of Confidence (MOC): MOC is a critical metric for assessing prediction accuracy. It quantifies the margin between two prediction scores. A larger MOC signifies more accurate predictions, while smaller margins imply potentially less reliable and inaccurate predictions. To measure MOC, we employed a classification head with two outputs for gender prediction (male and female). This setup allowed us to assess the accuracy of our model's gender predictions based on the given datasets. We distinguished between MOC  
Figure 1: Cases of partially or low-quality fingerprints: (**a**) complete fingerprint, (**b**) the inner cylindrical region, (**c**) the outer cylindrical region. Hard and MOC-Easy approaches. The MOC-Hard approach involved removing the top 5% of data instances where the model demonstrated elevated prediction confidence. This strategy allowed us to focus on more challenging data for model optimization. In contrast, the MOC-Easy approach targeted potential noise in the dataset by removing the 5% of data with the lowest prediction confidence scores, enhancing our focus on informative data. FLIP (Classification Prediction Flips): FLIP measures the number of times a classification prediction changes during training. This metric assesses the robustness and generalizability of models. It provides insights into the effort required to achieve an acceptable level of accuracy with unseen data. It also helps identify points at which further optimization efforts may not yield improved performance. It also helps identify points where such efforts should be halted due to diminishing model accuracy returns. Similar to MOC, we distinguished between FLIP-Hard and FLIP-Easy approaches. The FLIP-Hard approach involved removing 5% of data with consistent prediction scores, retaining more complex data elements. On the other hand, the FLIP-Easy technique discarded 5% of the most inconsistent prediction data, ensuring a focus on data where the model exhibited persistent prediction clarity.
# Experimental Evaluation of Hybrid Fibre-Wireless System for 5G Networks
## 3 5G New Radio Hybrid MIMO-Based Fibre-Wireless System
### MIMO-Based FiWi System  
After receiving the optical signal at the photodiode, it is then converted into an electrical domain signal followed by the duplex operation and is ready for reception on wireless channels. The duplexed signal is ready to be transmitted at the wireless channels after it is received at the photodiode. Because we have two bands of signals, MIMO with a dimension of 2 \(\times\) 2 should suffice to capture the signal and assess the wireless link's performance.  
As a result, the FiWi system contains two RRHs, each with its transmitter. As a result, the RX1 and RX2 receivers receive signals \(y_{1}\) and \(y_{2}\) that are represented as  
\[\left[\begin{array}{c}y_{1}\\ y_{2}\end{array}\right]=\left[\begin{array}{cc}H_{11}&H_{12}\\ H_{21}&H_{22}\end{array}\right]\left[\begin{array}{c}x_{1}\\ x_{2}\end{array}\right]+\left[\begin{array}{c}N_{1}\\ N_{2}\end{array}\right] \tag{3}\]  
where \(x_{1}\) is the first broadcast waveform and \(x_{2}\) denotes the second, \(N_{1}\) and \(N_{2}\) denote the channel noise. With \(Tx_{i}\) to \(Rx_{j}\), \(H_{ij}\) is the channel coefficient. Intersymbol interference (ISI) and optical fibre chromatic dispersion add to the MIMO system's complexity. As a consequence, the frequency domain equalisation (FDE) technique is applied, resulting in MIMO complexity being reduced.
# Sparse Envelope Spectra for Feature Extraction of Bearing Faults Based on NMF
## 3 Results
### Numerical Experiment
In the spectrum of Figure 6c, the frequency component of 80 Hz (denoted by the arrow) is effectively extracted in the sparse envelope spectrum constructed with the bases \(\mathbf{w_{5}}\) and \(\mathbf{h_{5}}\). The envelope spectra extracted by other bases such as \(\mathbf{w_{2}}\) and \(\mathbf{w_{3}}\) do not have the certain feature components because both point to aperiodic impact. It is noted that the impulsive features are extracted, which confirms that the sparse envelope spectrum offers huge advantages for the representation of feature information. To compare the effects of the proposed method, the results of the envelope demodulation with band-pass filtering [17] are also presented in Figure 7. The feature component of 80 Hz can be observed by the frequency band filtering with basis \(\mathbf{w_{5}}\) in Figure. Compared with Figure 6c, it can be seen that no clear fault features can be observed due to the influence of in-band noise although the frequency basis \(\mathbf{w_{5}}\) corresponds to the frequency band of 2200 Hz. This illustrates that the position and bandwidth of band-pass filtering can seriously affect the envelope effect. Considering the time domain envelope of the impulse series, as shown in Figure 8, the proposed method can obtain a sparse envelope spectrum. Therefore, the frequency feature of band-pass filtering is greatly enhanced, and is eliminated for in-band noise. For comparison, Figure 9a presents the kurtogram [9] of the same signal, from which the optimal demodulation band is 960-1280 Hz and the waveform and spectrum of the filtered signal are presented in Figure 9b,c. The extracted result indicates that the filtered signal with maximum kurtosis still has noise interference and the transient feature of the waveform is not very clear. When envelope analysis  
Figure 6: Sparse envelop spectrums is obtained with decomposed sub-matrices: (**a**) Basis \(\mathbf{w_{2}}\) and weight vector \(\mathbf{h_{2}}\); (**b**) Basis \(\mathbf{w_{3}}\) and weight vector \(\mathbf{h_{3}}\); (**c**) Basis \(\mathbf{w_{5}}\) and weight vector \(\mathbf{h_{5}}\). is applied to this signal, little fault frequency can be obtained, as shown in Figure 9d. In fact, we know that kurtosis is a measure of the peakedness, so it easily tends to highlight the outliers of the signal caused by noise. Figure 8: _Cont._  
Figure 7: Envelope spectrum by band-pass filtering: (**a**) Frequency basis \(\mathbf{w}_{2}\); (**b**) Frequency basis \(\mathbf{w}_{3}\); (**c**) Frequency basis \(\mathbf{w}_{5}\).
