esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 4 Experimental Design
### Benchmark Datasets  
The proposed ITACIE-GP method is evaluated on image datasets of varying difficulties. They are FEI1 [38], FEI2 [38], JAFFE [39], KTH [40], Flower [41], and ORL [42]. Figure 3 displays examples of these image datasets.  
FEI1 and FEI2 are datasets related to binary facial expression classifications, distinguishing primarily between smiling and natural expressions. The JAFFE dataset comprises images categorized into seven facial emotions: anger, fear, disgust, neutral, sadness, surprise, and happiness. The KTH dataset is designed for texture classification and includes images from 10 different categories. These images differ in lighting, angles, and sizes. The Flower dataset focuses on flower classification, featuring images of two distinct flower types. ORL, commonly used in face recognition research, consists of images from 40 different individuals. Each individual contributes 10 distinct facial photos, capturing a range of facial expressions and variables, including the presence or absence of glasses. Table 10 describes the detailed information of the image datasets, including image size, the split between training and testing sets, and the number of classes in each dataset.  
Figure 3: Example images from the FEI_1, FEI_2, JAFFE, FLOWER, KTH, and ORL datasets.  
This paper formed a degraded image dataset based on the parameters proposed in reference [16,18] and the aforementioned image datasets. Specifically, different levels of blur, low contrast, and noise, and various sizes of square occlusions in different positions were introduced separately into the dataset, resulting in datasets for blurred scenarios, low contrast scenarios, noisy scenarios, and occluded scenarios.  
Table 11 provides a detailed list of the parameters used to create these datasets, including the degree of blur, level of contrast, size of noise, and size of the occluding squares. Figure 4 shows examples of degraded images in four different scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Image Size** & **Training Set Size** & **Test Set Size** & \# **Class** \\ \hline FEI1 & 180 \(\times\) 130 & 150 & 50 & 2 \\ FEI2 & 180 \(\times\) 130 & 150 & 50 & 2 \\ Jaffe & 128 \(\times\) 128 & 140 & 73 & 7 \\ Flower & 100 \(\times\) 100 & 113 & 38 & 2 \\ KTH & 100 \(\times\) 100 & 600 & 210 & 10 \\ ORL & 92 \(\times\) 112 & 280 & 120 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Image dataset parameters.  
Figure 4: Some examples from image datasets in four different scenarios. (**a**) Examples of blurred Images with Different Standard Deviations \(\sigma\) (**b**) Examples of Low-Light Images with Different Contrasts (**c**) Examples of Noisy Images with Different Variances \(\sigma^{2}\) (**d**) Examples of Occluded Images with Obstructions of Different Sizes.
## 4 Results and Discussion
### Ablation Study  
To further investigate the effectiveness of the modules in DBSTGNN-Att, we conducted evaluations for three DBSTGNN-Att variants: (1) W/o an attributed KNN graph: It does not use the attributed KNN graph as a model input and does not fuse multi-model features (BSs and POIs). (2) W/o a dual GLU: The model does not use a double GLU structure. (3) W/o an attention mechanism: Dual branch-gated convolution does not use the attention mechanism. The prediction performance comparisons of the ablation study of DNSTGNN-Att are shown in Table 4, where w/o denotes the deletion of a component.  
By examining Table 4, it is evident that the aforementioned three components have a positive impact on the prediction of cellular traffic. Among these components, the dual GLU structure exhibits the greatest improvement in the model's prediction accuracy. This can be attributed to the fact that the dual GLU structure enhances the model's flexibility, enabling it to better adapt to diverse data distributions and complex models. Additionally, each GLU has the capacity to learn distinct feature representations, which are then combined through element-level multiplication operations. This integration allows the model to more effectively capture the nonlinear relationships present in the input data, thereby enhancing the model's expressive capabilities and prediction performance.  
Furthermore, the attributed KNN graph incorporates BSs, POIs, and SAs, encompassing a wider range of information that is beneficial for cellular traffic prediction. The multi-modal features are fused and input into the neural network, which increases the interpretability of the model and improves the prediction accuracy.  
