esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Moreover, a considerable portion of its territory comprises deserts and semi-deserts, which predetermines an increased intensity of exposure to the atmospheric aerosols that critically affect the life and health of its residents [15; 18; 33; 34; 35; 36]. There is no effective way to prevent and/or reduce the negative social impacts of atmospheric aerosols in Saudi Arabia. Owing to this reason, the spatiotemporal variations of aerosols are not explicitly quantified. Presently, there are relatively fewer studies available on the optical properties of aerosols in Saudi Arabia basedon ground measurements and satellite observations. The AOD trend over Saudi Arabia has been mainly studied using long-term MODIS and AERONET data [17, 37, 38, 39, 40, 41, 42, 43, 44]. [PERSON] et al. [45] examined the seasonal variations and characteristics of aerosol emissions over the Middle East areas. They showed that the area with the highest AOD value (i.e., large aerosols) during spring and summer, and this was attributed to the coarse particles emitted from the desert in this area as a predominant factor. [PERSON] et al. [46] studied the seasonal distribution of AOD in the Arabian Peninsula, Middle East, and Central Asia and concluded that AOD values were significantly higher in spring and summer, as the sandstorms in Saudi Arabia, Iraq, Iran, and southwest Pakistan were more frequent in spring and summer than in autumn and winter. Moreover, precipitation during the colder months can help remove aerosols, thus lowering AOD. [PERSON] et al. [45] also computed the average AOD value based on their retrievals in the Middle East from 2001 to 2019. The AOD ranged ranges between 0.0 and 1.7 over the different geographical locations. The highest AOD was mainly observed in spring and summer, whereas the lowest AOD was mainly detected in autumn and winter. A comprehensive understanding of the impact of aerosols on climate and urban air quality depends on a systematic, accurate quantification of the physical, chemical, and optical properties of aerosols with their spatiotemporal and vertical distributions. According to the literature, there are limited studies available on the vertical distribution of different aerosol layers over Saudi Arabia during the day and nighttime. Therefore, this study analyzed the vertical distribution of the optical and physical characteristics of daytime and nighttime aerosols over Saudi Arabia from 2007 to 2019 using CALIOP Level 2 Version 4 aerosol layer products. The results of this study will advance the ability to predict atmospheric aerosols, thus aiding the formulation of strategies for the improvement in the air quality in Saudi Arabia.
## 3 Results and Discussion
### Behavior Study of the MSAMT
#### 3.3.3 Effect of the Maximum Generation Number for Each Running of the G-MFEA  
The maximum generation number \(k_{max}\) for each running of the G-MFEA serves to balance the performance and optimization costs. A smaller \(k_{max}\) may make it challenging for the algorithm to locate an improved solution, and a larger \(k_{max}\) is beneficial for the discovery of the optimal solution but may result in an unaffordable running cost.  
To achieve the optimal value of \(k_{max}\) that achieves a balance between performance and optimization time, the same four test problems employed in the preceding section were utilized to assess the performance of the MSAMT variants with different \(k_{max}\) values. Figure 4 displays the box plots of the average fitness values of the MSAMT variants with different \(k_{max}\) values. It is evident from the Figure 4 that the medians, the maximum, and the interquartile range of the MSAMT were worse when the \(k_{max}\) value was equal to 10 or 20. For different test problems, the performance of MSAMT with \(k_{max}\) values greater than or equal to 30 displayed fluctuations, but the differences were not statistically significant. The maximum number of iterations for the G-MFEA was set to 50, taking into account both the optimization efficiency and the computing cost.
## 4 System Model
### Computation Model
#### 4.2.1 Local Execution  
For the local execution approach where the computation task \(j\) will be executed locally at the VR device itself, the total execution time and energy consumption can be respectively calculated as:  
\[T_{i,j}^{L}=\frac{c_{i,j}}{f_{i}^{L}} \tag{3}\]  
\[E_{i,j}^{L}=\zeta_{i}c_{i,j} \tag{4}\]  
where \(f_{i}^{L}\) denotes the computational capability (CPU cycles per seconds) of VR device, and \(\zeta_{i}\) is a coefficient, that denotes the consumed energy per CPU cycle. we set \(\zeta_{i}=10^{-11}(f_{i}^{L})^{2}\), where the energy consumption is a superlinear function of VR device frequency [40; 41].
