esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Approach and Associated Methods
### TSP and Visibility Datasets  
Several sets of data point measurements have been collected from different sources (Table 2). Diversifying collected datasets provides region-independent estimations and the viability of drawing generalized conclusions. It is worth noting that the values tabulated represent the minimums and maximums of the collected data. The data points between the extrema are generally known to obey the form of a power function [16].  
The concisely listed variations in Table 2 confirm the notion of difficulty in producing a general empirical relationship. This is observed in Figure 1, which is a visualization of quantities in Table 2, where, for example, [PERSON] et al.'s [20] and [PERSON]'s [17; 18] rather similar visibilities correspond to two different TSP concentrations in the air. However, despite the noted variations, the data plotted in Figure 1 confirms an inversely-proportional relationship between the visibilities and TSP concentrations, which generally agrees with the relationship of visibility (V) and TSP concentration (\(M\)) parameters in the equations listed in Table 1.  
Accounting for possible errors in measurements, Table 2 is supplied with additional information in that regard. However, despite the lack of thorough documentation for some studies, it is believed that most of the sources cited in this work hold great credibility, particularly the recent ones, given the different techniques employed to conduct robust measurements [23].
focused on the imperviousness of their materials. A high asphalt content and low gradation index were adopted. The low content of coarse aggregate leads to the deterioration of the stability of skeleton structure formed by asphalt concrete. Under a certain slope, the asphalt mortar flowed and the slope thermal stability deteriorated. Therefore, it is of great significance to study the slope stability of asphalt concrete in combination with the engineering environment of asphalt concrete impervious layer plates.  
[PERSON] [14; 15] studied the performance of asphalt concrete by changing the filler content, and found that an increase in the filler/asphal ratio was unfavorable for the thermal stability of asphalt concrete; [PERSON] [16] explored the influence mechanism of asphalt concrete slope flow through a slope flow test, and found that the influence of mineral material gradation, asphalt content and filler content on the slope thermal stability of asphalt concrete varies; [PERSON] [17] compared, using a slope flow test, asphalt concrete evaluated by the European Union and China, and found that the method of using a Marshall specimen to conduct a slope flow test in China is still convenient. If the mix ratio is appropriate, the slope flow value meets the specification requirements (less than 0.8 mm); [PERSON] [18] analyzed the factors influencing the slope thermal stability of impervious asphalt concrete panels and concluded that the asphalt content had the greatest influence on the slope flow value.  
These research results prompted further research on the slope stability of asphalt concrete slabs. However, there are relatively few studies on the influence of asphalt concrete slope thermal stability with a high-grade index and large aggregate size, and there is a lack of relevant calculation models.  
Therefore, in this study, we conducted a slope flow test, using a maximum aggregate particle size of 19 mm and the advanced distribution index (0.35-0.45). The test plan was formulated to carry out the slope flow value test. The variation law of slope stability of asphalt concrete during the test time was studied and analyzed, and a prediction model of slope flow value and test time of asphalt concrete was established. The aim was to provide a reference for the study of slope stability of impervious asphalt concrete slabs in pumped storage power stations in the future.
## 4 Experimental Performance Evaluation  
The proposed method was evaluated by its speech recognition performance on the _LibriMix_ dataset [13], where each audio sample contains only two speakers. Specifically, the speech recognition performance was evaluated based on the speaker embeddings from the speech profiles used in the proposed method, as well as the number of utterances extracted for the profile. Furthermore, we compared the speech recognition performance across three different systems using speaker embeddings and speaker profiles: single-speaker ASR, existing multi-speaker ASR, and the proposed SAT-ASR methods.
# Tunable Fano Resonances in an Ultra-Small Gap
## 3 Results and Discussion
### Effect of Nanocube Size on Fano Resonance  
Figure 4a shows three scattering spectra from difference sized silver nanocubes acquired with the same 2 nm gap thickness. In Figure 4b, a series of simulated scattering spectra resembling the experimental data are obtained by changing the size of the silver nanocube. The simulated radii \(r\) of the rounded nanocube corners are 9 nm (blue), 9 nm (red), and 10 nm (black), and the corresponding side lengths \(d\) are 73 nm, 74 nm, and 75 nm, respectively. As the nanocube size increases in the NCoM structure, the Fano depletion dips and red-shifts. When the particle sizes become larger, the distance between the charges on the two opposite surfaces becomes larger. The Coulomb effect between them is weakened, which results in a decrease in the LSPR frequency and the red-shifted of spectra [46]. Therefore, when the nanocube size increases, the dip position of the spectra is red-shifted. Therefore, the Fano resonance in the NCoM structure is very sensitive to the nanocube size.  
Figure 4: (**a**) Scattering spectrum of three different samples in experiment; (**b**) Theoretically simulated scattering spectrum of silver nanocubes with three different sizes.  