The influence of the attention mechanism on the prediction accuracy remains minimal in short-term prediction. However, as the collection interval of cellular traffic data expands, the advantageous impact of the attention mechanism on the prediction accuracy becomes increasingly prominent. This is because the attention mechanism enables the model to assign different weights to different parts of the input, thereby paying more attention to information that is more important to the current task. This mechanism enables the model to better handle long sequences and complex inputs, and improves the expression ability and prediction performance of the model.
# Speckle Noise Detection and Removal for Laser Speech Measurement Systems
## 4 Experiments and Discussion
### Experimental Setting  
The detection scene of the whole experiment is shown in Figure 8. We used a laser speech measurement system that we constructed by ourselves, as shown in Figure 8a, for the remote speech detection. The selected laser speech measurement system had a nominal working distance of 5-500 m and operated at a wavelength of 1550 nm. Figure 8b shows the target side scenario. The scenario was comprised of a loudspeaker playing clean speech,  
Figure 7: Laser captured speech signal showing (**a**) the raw-detected signal, (**b**) noise locations identified by the proposed method, (**c**) the enhanced-signal, and (**d**) clean signal.  
an empty paper cup as a detection target, and an acoustic shell meter for detecting the decibel that reaches the surface of the paper cup. In the experiment, the loudspeaker was located 1 m away from the paper cup, and started playing pure voice. The volume of the loudspeaker was adjusted so that the sound intensity level on the surface of the paper cup did not exceed 80 dB. We focused the measuring light on the surface of the paper cup and then obtained a speech signal. By increasing the detection distance from 100 to 300 m and creating human interference, we obtained different intensities of speckle-noise for the noisy speech with various durations.  
Clean speech was used for the speech played by the loudspeaker. This was taken from the Librispeech ASR corpus dataset [29]. LibriSpeech is a corpus of approximately 1000 h of 16 kHz read English speech. In this experiment, we randomly chose 50 groups of voice examples from one female reader and one male reader in the \"dev-clean\" subfolder, as the pure speech. In order to create speckle noise and therefore the noisy speech, we set interference with indefinite duration, such as occlusion on the measured optical path. We ended up with 50 groups of voice examples at each detection distance, giving a total of 150 groups of speech with speckle noise. The pure speech and noisy speech measured in the experiment are available on Gite [30].
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
###### Abstract
This framework is based on a comprehensive physical-layer emulator to provide insights into key network performance indicators such as the probability of success, and energy consumption. Additionally, to mitigate the co-channel interference and Doppler shift effect with reduced power consumption, the IoT-over-Satellite network utilized LoRa CSS, as an example, in four different random access mechanisms, namely; (i) pure ALOHA, (ii) scheduled access, (iii) scheduled access with repetition, and (iv) scheduled round robin. The contributions of this work are summaries as follows:  
* It presents an emulation framework that predicts the uplink LoRa performance for IoT-over-Satellite networks. * It evaluates the uplink packet error rate (PER) and bit error rate (BER) of the practical Walker-Delta constellations and the Walker-Star constellations. * It evaluates the uplink PER and BER of the IoT-over-Satellite system in four different access methods in a shared radio spectrum. * It shows the impact of fading, Doppler shift, and practical interference to the IoT-over-Satellite system performance. * It provides a methodology for capturing the amount of wasted energy of the IoT end-devices in different access methods. * It evaluates the impact of intra-SF interference in LoRa communication.
# Development and Assessment of a 4D Printing Technique for Space Applications
## 2 Materials and Methods
### 4D printing  
The term 4D printing, coined by [PERSON] a decade ago [2], represents a paradigm shift in additive manufacturing. It involves the creation of multi-material prints with the unique ability to transform over time. [PERSON] defines it as a process enabling customized material systems to change from one shape to another directly off the print bed [2].  
Since its introduction, 4D printing has evolved into a dynamic field of research [1]. The initial definition by [PERSON] has undergone several refinements, leading to a broader context. In modern terms, 4D printing includes structures that, post-printing, can autonomously alter their shape in response to specific stimuli [1]. This includes the utilization of SMPs, a crucial aspect of 4D printing.