## 4 Results and Discussion
### Major Cracks and Stress Variations
#### 4.2.3 Rationality and Correctness of the Test Method to Monitor the Evolution of the Major Cracks
Burying too many EPCs would destroy the integrity of the soil slope and make the test results unreliable. In this test, the adjacent EPCs were 30-60 cm apart in the horizontal direction. Therefore, while the stress increment analysis of the horizontal earth pressures could determine which two EPCs the crack appeared between, it could not determine the exact horizontal position of the crack. Each row of EPCs was 20-30 cm apart in height, so only the approximate depths of cracks could be determined. In addition, EPC 42 was damaged during the test and no available data were collected on it, so the situation of cracks in the fourth layer could not be analyzed. In engineered slope monitoring, it is neither economical nor necessary to embed too many sensors. For the size of the landslide model in this study, the determination of the locations and depths of the major cracks was relatively accurate.
# Accurate Performance Analysis of Coded Large-Scale Multiuser MIMO Systems with MMSE Receivers
## 6 Conclusions  
This work focuses on the uplink performance of massive multiuser MIMO networks. We proposed a novel approach to derive the distribution of the SINR for an MMSE decoder. Then, we derived the PEP, which was used to obtain the result of the upper-bound on BER for convolutionally encoded massive MMSE-MIMO systems. Finally, the results of Monte-Carlo simulations show that our analytical expressions are valid.  
[PERSON] conceived the idea, conducted theoretical derivation, simulation verification, and completed the initial drafting of this paper. [PERSON] and [PERSON] analyzed the results, provided theoretical guidance and suggestions, improved the paper, and supervised the activities. All authors approve the manuscript for publication.  
This research was funded by the National Natural Science Foundation of China (NSFC) (No. U1734209, No. U1709219, No. 61571373), the Key International Cooperation Project of Sichuan Province (No. 2017 HH0002), the Marie Curie Fellowship (No. 792406), and the NSFC China-Swedish project (No. 6161101297).  
Figure 3: Upper-bound on BER of various signaling schemes for convolutionally encoded multiuser MMSE-detected MIMO system (**a**) the number of users \(K\) is set to 5, and the number of BS receiving antennas R is set to 15, 45; (**b**) the number of users is set to 10, and the number of BS receiving antennas \(R\) is set to 100, 160.  
Figure 2: Symbol error probability (SEP) performance of 16 QAM, and 64 QAM signaling for an uncoded large-scale MIMO system equipped with a linear MMSE equalizer. The number of users \(K\) and the number of receiving antennas \(R\) at the base station are 10 and 200, respectively.
## 4 Multiple Objective Motion Optimization Problem
### Combining the Acceleration Constraints
#### 4.2.2 The One-Interval Optimization Problem  
Based on the optimization objectives and the constraints on the accelerations, we can define the one-step optimization problem as follows:  
**Problem 2**.: _Given \(VO_{t}\) the VOs at \(t\), \(X_{t}\) the MASS location at \(t\), \(T\) the target location, \(V_{t}\) the velocity of the MASS, \(\Delta_{t}\) the interval length, \(ACC_{max}\) the maximum acceleration, and \(H\) the threshold on the turning angle, the one-step optimization problem in one interval can be formulated as:_  
Figure 4: Illustration of the VO model of multiple mobile objects. \(B_{1}\) and \(B_{2}\) are two mobile objects. The shaded areas show \(VO_{B_{1}}\) and \(VO_{B_{2}}\), respectively.  