Figure 3: Experimental (**a**) and numerically simulated (**b**) scattering spectrum of individual silver nanocubes with different spacer thicknesses: 2 nm, 3 nm, 4 nm, 6 nm, respectively.  
In addition, we also studied the similar NPoM structure of silver nanocubes with a side length of 100 nm and gold nanospheres with a diameter of 100 nm under the same experimental condition. However, no mode splitting was obtained. It shows that only the specific shape and the appropriate ratio of side length to gap could generate Fano resonance. Moreover, the rounded corner size of the cube has an impact on its scattering spectrum, which is related to the effective mode volume of the plasmonic nanocavity.
## 4 Evaluation
### Experimental Setup
One can modify the settings of the program during runtime by sending predefined commands via the serial interface to the Arduino UNO or select the message that is transmitted. On the receiving side of the experimental setup, a Canon EOS D1100 DSLR camera is used to record videos that are then analyzed using a python script. However, the stock firmware from Canon does not allow setting the exposure time to a fixed value when recording a video. Thus, the third-party firmware-addon Magic Lantern [27] was installed on the receiving camera, which enables various manual settings for recording videos with a Canon DSLR camera. The exposure time was set to a value between 1/8000 s and 1/1000 s, depending on the experiment. As those exposure settings lead to very dark images, except if the environmental lighting is very bright, additionally the aperture was set very low and the sensitivity of the image sensor, i.e., the ISO, was set very high, to get brighter images. For the experiments, a distance of approx. 1.5 m was chosen between the transmitting car model and the receiving camera. In the scale of 1:24, this equals a distance of 36 m, which is a common distance between two cars on the road, following each other. This experimental setup only targets the evaluation of the raw data transmission and bit errors caused by the modulation itself. Other influences such as movement of transmitter and receiver and weather are not simulated. Especially the impact of rain or fog cannot be simulated realistically in the small scale. These evaluations will be done in future research when implementing the system using full-sized vehicles on the road.
# Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification
## 1 Introduction
[PERSON] and [PERSON] [20] introduced in 2015 the VGG model, which accomplished an extremely well-behaved performance with ImageNet and became used-widely in different studies of CV. On the other hand, one of the most significant drawbacks of DL is that it requires more processing time due to a large amount of data it must deal with. As a result, they employ various parallel hardware architecture platforms, such as FPGA and GPU, to overcome these difficulties. Field programmable gate array (FPGA) technology is an emerging hardware technology developed in recent years [21]. It has shown strong potential and capabilities for embedded intelligence (EI) applications. As compared to Graphics processing unit (GPU) and CPU technologies, the core benefit of the FPGA is its capability to customize the hardware implementation to achieve the applicability of the specified needs of the EI algorithm. Thus, the FPGA becomes an effective hardware solution for more significant energy saving and higher computation processing [22]. For example, the FPGA can use proper quantized memory representations along with fixed-point computations instead of floating-point. Compared to GPU and CPU systems, the FPGA dedicated hardware further benefits the FPGA-based systems. Therefore, the integration of embeddedsystems/devices with decision-making algorithms, Machine Learning (ML) techniques, as well as methods that are commonly achieved on central computation systems such as cloud computing was introduced by several researchers. The following are the main contributions provided in this research:  
* A network proposal was used to group the human face by its race, specifically from Chinese, Pakistani, and Russian citizens;
* The collected image dataset is a unique one in its kind and is publicly available upon request;
* Several HPC hardware accelerator architectures have been tested, such as FPGAs and GPUs;
* The proposed network was compared against four different pre-trained CNN models: ResNet50, DenseNet, AlexNet, and GoogleNet;
* The experimental results were enriched by reporting the power usage and execution time achieved by the proposed network in order to facilitate its future easy development as a mobile embedded system. The structure of the paper is as follows. Section 2 is devoted to introducing the background and fundamentals regarding the technologies used in this research. Next, Sections 3 and 4 are aimed at describing in detail the deployment of DL in FPGAs and the DL-based ethnicity identification proposed in this work, respectively. Section 5 is focused on the experimental results reported by our proposal and its competitors from the state-of-the-art (SoTA). A summary of the conducted study and some future research suggestions is finally introduced in Section 6.