## 3 Wavelet Analysis  
### Continuous Wavelet Transform  
A continuous wavelet transform (CWT) can simultaneously extract the frequency and time scale information of signals in a variety of wavelet scales, so it is widely used to extract the periodic characteristics of time series in meteorological analysis. Generally, the continuous wavelet transform is defined as follows [28]:  
\[W_{x}(a,b)=\frac{1}{\sqrt{a}}\int_{-\infty}^{\infty}x(t)\varphi\bigg{(}\frac{t- b}{a}\bigg{)}dt \tag{1}\]  
where \(W_{x}(a,b)\) is the wavelet coefficient; \(a\) and \(b\) are the scale parameter and translation parameter, respectively; \(x(t)\) is the time series; \(t\) is time; and \(\varphi\bigg{(}\frac{t-b}{a}\bigg{)}\) is the daughter wavelet, which is obtained from the mother wavelet \(\varphi(t)\) after translation and scaling in the time series.  
The wavelet square variance is the integral of the square norm of wavelet coefficients in the time domain, which can represent the energy distribution of periodic waves on  
Figure 1: Schematic diagram of annual solar radiation distribution in mainland China.  
different scales. For a particular scale, the larger the wavelet square variance, the stronger the oscillation intensity. The wavelet square variance is defined as follows [28]:  
\[Var(a)=\int_{-\infty}^{\infty}|W_{x}(a,b)|^{2}dt \tag{2}\]
## 5 Discussion
### Comparison with Existing Method and Identified Limitations
To enhance the accuracy of offset field analysis, we implemented a single SNR threshold to exclude pixels with low signal-to-noise ratios across the offset field. However, factors such as terrain characteristics and surface deformation also impact the SNR values. A uniform threshold for pixel exclusion may result in the loss of some deformation information. Thus, future research could set different SNR thresholds based on the surface characteristics of various regions within the offset field. For instance, in the areas of deformation, appropriately lowering the SNR threshold may facilitate the capture of more comprehensive offset field information. (2) Our study on the correction of attitude angle distortion addressed how varying pixel qualities might affect correction effectiveness. Our research lacks a discussion on whether weighting pixels based on the SNR of each pixel could enhance the effectiveness of attitude angle error correction. (3) The selected experimental area is relatively flat, neglecting the impact of terrain artifacts on the accuracy of the offset field in complex terrain environments. This error can be corrected using the method of constrained spatial baseline and radiation intensity described in [17].
# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 4 Performance Evaluation
### Prediction Result Sharing via V2X  
The proposed system transmits and receives the predicted driving behavior message in the form of BSM every 100 ms through V2X communication. As a result of the experiment, the number of transmitted and received BSM packets was measured the same. Figure 17 shows the distribution of communication delay. The average delay of V2X communication was observed as 4.8 ms, the minimum delay was 1.2 ms, and the maximum delay was 13.3 ms. Therefore, the proposed system has proven to be effective in predicting and sharing aggressive driving behavior even in real environments.
# Removal of Phosphate from Aqueous Solution by
## 1 Introduction
After considering the advantages and disadvantages of zeolite and biochar for removing phosphorus from water, this study assumes that the employment of zeolite and biochar as a matrix for the preparation of zeolite-biochar composites has tremendous potential for removing phosphorus from water. The preparation process of biochar pyrolysis can indirectly provide thermal modification for zeolite, and at the same time, the organic acids released during the pyrolysis of biochar feedstock have the potential to modify zeolite for acid modification. Zeolite can provide attachments for biochar, reduce the volume of biochar, and facilitate transportation [37]. Thus, the processes for preparing zeolite-biochar composite materials may significantly lower the amount of biochar. Although the adsorption effects of the composite material on phosphorus in water are well-known, the mechanisms of its adsorption are still unclear. Therefore, determining the optimal ratio and selecting a suitable method for the preparation of the biochar and zeolite composite material, as well as studying its adsorption performance and mechanism, is very important for reducing water eutrophication. The main intentions of this work are to: (1) prepare a zeolite-biochar composite material with the most suitable adsorption performance (proportion, mixing method), (2) explore the influences of dosage and pH on the phosphate adsorption, and (3) highlight the phosphate adsorption mechanisms of the zeolite-biochar composite.