## 3 Methodology
### Method of Maize Area Identification in the Reference Area  
By surveying the phenological characteristics of the main crops in the reference area (Figure 4), it can be seen that summer maize was sowed in early and middle June, which presented as bare soil on remote sensing images. The sowing dates of peanut and cotton were between late April and early May. The planting area of peanut and cotton showed vegetation information on remote sensing images in mid-June. It is the best time to distinguish summer maize from other summer crops in mid-June. From July to September, the summer maize was in the period from seven-leaf to maturation, which showed vegetation information on the remote sensing images. In late October, summer maize had been harvested and winter wheat was sowing or had seedling emergence in the reference area, so the summer maize planting area displayed the bare soil spectrum feature on the remote sensing images.  
Figure 3: The flowchart to map the summer maize cultivated area over Huanghuaihai Plain.  
Hence, the images with good quality on 9 June, 13 September, and 31 October, 2012, represented different spectrum features at different growth stages of maize, which were used to extract the summer maize acreage in the reference area. The specific steps were as follows:  
1. Combined with Google Earth, the training samples of road, vegetation, habitation, water body, and bare soil were selected from three periods of ETM+ data by visual interpretation. The SVM algorithm was used to classify the three images.
2. The vegetation coverage of the ETM+ image on 13 September 2012, and the bare soil coverage of the ETM+ images on 9 June 2012 and 13 October 2012, were recognized. Spatial intersection operations ([PERSON] et al., 2010) were performed for recognizing vegetation and bare soil coverage from the remote sensing images in the reference area.
3. The result of images intersection processing was superimposed with the cultivated land dataset (code 10) from the land use data of 30 m spatial resolution in 2010 (GlobeLand 30), to correct the extracted summer maize acreage in the reference area.
## 3 Decremental Antenna Selection Algorithms
### Random Decremental Selection (RDS) Algorithm  
We first propose a random decremental selection (RDS) algorithm. In the \(r\)-th step, \(k\) antennas are randomly selected from the antenna subset \(S_{(r-1)}\) and removed, the new subset is \(S_{(r)}\). Then, we calculate the minimal transmit power consumption of \(S_{(r)}\) using the SCA approach as it leads to an efficient solution with lower transmit power consumption compared with the routine SDR approach [1]. The total consumed power of \(S_{(r)}\) can be obtained by summing the circuit power and the minimal transmit power calculated by the SCA approach [5]. If the antenna subset \(S_{(r)}\) consumes less power than \(S_{(r-1)}\), the algorithm goes into the next step, otherwise, the algorithm should be terminated with \(S_{(r-1)}\) being the selected best antenna subset. Since \(k\) antennas are randomly selected and removed from the previous subset in each iteration, the tradeoff between the system performance and the algorithm efficiency can be balanced by judiciously adjusting the parameter \(k\).
## 4 Experiments and Results
### Results
This comparison highlights the practicality and scalability of our approach, especially in environments with limited computational resources, while maintaining accuracy levels comparable to or better than those of more complex architectures.
## 4 Experiment Setup
### Performance Metrics  
We adopted commonly used quality metrics to compare the correctness of the classification of the detection model. We used the following terms for determining the quality of the classification models [46]:  
* _True Positive (TP)_: the number of data items correctly classified to the positive class.
* _True Negative (TN)_: the number of data items correctly classified to the negative class.
* _False Positive (FP)_: the number of data items wrongly classified to the positive class.
* _False Negative (FN)_: the number of data items wrongly classified to the negative class.  
When dealing with unbalanced data, choosing appropriate evaluation metrics that consider the class distribution in the dataset is important. We used the following metrics:  
* Precision: precision measures the proportion of true positives among the predicted positives. This metric is useful for minimizing false positives or when the positive class is rare.  
\[\textit{Precision}=\textit{TP}/(\textit{TP}+\textit{FP}) \tag{1}\]  
Figure 8: BiGRU algorithm setup.  
Recall: recall measures the proportion of true positives among the total number of actual positives. This metric is useful when minimizing false negatives or when the positive class is important.  
\[\textit{Recall}= \textit{TP}/(\textit{TP}+\textit{FN}) \tag{2}\]  
F1-score: the F1-score is the harmonic mean of the precision and recall and is a good metric to use when there is an uneven distribution of classes in the dataset.  
\[\textit{F1-Score}= \textit{2}\times((\textit{Precision}\times\textit{Recall})/( \textit{Precision}+\textit{Recall})) \tag{3}\]  
Micro avg: calculates the overall average precision score over all classes, weighting each instance by its weight.  
