esa-sceva/satcom-chunk-collection huggingface dataset:
# The Extreme Function Theory for Damage Detection: An Application to Civil and Aerospace Structures
## 6 Conclusions  
This study investigated the validity of EFT as a framework for mode shape-based SHM in 2-dimensional plate-like and frame structures, which are representative of common applications in Aerospace and Civil Engineering, respectively. The rationale is that damage is a rare occurrence and therefore the data-driven models defined over the undamaged conditions must take this consideration into account. Experimental and numerical data were utilised to this aim for different damage locations and severity levels. The robustness of the procedure to artificially added white Gaussian noise has been methodically studied and the results have been benchmarked against the results of the same algorithm trained with pointwise EVT values, showing a statistically relevant decrease of the number of false positives. Moreover, the use of EFT allows to compare points where the data were not directly collected; furthermore, it assigns only one possible outcome (\"normal\" or \"abnormal\") to the whole function. On the other hand, the single components of the mode shape, if taken one by one as in the EVT, could be under the threshold at some modal coordinates and over it at other locations for the same identification.  
This study provides a strong foundation for future works in the field of EFT and EVT for structural health monitoring purposes. It will be important to validate the proposed approach in situ on real-life civil structures. Another related field of research, which the Authors are intended to investigate deeper in the next future, regards the scarcity of data from damaged structures and the need to compensate with numerically simulated data.  
Conceptualization, M.C. and C.S.; Data curation, D.M. and M.C.; Methodology, D.M., M.C., and C.S.; Project administration, C.S.; Resources, C.S.; Software, D.M. and M.C.; Supervision, M.C. and C.S.; Validation, D.M. and M.C.; Visualization, D.M. and M.C.; Writing--original draft, D.M. and M.C.; Writing--review and editing, C.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable.  
The data presented in Section 3 are available on request from the corresponding author.  
The Authors would like to thank [PERSON] from the University of Sheffield for his precious help and advice and LANL for the experimental dataset.  
The Authors declare no conflict of interest.
## 3 Experimental Analysis
### Comparison of Test Results
Although both of the above models introduce the CBAM attention mechanism, the YOLOv5 algorithm does not perform as well as YOLOv7 in terms of both model parameters and detection performance. Obviously, based on the YOLOv7 algorithm, the improved GCB YOLOv7 algorithm has lower parameter counts and floating-point operations, and the model size is smaller, which is suitable for the future needs of intelligent harvesting equipment. In addition, the real-time performance surpasses the YOLOv5 and YOLOv8 algorithms, reaching 100.00 FPS. As we can see in Table 3, the accuracy of the GCB YOLOv7 model can meet the needs of detection, while the real-time performance also meets the requirements of target detection. Therefore, the improved YOLOv7 model is suitable for the maturation of Hemercollis citrina Baroni. In summary, the improved GCB YOLOv7 algorithm has obvious advantages in being lightweight, which reduces the model computation, compresses the model volume, and simplifies the model structure. At the same time, detection precision, recall, average precision, and harmonic mean performance are all improved to different degrees. In addition, the detection efficiency also meets the requirements of real-time performance. It can be seen that the GCB YOLOv7 algorithm proposed in this paper can meet the requirements of Hemercollis citrina Baroni maturity detection [31]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **Parameters/Millions** & **Flops/G** & **Model Volume/M** & **[EMAIL_ADDRESS]** & **R** &
\begin{tabular}{c} **Real Time** \\ **Performance/FPS** \\ \end{tabular} \\ \hline YOLOv5 & 46.14 & 107.9 & 92.7 & 89.035\% & 85.7\% & 64.16 \\ CBAM-YOLOv5 [29] & 44.52 & 104.1 & 90.5 & 65.191\% & 68.55\% & 71.72 \\ YOLO-CBAM [30] & 46.73 & 110.6 & 93.7 & 73.652\% & 74.63\% & 83.29 \\ YOLOv7 & 37.20 & 104.8 & 74.8 & 78.399\% & 77.08\% & 119.05 \\ YOLOv8 & 43.62 & 165.4 & 87.7 & 82.735\% & 65.73\% & 80.75 \\ GCB YOLOv7 & 35.17 & 97.5 & 70.8 & 79.782\% & 77.69\% & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of experimental results.