# Impacts of MT-HVDC Systems on Enhancing the Power Transmission Capability
## 5 Simulations and Results
### Interconnected AC/DC Grid Simulations and Nomogram  
Interconnected Kunder's AC network with CIGRE B4 DC system of Figure 1 is assessed with Matlab/Simulink simulations, using parameters given in Tables 4-6. Security region nomogram is shown in Figure 10 with red curve drawn with technique explained in Section 3. Power transfer and stability evaluation for interconnected AC/DC configuration is shown in Figure 9 through red wave. Figure 9 also provides comparison between case 1 and 2. As the power transmission is increased beyond 649 MW, the stand-alone system becomes unstable but the interconnected AC/DC system remains stable. This can also be seen in Figure 8, as we move towards point 'd', the system is still in the secured region. Load flow analysis results are given in Table 7. It shows that whence DC grid is employed with two-area system some of the power flows through the HVDC network, hence enhancing the power transmission capability as well as the stability of the system.  
increased in steps and a limit is reached at 1130 MW as shown in Figure 10. When the power exchange is increased beyond 1130 MW, the system of Figure 1 becomes unstable and oscillations are observed. The same results are shown in Figure 8, i.e., interconnected AC/DC grid remains stable till point 'f' (1130 MW). It is evident that interconnected system has larger secured operation area than standalone two area AC system. MT-HVDC system provides additional path for flow of power and extra stability to [PERSON]'s two area system.  
In [32], the authors computed 749 MW as maximum power transfer capability of [PERSON]'s two area system which is 381 MW less than 1130 MW. Thus, proposed approach based on DC OPF gives improved power transmission results for AC systems when connected with MT-HVDC girds.
# IoT-Based Cotton Plant Pest Detection and Smart-Response System
## 3 IoT-Based Smart Detection and Response System Architecture  
In this section, we describe our proposed cotton crop pest detection and a smart response in detail. In our initial work, we developed a prototype system to detect cotton bollworm pests. Our proposed device consisted of Sharp's high-resolution motion-detecting sensors and a ZigBee communication module connected with an Arduino ATMega module, and a used Arduino editor and Sketch to program all these modules.  
We tested this detection prototype in three phases. In the first stage, we tested in a controlled environment in the university research lab for 15 days, and the results were very encouraging. Then, we deployed it in the cotton fields for several days, and again we observed a reasonably good detection rate. The sensors in the prototype send a detection packet containing data in Hexa decimal values, including its coordinates, to gateway devices, and it tells us the location of the sensor, and we would know in which part of the tested area has the presence of pests. It took us four months to complete this process. It was a small-scale test due to the unavailability of a large number of sensors.  
To test large-scale implementation, we used a cup-carbon simulator [40] to test various scenarios. In the simulation, we program a device as a drone to test our smart response system after detecting pests. We proposed a drone movement prediction algorithm for targeted spray. In the future, we plan to implement the prosed pest detection and response system in large cotton fields, subject to the availability of sufficient funds.
# A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks
## 2 System Model
### Problem Formulation  
The objective in this work is to maximize throughput, however, maximizing throughput can lead to higher energy consumption. Since the devices have limited energy, the higher energy consumption can lead to dead nodes in the network and subsequently network singularities. Therefore in this paper, we aim to increase EE. Based on the sum throughput and energy calculations (4) and (11) in the previous subsections, we can define the EE as:  
\[\text{EE}=\frac{\zeta}{E_{tot}}=\frac{\sum\limits_{i\in I}\sum\limits_{s_{i}} \lambda_{i_{s}i}}{\sum\limits_{i\in I}\sum\limits_{s_{i}}E_{tx}(i,s_{i})+\sum \limits_{i\in I}E_{tx}(i)}. \tag{12}\]  
The objective function can now be written as:  
\[\begin{split}\underset{H_{i,s_{i}}}{\text{max}}& \text{EE}\\ \text{s.t.}& E_{res}(i)>0\\ & R_{max}\leq 30m.\end{split} \tag{13}\]  
In the following we present an intelligent routing technique based on RL to maximize the EE.