Figure 3: The red points show the discretized possible acceleration vectors at time \(t\) that satisfy the motion constraints.  
\[A_{t}^{*}=\operatorname*{arg\,min}\mathcal{V}(X_{t})=\left\{\frac{ \lambda_{0}\|XT\|}{\langle V_{t}+A_{t}XT\rangle}+\lambda_{1}\left(\arccos\frac{ \langle v_{X}+A_{t},V_{t}\rangle}{\|V_{t}+A_{t}\|\|V_{t}\|}\right)+\lambda_{2}\, \|A_{t}*\Delta_{t}\|\right\} \tag{12}\] \[s.t.\left\{\begin{array}{l}\sqrt{A_{t,X}^{2}+A_{t,Y}^{2}}\leq ACC _{\text{max}}\\ \frac{\left\|A_{t}-\frac{\langle V_{t}+A_{t}\rangle}{\|V_{t}\|V_{t}\|}V_{t} \right\|}{\|V_{t}\|}\leq H\\ (V_{t}+A_{t}\Delta_{t})\cap VO_{t}=\emptyset\end{array}\right.\]
## 4 Digitalization of MGs
### Internet of Things (IoT)
Energy providers need to ensure that IoT devices in MGs can communicate and exchange data seamlessly, regardless of their origin and technology. This requires the adoption of open standards and protocols, such as the OpenADR and IEEE 2030.5, that facilitate the integration and interoperability of IoT devices and systems. Furthermore, regulatory issues can also impede the adoption of IoT in MGs [140]. Regulations governing the energy sector may not always be compatible with the deployment of IoT-enabled MGs, which can create legal and administrative barriers. Energy providers need to work closely with policymakers and regulators to ensure that regulations support the deployment of IoT in MGs and foster innovation and competition in the energy sector. A general SWOT analysis for IoT-enabled MGs is presented in Table 7. To address these weaknesses and capitalize on the opportunities, energy providers and stakeholders must collaborate to overcome the challenges of IoT-enabled MGs. For instance, they could invest in research and development to develop standardized and interoperable IoT devices and systems, which can facilitate integration and scalability. Additionally, they can develop robust data privacy and security protocols to ensure that sensitive information collected by IoT devices is protected from cyberattacks and unauthorized access. To address the high deployment and maintenance costs, stakeholders can develop new business models and financing options to enable more affordable and accessible microgrid systems. This can include partnerships with local communities, governments, and nongovernmental organizations (NGOs) to provide financing options and incentives for microgrid deployment, particularly in remote and underserved areas. By providing access to clean and affordable energy, IoT-enabled MGs can help bridge the energy access gap and enhance the quality of life for many people around the world. In conclusion, IoT applications in MGs have the potential to transform the energy sector by improving efficiency, reliability, and sustainability. However, stakeholders must address the challenges posed by data privacy and security, interoperability, and regulatory issues to realize the full potential of IoT-enabled MGs. By collaborating and investing in research and development, stakeholders can develop standardized and interoperable IoT devices and systems, while also ensuring data privacy and security. As the world continues to face environmental, social, and economic challenges, IoT-enabled MGs can offer a viable and scalable solution to address these challenges and pave the way towards a more sustainable and equitable energy future.
## 4 Performance Validation
Table 5 and Figure 9 illustrates the detection results of the RNN-LSTM model on the detection of distinct attack types. On the detection of DoS attack, the RNN-LSTM model has reported a precision, recall, F-measure, and accuracy of 96.15%, 97.17%, 96.36%, and 96.58%. Likewise, on the prediction of R2I attack, the RNN-LSTM method has shown a precision, recall, F-measure, and accuracy of 97.19%, 97.41%, 97.30%, and 97.38%. Besides, on the examination of Probe attack, the RNN-LSTM model has exhibited a precision, recall, F-measure, and accuracy of 98.08%, 98.02%, 97.91%, and 97.89%. Additionally, on the prediction of U2r attack, the RNN-LSTM model has implied a precision, recall, F-measure, and accuracy of 97.06%, 97.43%, 97.22%, and 97%. Furthermore, on the detection of normal attack, the RNN-LSTM model has showcased a precision, recall, F-measure, and accuracy of 96.71%, 97.20%, 97.09%, and 97.13%. Moreover, the average outcome of the RNN-LSTM model has attained maximum outcome of precision, recall, F-measure, and accuracy of 97.04%, 97.45%, 97.18%, and 97.20%. Table 6 and Figure 10 provides the comparative results of the RNN-LSTM model with previous techniques [27; 28; 29]. The experimental results implied that the AK-NN method has accomplished inferior results with the minimum precision of 92.19%, recall of 93.76%, F-measure of 92.92%, and accuracy of 91.99%. Similarly, the DL model has achieved a considerable precision of 93.54%, recall of 94.87%, F-measure of 94.12%, and accuracy of 92.77%. Afterward, the DPC-DBN model has achieved a reasonable precision, recall, F-measure, and accuracy of 95.12%, 94.99%, 95.08%, and 94.98%. Similarly, the DT model has attained competing for precision, recall, F-measure, and accuracy of 96.59%, 92.84%, 95.42%, and 93.65%. At last, the proposed RNN-LSTM model has resulted in higher precision, recall, F-measure, and accuracy of 97.04%, 97.45%, 97.18%, and 97.20%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **F-Measure** & **Accuracy** \\ \hline RNN-LSTM & 97.04 & 97.45 & 97.18 & 97.20 \\ \hline Deep Learning & 93.54 & 94.87 & 94.12 & 92.77 \\ \hline DPC-DBN & 95.12 & 94.99 & 95.08 & 94.98 \\ \hline AK-NN & 92.19 & 93.76 & 92.92 & 91.99 \\ \hline Decision Tree & 96.59 & 92.84 & 95.42 & 93.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Result Analysis of Existing with Proposed RNN-LSTM Method. Figure 9: The Classifier results analysis of the RNN-LSTM model.
# Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification
## 6 Conclusions  
Enormous amounts of high-dimensional data are prevalent in numerous fields, including social media, e-commerce, bioinformatics, healthcare, transportation, and online education. The preprocessing phase of feature selection has been widely employed to reduce the dimensionality of problems and increase the accuracy of classification. There has been an increase in the need for such approaches in recent years due to situations involving high numbers of input attributes and samples. In other words, the big data boom today has the resulted in the challenge of big dimensionality. Featuring a distributed fuzzy cognitive map based feature selection method, this paper addressed the paramount need for a feature selection algorithm. The algorithm was tested on 15 benchmark datasets. A variety of comparisons were made with existing feature selection algorithms, including VectorSlicer, RFormula, ChiSqSelectotr, UnivariateFeature Selector, and VarianceThreshold Selector. The efficiency of the optimal feature set obtained was evaluated on different classification algorithms, namely Naive Bayes, Decision Tree, Random Forest, MultiLayer Perceptron, and Logistic Regression. The results depict that the Random Forest algorithm produced the most accurate results for most of the datasets considered. The average accuracy was above 90% when using the Random Forest classifier along with the proposed feature selection method, in contrast to 85.6% without applying feature selection. The number of optimal feature sets selected was considerably less in the case of proposed model compared to the existing methods. Conceptualization, K.H. and M.V.J. ; methodology, K.H., K.P. and M.V.J. ; formal analysis and investigation, K.H. and M.V.J. ; validation, K.H. and K.P. ; writing--original draft preparation, K.H. ; writing--review and editing, K.H., M.V.J., K.P., V.C.G. and E.P. ; supervision, M.V.J., V.C.G. and E.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Fifteen benchmark datasets available in UCI machine learning repository were analyzed in this study.
## 3 Results and Discussion
### Modeling Based on Texture Data  
To select the most informative characteristic images that explain most of the variation among samples, PCA was firstly carried out on the original hyperspectral images. The first two PC images explained 98.13% and 1.41% of the variance, respectively. Therefore, 16 textural variables were obtained from the calculated GLCM of the first PC score image. These textural variables were used as dependent variables to develop a new classification model. As shown in Table 2, compared to models based on full wavelengths and optimal wavelengths, the model based on textural features had much a lower CCR for  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & **Variable** & **Latent** & \multicolumn{3}{c}{**CCR (\%)**} \\ \cline{3-6}  & **Numbers** & **Variables** & **Calibration** & **Cross Validation** & **Prediction** \\ \hline Full spectra & 472 & 7 & 85 & 78 & 78 \\ Optimal spectra & 7 & 6 & 76 & 72 & 73 \\ Texture data & 16 & 5 & 64 & 53 & 59 \\ Data fusion & 23 & 8 & 90 & 83 & 86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CCR (%) for the calibration set, cross-validation set, and prediction set of different models.  
Figure 3: The error rate of cross-validation set of partial least square-discriminant analysis (PLS-DA) model based on full wavelengths.  
the calibration set, the cross-validation set, and the prediction set, with values of 64%, 53%, and 59%, respectively. This may be because, compared to the external characteristics explained by the textural data, internal features explained by the spectra will contain more information about the chemical composition and internal structure of the tested samples. When developing models to classify samples into different WHC groups in this study, spectral data made a greater contribution than textural features.