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 3 From S-MIM to F-SIM
### F-SIM Key Implementation Aspects and Laboratory Test Results
The first use case (UC#1), named High Efficiency F-SIM, aims at showing the F-SIM performance in a typical GEO Ku band scenario with a SmartLNB terminal (0.5 W RF power, 75 cm dish). F-SIM waveforms Cr3840 Sf16 Ds38 and F-SIM Cr3840 Sf64 Ds38 [20] (i.e., with chip rate of \(R_{c}=3.84\) Mchip/s, data size equal to 38 bytes and two spreading factors SF = 16 and SF = 64) were simulated. Link budgets with typical Ku-band satellite ratings show a link margin at the center of coverage (that can be used for power spreading) of about 4 dB for the selected waveform when SF = 16 is employed with a minimum C/N = \(-\)14 dB, and of about 10 dB for SF = 64 with a minimum C/N = \(-\)20 dB. Thus, with SF = 64, power randomization of 9 dB is considered. Figures 4 and 5 show the throughput and PLR performance, respectively, also considering the case of phase noise at the terminal, with the phase noise (PN) mask as described in [20]. The number of SIC iterations was set to a high value (up to 32) to obtain the maximum performance, although for a realistic performance, it could be lower. Results are also summarized in Table 1. The increasedthroughput (factor 2 increase) achievable, passing from a spreading factor of 16 to 64, can be observed. We also remark that the phase noise effect on the performance is quite limited thanks to the robust gateway demodulator processing algorithms, introduced at the end of Section 2.2. Figure 4: UC#1: F-SIM throughput performance. Figure 5: UC#1: F-SIM PLR performance.
# A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
## 2 Materials and Methods
### Class Imbalance  
This study's samples were binary and categorized as either control or stress. Among the 207 samples introduced in Section 2.1, 32 were control and 175 were stress, indicating class-imbalanced data. In such cases, using standard classification methods could lead to bias toward the majority class, potentially increasing the misclassification rate in the minority class.  
To address this issue, an ML-based algorithm known as 'oversampling' was employed. The oversampling technique generates synthetic samples in the minority class to balance the dataset. The details on the principle of oversampling can be found elsewhere [20]. In the present work, the synthetic minority oversampling technique (SMOTE) [20] was applied in WEKA software (Machine Learning Group, University of Waikato, New Zealand) [21] to oversample the minority class. Nearest neighbors (K), specifying the number of nearest neighbors, and random seed value were kept as defaults (i.e., 5 and 1, respectively). The percentage parameter, which determines the amount of oversampling, was set to 400.
# Automated Vision-Based Crack Detection on Concrete Surfaces Using Deep Learning
###### Abstract  
Cracking in concrete structures affects performance and is a major durability problem. Cracks must be detected and repaired in time in order to maintain the reliability and performance of the structure. This study focuses on vision-based crack detection algorithms, based on deep convolutional neural networks that detect and classify cracks with higher classification rates by using transfer learning. The image dataset, consisting of two subsequent image classes (no-cracks and cracks), was trained by the AlexNet model. Transfer learning was applied to the AlexNet, including fine-tuning the weights of the architecture, replacing the classification layer for two output classes (no-cracks and cracks), and augmenting image datasets with random rotation angles. The fine-tuned AlexNet model was trained by stochastic gradient descent with momentum optimizer. The precision, recall, accuracy, and F1 metrics were used to evaluate the performance of the trained AlexNet model. The accuracy and loss obtained through the training process were 99.9% and 0.1% at the learning rate of 0.0001 and 6 epochs. The trained AlexNet model accurately predicted 1998/2000 and 3998/4000 validation and test images, which demonstrated the prediction accuracy of 99.9%. The trained model also achieved precision, recall, accuracy, and F1 scores of 0.99, respectively.  
crack detection; deep learning; convolutional neural networks; image processing; AlexNet network
## 4 Experiment Results and Discussion
### Experiment Results  
Three experiments on NSL-KDD and ISCX datasets are performed. These experiments are implemented on Windows 10 and used Python language.  
* The first experiment is to build SFSDT model to generate the best feature subset on both IDS datasets. The proposed model can generate the list of combination feature subsets. The best feature subset is selected based on the best score of accuracy and error.
* The second experiment is to detect types of attack in both datasets. This work builds three classifiers of various RNNs including conventional RNN, LSTM, and GRU. These approach models are learned on the best-selected feature subset by the proposed model. In NSL-KDD dataset, this task points the classification result of four attacks including ['R2L', 'DoS, 'U2R', 'Probe'] and none attack is ['Normal']. In the ISCX dataset, the detection result of two classes including ['Normal'] and ['Attack'] are presented. The classification results are evaluated based on confusion matrix and ROC.
* The final experiment is to measure memory profiles of the learning models including memory used and time executed in both cases. The first case is on the original feature set. The second case is on the selected feature subset.
## 2 Methodology
### Dataset Assessment
#### 2.1.1 Data Pre-Processing  
The dataset used in this study is balanced and does not contain any noisy or missing values. We used two preprocessing methods: normalization and standardization. The normalization process rescales the values to fall between zero and one [23]. We applied a power transformer scalar using the Yeo-Johnson transformation technique [24]. This approach transforms the data to fit a Gaussian distribution and handles zero, positive, and negative data values. Other power transformation methods, such as the Box-Cox transform, are applicable only for positive values. The standardization process rescales the sample data's feature values to provide a mean of zero and a standard deviation of one.  