# Hardware-Based Architecture for DNN Wireless Communication Models
## 1 Introduction
In this paper, from all of the shortcomings of the above studies, we propose three DL-based MIMO OFDM receivers capable of improving receiver performance. There are two proposed models to help improve the receiver performance of the MIMO OFDM system, while the other is proposed to completely replace the transmitter using DL. In particular, our systems use the point-to-point MIMO OFDM model, which has not been considered by any previous work, with the high PAPR problem at the transmitter during simulation. In addition, the size of our DL models is also relatively small compared to the DL models in [12]. With model type I, the received symbols are first pre-processed by the Least Square (LS) estimator to estimate channel information and the Zero Forcing/Maximum-Likelihood Detection (ZF/MLD) to detect the signal, and then fed to a DNN model to improve the BER performances. With model type II, we use a DNN model to improve the noise cancellation, and then the signals are clearer to pass through the channel estimator and signal detector. The LS estimator and the ZF/MLD signal detector are still used in this model. Finally, the most special is model type III, which completely replaces the conventional DSP signal demapping and detection modules at the receiver of the MIMO OFDM system. In this model, the GI is removed from the received signals and then these received signals are fed to the DNN model for signal detection. Therefore, the received signals do not need to go through the channel estimator and signal detector to get the original signals at the transmitter. Then the architecture is optimized and less complex than a conventional MIMO OFDM system. Furthermore, we also design the appropriate hardware architectures for the three DNN models proposed above. In addition, our hardware architecture is built using several techniques, such as pipeline and quantization, that increase computational speed and reduce storage resources compared to conventional designs. The remainder of this paper is organized as follows. Section 2 shows the system model. Section 3 presents a background knowledge of conventional detectors in the MIMO OFDMsystem and non-linear noise in the MIMO OFDM system. Section 4 describes our proposed models. Section 5 shows the hardware architectures in detail. Section 6 presents the results after running the simulation. Finally, Section 7 concludes the paper.
# Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging
## 3 Experimental Results and Discussions
### Hyperparameter Optimization and Model Visualization
#### 3.5.3 Ensemble by All Model ROC and P-R Curve  
Figure 12 evaluates the performance of seven machine learning models using the ensemble voting classifier. The performance of the models is measured using two main metrics: Area Under the Receiver Operating Characteristic curve (AUC) and Average Precision (AP).  
These metrics provide valuable insights into each model's ability to separate classes and deliver precise classification. The linear model Logistic Regression performed well, achieving an AUC of 0.9618 and an Average Precision (AP) of 0.9619, indicating high precision in distinguishing between positive and negative classes. Similarly, the SVM model showed strong performance, comparable to Logistic Regression, with an AUC of 0.9620 and an AP of 0.9618. However, K-Nearest Neighbors (KNNs) underperformed  
Figure 11: Ensemble of all model scores with MAE.  
Figure 12: Ensemble by all models (**a**) ROC curve, (**b**) P-R curve.  
relative to the other models, with an AUC of 0.9148 and an AP of 0.8732, suggesting limitations in handling this dataset effectively in terms of precision and class separation.  
The Random Forest model achieved the highest overall performance, with an AUC of 0.9767 and an AP of 0.9812, demonstrating its ability to manage the dataset's complexity and provide accurate classification. The XGBoost model also showed strong performance but with a slightly lower AUC of 0.9553 and AP of 0.9608, while still proving to be robust. LightGBM performed similarly well, with an AUC of 0.9739 and AP of 0.9775, positioning it among the top performers.  
Finally, CatBoost, which is optimized for categorical data, demonstrated strong class separation and accuracy, with an AUC of 0.9635 and AP of 0.9670.  
Overall, ensemble methods, particularly Random Forest, LightGBM, and XGBoost, proved to be highly effective in classifying the Titanic dataset. These models captured the data's underlying patterns more effectively than individual models like Logistic Regression and SVM, thanks to the strengths of ensemble learning. This finding suggests that combining models using ensemble techniques can lead to highly accurate and precise predictions. Moving forward, Stacking and Voting will be applied as ensemble methods, along with SWA, to further enhance prediction precision.