Weighted avg: calculates the score by averaging the results for each class, weighting each result by the number of instances of that class in the dataset.  
K-fold cross-validation: since the data are not balanced, we used the K-fold cross-validation technique to calculate the mean accuracy. This technique is also helpful in avoiding overfitting the training data. This technique is a suitable metric for determining which of the four DL algorithms performs best.
# Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond
###### Abstract  
One of the major concerns in wireless sensor networks (WSNs) is most of the sensor nodes are powered through limited lifetime of energy-constrained batteries, which majorly affects the performance, quality, and lifetime of the network. Therefore, diverse clustering methods are proposed to improve energy efficiency of the WSNs. In the meantime, fifth-generation (5G) communications require that several Internet of Things (IoT) applications need to adopt the use of multiple-input multiple-output (MIMO) antenna systems to provide an improved capacity over multi-path channel environment. In this paper, we study a clustering technique for MIMO-based IoT communication systems to achieve energy efficiency. In particular, a novel MIMO-based energy-efficient unequal hybrid clustering (MIMO-HC) protocol is proposed for applications on the IoT in the 5G environment and beyond. Experimental analysis is conducted to assess the effectiveness of the suggested MIMO-HC protocol and compared with existing state-of-the-art research. The proposed MIMO-HC scheme achieves less energy consumption and better network lifetime compared to existing techniques. Specifically, the proposed MIMO-HC improves the network lifetime by approximately 3\(\times\) as long as the first node and the final node dies as compared with the existing protocol. Moreover, the energy that cluster heads consume on the proposed MIMO-HC is 40% less than that expended in the existing protocol.  
wireless sensor network; Internet of Things; energy efficiency; routing protocol; 5G communication 202121537  
M 2021
# Structural Damage Identification Using a Modified Directional Bat Algorithm
###### Abstract  
Bat algorithm (BA) has been widely used to solve optimization problems in different fields. However, there are still some shortcomings of standard BA, such as premature convergence and lack of diversity. To solve this problem, a modified directional bat algorithm (MDBA) is proposed in this paper. Based on the directional bat algorithm (DBA), the individual optimal updating mechanism is employed to update a bat's position by using its own optimal solution. Then, an elimination strategy is introduced to increase the diversity of the population, in which individuals with poor fitness values are eliminated, and new individuals are randomly generated. The proposed algorithm is applied to the structural damage identification and to an objective function composed of the actual modal information and the calculated modal information. Finally, the proposed MDBA is used to solve the damage detection of a beam-type bridge and a truss-type bridge, and the results are compared with those of other swarm intelligence algorithms and other variants of BA. The results show that in the case of the same small population number and few iterations, MDBA has more accurate identification and better convergence than other algorithms. Moreover, the study on anti-noise performance of the MDBA shows that the maximum relative error is only 5.64% at 5% noise level in the beam-type bridge, and 6.53% at 3% noise in the truss-type bridge, which shows good robustness.  
directional bat algorithm; structural damage identification; constrained optimization problem; modal parameters; objective function +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 4 Proposed AMR Algorithm
### Feature Extraction  
For each of the five modulation techniques, HOCs were derived from the received signal. The cumulants were made up of moments, which can be presented by the general expression shown in Equation (3), whereas the extracted HOCs were formulated from Equations (4)-(13). The features extracted from the received signal are tabulated in Table 1.  