Figure 2: Process to study the impact of dataset characteristics and model parameters on GPS spoofing detection systems.
# A SAM-Based Detection Method for the Distance Between Air-Craft Fire Detection Lines
## 2 Materials and Methods
### Loss Function
#### 2.6.2 Loss Function of the Target Task  
Since the target task is to calculate the minimum distance between the fire detection lines through the segmentation masks, the ratio of the width of the segmentation masks to the actual width of the fire detection lines is used as a reference during the calculation process. In order to prevent the situation where the mask prediction is good but the mask width and relative distance prediction are poor, the minimum distance between the fire detection lines is recorded during the process of collecting the custom dataset. During training, the minimum distance between the fire detection lines is calculated by using the predefined masks, and the difference between this calculated distance and the recorded true minimum distance is used as part of the total loss function to optimize the model's performance on the target task. Because the mask prediction in the early training stage is relatively rough, it is meaningless to introduce the target task loss function. Therefore, when the training is 50%, the target task loss function is introduced. The total loss function is  
\[Loss=\left\{\begin{array}{ll}Dice\ Loss&\mbox{ \emph{epoch} $<0.5 epoch$}\\ \lambda_{1}Dice\ Loss+\lambda_{2}Distance\ Loss&\mbox{ \emph{epoch} $\geq 0.5 epoch$}\end{array}\right. \tag{8}\]  
In this study, hyper-parameters \(\lambda_{1}\) and \(\lambda_{2}\) are set to 0.5. Although this method is demonstrated with the task of measuring the minimum distance between fire detection lines, it is of universal applicability. Additionally, Distance Loss can be adjusted or replaced to suit different target tasks. The calculation method for Distance Loss used in this paper is as follows.  
Curve width measurement is widely used in the field of traditional machine vision. Because this curve is usually an object with uneven width of road cracks, product scratches, or human tissue, it is generally necessary to identify the contour of the curve and then calculate it based on the contour. The width of the fire detection line is consistent. The algorithm can be appropriately simplified. Because the distance between the fire detection line is introduced into the training iteration process, the simplified algorithm also helps to reduce the computational overhead.  
The overall workflow of the proposed method is illustrated in Figure 3, including image acquisition, annotation and supervision preparation, LoRA-based SAM fine-tuning with fusion loss, segmentation inference, post-processing for distance calculation, and final evaluation.  
As shown in Figure 4, after denoising the mask image, uniform grid lines n \(\times\) n are drawn. The pixel lengths \(1=\{1_{1},1_{2},\ldots,1_{m}\}\) of the line segments where the grid lines intersect with the mask, the midpoint coordinates \(\mathrm{p}=\{\mathrm{p}_{1},\mathrm{P}_{2},\cdots,\mathrm{p}_{m}\}\), and the pixel distances \(\mathrm{r}=\{\mathrm{r}_{1},\mathrm{r}_{2},
# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 5 Experiment Results
### Scenarios Experiment Results
In the policy-based scaling, resources are allocated gradually (red dotted lines) without considering the QoS values. At the same time, ReSQoV allocates resources more rigorously and appropriately. The VM works with the new assigned PEs when needed. It is observed that QoS parameters are not considered in policy-based resource allocation. A maximum of four PEs are allocated even though twelve scalable PEs were allocated to the VM. The results show that ReSQoV focuses on the QoS, and after allocating the required resources by considering the parallelism and serialization overheads, it ensures QoS is maintained.
# Intelligent Control System to Irrigate Orchids Based on Visual Recognition and 3D Positioning
###### Abstract  
This work develops a novel automatic irrigation system to implement the customized and accurate watering for an individual seedling. The system integrates the modules of visual recognition of the stem-leaf junction, identification of the stem-root junction as the watering point, and control of the spraying nozzle. The model of YOLOv3 is employed to screen the stem-leave junction of an orchid seedling, whose depth map then acquired by the method of Semi-Global Block Matching (SGBM) extracts the three-dimensional (3D) coordinates of the junction center. Next, the concept of leaf vector is introduced to identify the stem-root junction of the orchid seedling as the accurate watering point, which the spraying nozzle is controlled to reach for supplement of the specific amount of water. A number of experiments were conducted to verify the proposed irrigation system for orchid seedlings at different locations with various heights. The experimental results show that the rates of successful watering are 82% and 83.3% for the uni-pot and multi-pot orchid seedlings, respectively.  
automatic irrigation system; orchid seedling; object detection; stereo vision; depth map +
Footnote †: journal: Applied  
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