\[m_{pq}=E[v(t)^{p-q}v^{*}(t)^{q}] \tag{3}\]  
\[\zeta_{1}=C\{v(t),v(t)\}=E[v^{2}(t)] \tag{4}\]  
\[\zeta_{2}=C\{v(t),v^{*}(t)\}=E[|v(t)|^{2}] \tag{5}\]  
\[\zeta_{3}=C\{v(t),v(t),v(t),v^{*}(t)\}=m_{40}-3m_{20}m_{21} \tag{6}\]  
\[\zeta_{4}=C\{v(t),v(t),v^{*}(t),v^{*}(t)\}=m_{42}-|m_{20}|^{2}-2m_{21} \tag{7}\]  
\[\zeta_{5}=C\{v(t),v(t),v(t),v(t),v(t),v(t)\}=m_{60}-15m_{20}m_{40}+30m_{20}^{3} \tag{8}\]  
\[\zeta_{6}=C\{v(t),v(t),v(t),v(t),v(t),v^{*}(t)\}=m_{61}-5m_{21}m_{40}-10m_{20} m_{41}+30m_{20}^{2}m_{21} \tag{9}\]  
\[\zeta_{7}=C\{v(t),v(t),v(t),v(t),v^{*}(t),v^{*}(t)\}=m_{62}-6m_{20}m_{42}-8m_{ 21}m_{41}-30m_{22}^{2}m_{40}+6m_{20}^{2}m_{22}+24m_{21}^{2}m_{22} \tag{10}\]  
\[\zeta_{8}=C\{v(t),v(t),v(t),v^{*}(t),v^{*}(t),v^{*}(t)\}=m_{63}-9m_{21}m_{42}+ 12m_{21}^{3}-3m_{20}^{2}m_{43}-36m_{22}m_{41}+18m_{20}m_{21}m_{22} \tag{11}\]  
\[\zeta_{9}=C\{v(t),v(t),v(t),v(t),v(t),v(t),v(t),v(t)\}=m_{80}-35m_{40}^{2}-28m _{60}m_{20}+420m_{40}m_{20}^{2}-630m_{20}^{4} \tag{12}\]  
\[\zeta_{10}=C\{v(t),v(t),v(t),v(t),v^{*}(t),v^{*}(t),v^{*}(t),v^{*}(t)\}=m_{84} -16\zeta_{8}\zeta_{2}+1\,\zeta_{3}\,^{1}-18\zeta_{4}\,^{2}-72\zeta_{4}\zeta_{2 }\,^{4} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \(\zeta_{1}\) & \(\zeta_{2}\) & \(\zeta_{3}\) & \(\zeta_{4}\) & \(\zeta_{5}\) & \(\zeta_{6}\) & \(\zeta_{7}\) & \(\zeta_{8}\) & \(\zeta_{9}\) & \(\zeta_{10}\) \\ \hline
**BPSK** & 0.78 & 2.45 & 0.29 & 0.27 & 0.46 & 31.41 & 19.65 & 77.40 & 123.07 & 2284.53 \\ \hline
**QPSK** & 0.05 & 2.41 & 0.09 & 0.03 & 0.58 & 32.95 & 0.40 & 77.87 & 12.49 & 2210.43 \\ \hline
**QAM** & 0.16 & 3.35 & 0.03 & 0.28 & 0.40 & 0.83 & 42.92 & 210.23 & 1.27 & 8477.33 \\ \hline
**16-QAM** & 0.79 & 11.64 & 0.10 & 0.51 & 0.56 & 13.34 & 384.43 & 956.81 & 331.22 & 13,052.11 \\ \hline
**64-QAM** & 1.10 & 42.16 & 0.04 & 0.54 & 0.58 & 31.43 & 1004.16 & 4053.52 & 1186.00 & 228,652.61 \\ \hline \end{tabular}
\end{table}
Table 1: HOC values.  
Figure 2: Proposed AMR module.
## 4 Experiment Results
### Alignment  
Usually, Offner and Wynne-Offner spectrometers have a loose alignment tolerance when the MTF is used as a criterion. However, the concentricity of convex grating and concave mirror is quite sensitive to spectral distortions. In the long-slit spectrometer presented in this paper, the concentricity error is rigorous, which must be controlled below 0.01 mm to keep the smile and keystone at less than 10% of a pixel. To realize the high-precision alignment of spectrometers, we developed a high-precision point-source microscope, as shown in Figure 14a. It is used to find spherical centers of optical surfaces to assist the alignment of spectrometers, with a lateral accuracy of 0.4 um and an axial accuracy of 1.0 um. In B1 and B2 bands, it is used to determine the relative position of the meniscus-grating and the concave mirror, as shown in Figure 14b. In the B3 band, it is used to determine the relative position of two separate menisci, the convex grating, and the concave mirror. The experiment device for adjusting the separate menisci is shown in Figure 14c,d shows the image of two spherical centers after alignment, with an interval of 3.6 um. After aligning the convex grating and the concave mirror, we aligned the slit. The slit is required to be aligned parallel to the grating groove, so that a uniform spectral image can be obtained; that is, the polychromatic image of any field point should form a dispersion line that is straight and perpendicular to the slit, which is aligned with the detector array. The slit-to-grating rotation is adjusted by monitoring the spectral lines' arrangement while the slit is illuminated by a mercury-cadmium (Hg-Cd) lamp. Figure 15 shows the spectral lines in the image plane when the slit-to-grating rotation is or is not corrected.  
The B4 and B5 spectrometers are designed in a snap-together manner [52; 53] with an all-metal structure to minimize the elements and simplify the alignment process. These two spectrometers can achieve high performance only by assembling without adjusting. The prototypes of spectrometers for each band are shown in Figure 16.  
Figure 14: Alignment of spectrometers by utilizing a point source microscope. (**a**) Self-developed point-source microscope, (**b**) alignment of meniscus-grating and concave mirror in B1 and B2 spectrometers, (**c**) alignment of two separated menisci in B3 spectrometer with point-source microscope, (**d**) star images of two spherical surfaces in the field of the point source microscope.
## 4 Experiment and Results
### Settings  
The BAT-Transformer is a model based on the transformer architecture, de-signed to process time series data such as bus arrival delays. In this study, we configured the model with a depth of eight layers and set the dropout rate to 0.3, as this was the best performing layer in the transformer-encoder when we tried training with multiple layers. The dropout mechanism, which randomly disables neurons during training, was used to improve the model's generalization performance. Dropout is a widely used technique to prevent overfitting by ensuring that the model does not rely heavily on a particular pattern, thereby improving its generalizability.  
Figure 8: Data Distribution of data before and after resampling by time zone: blue line shows before resampling and red line shows after resampling.  
The learning rate was set to \(1\times 10^{-5}\) to control the rate at which the model weights are updated, maintain stability, and prevent excessive changes. The number of epochs was set to 50 to provide enough learning time for the model to effectively learn the data, and was set to 50 as the appropriate learning epoch after checking the instability of the model due to overfitting if the number of epochs was higher than 50, and the poor learning progress if the number of epochs was too low.  
The model training was conducted in a local environment using NVIDIA Quadro P2000 GPU, 16.0 GB RAM, Intel i7-8700 CPU, Visual Studio Code development environment, and Python 3.11. Hardware and software environments have a significant impact on the efficiency of model training, and GPUs in particular enhance the parallel processing capabilities of deep learning models, significantly accelerating the learning process. The Adam algorithm, which provides both learning speed and stability, is effectively used in many deep learning models and was also used in this experiment to update the model parameters. We also evaluated the model performance during the learning process using various evaluation metrics to objectively assess the predictive performance of the model. In this study, we used root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R\({}^{2}\)), and time-weighted error (TWE) as the main evaluation metrics.
# Heuristic Fuzzy Approach to Traffic Flow Modelling and Control on Urban Networks
## 5 Simulation Results
Among the fuzzy controllers, the best result of 61 h of total travel time is obtained for the controller with three-valued logic inputs and five-valued logic output. Figure 6 presents the delay time measured in seconds per kilometre. For this traffic indicator, the best result of 59 s/km is obtained for the fuzzy controller with three-valued logic inputs and five-valued logic output. The classical controller gives the second-best result with 61 s/km. Figure 4: Flow for the whole network of crossroads measured in vehicles per hour. Figure 5: Total travel time for the whole network measured in hours. Figure 6: Delay time per car measured in seconds per kilometre. Figure 7 presents travel time by car measured in seconds per kilomet. Here again, the best result of 126 s/km is obtained for the controller with three-valued logic inputs with five-valued logic output. The classical controller gives the second-best result with 127 s/km. A conclusion can be drawn from the presented traffic indicators that in terms of the overall traffic system performance, the classical controller provides better results. Whereas, when it comes down to behaviour per car unit, Fuzzy Controller 3 (three-valued logic for the inputs and five-valued logic for the output) gives the best results. The novelty of the study is that it examines, in a heuristic setting, the performance of the four different fuzzy controllers with the hierarchical system and compares them with a classical controller that is used as a benchmark. The practical implications of this study are that the research results can be replicated using a different simulation environment by following the details presented for the design of the fuzzy controllers. Table 21 presents the graphical results in a tabular form, where the change in the traffic indicators (flow, total travel time, delay and travel time) is shown, based on the experiment with each controller. It can be seen that Fuzzy Controller 3 and the classical controller are the best performing ones with each of them producing the best results for two of the four indicators while being second-best for the remaining two indicators. However, Fuzzy Controller 3 is easier to interpret than the classical controller due to the fact that its linguistic rules are easy to understand even by non-expert users.