## 4 Dynamic Modeling and Test Verification
### Steady Static Circular Test Verification  
Based on the standard GB/T6323.6-1994, the steady static circular tests were conducted. The specific test method is as follows: firstly, fix the steering wheel angle and the starting circle radius is 15.0 m; then, accelerate slowly at the longitudinal acceleration below 0.25 m/s\({}^{2}\) until the lateral acceleration reaches 6.5 m/s\({}^{2}\). Based on the two truck dynamic models, the steady static circular test were carried out, respectively. The simulation settings are the same as the real test conditions. A comparison of the curves of the body roll angle versus the lateral acceleration is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline The unsprung mass for the front wheel /(kg) & 46.4 & The differential ratio & 4.778 \\ The front axle load /(kg) & 884 & The mass of the drive shaft /(kg) & 8.3 \\ The rear axle load /(kg) & 1641 & The arm length of the stabilizer /(mm) & 204 \\ The wheeles/(m) & 2.6 & The bar length of the stabilizer /(mm) & 910 \\ The front tread /(m) & 1.32 & The front wheel radius /(m) & 0.297 \\ The rear tread /(m) & 1.41 & The rear wheel radius /(m) & 0.297 \\ The stiffness of the front suspension /(N/mm) & 55 & The front wheel stiffness /(kNm\({}^{-1}\)) & 383.4 \\ The unsprung mass for the rear wheel /(kg) & 83 & The rear wheel stiffness /(kNm\({}^{-1}\)) & 383.4 \\ The angular ratio of steering gear & 18.0 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters values of the light truck.  
Figure 7: The light truck models: (**a**) The geometric model, (**b**) the dynamic model, (**c**) the sub-model of the stabilizer system, and (**d**) the sub-model of the rear axle with dampers.  
and \(\alpha_{2}\) are the absolute values of the sideslip angles of the front axle and the rear axle, respectively. In order to facilitate the test and analysis, researchers usually choose evaluation indicators to describe and evaluate the steady-state response of the vehicle according to their own habits. \(\alpha_{1}-\alpha_{2}\) is one of the most important evaluation indicators. If \(\alpha_{1}-\alpha_{2}>0\), the vehicle has under-steer characteristics; if \(\alpha_{1}-\alpha_{2}=0\), the vehicle has neutral steering characteristics; If \(\alpha_{1}-\alpha_{2}<0\), the vehicle has over-steer characteristics [43]. A comparison of the curves of \(\alpha_{1}-\alpha_{2}\) versus the lateral acceleration is shown in Figure 9. From Figure 9, it can be seen that the simulation results are close to the test results, which proves that the two truck models can reproduce the handling dynamic responses. Figure 9 also shows that the truck has slight over-steer characteristics.
## 2 Materials and Methods
### Methodology  
Figure 2 presents a flowchart of the methodology for this study. Each step is described in the following sections:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Satellite** & **Provider** & **Sensor** & **Polarization/Band** & **Resolution (m)** & **Wavelength** & **Mode** \\ \hline \multirow{3}{*}{Sentinel-1} & \multirow{3}{*}{ESA} & C-band & \multirow{3}{*}{VH} & \multirow{3}{*}{10} & \multirow{3}{*}{5 cm} & \multirow{3}{*}{IV} \\  & & SAR & & & B4 (Red) & & \\ \cline{1-1} \cline{5-6} Sentinel-2A and B & & & MSI & B8 (NIR) & 10 & 842 nm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic of the satellite data used in this study.
# High-Speed Visible Light Communications: Enabling Technologies and State of the Art
## 3 Enabling Technologies for a High-Speed VLC System
### Blue Filter  
Blue filter is one of the effective ways to improve the data rate and transmission distance of a ps-LED based VLC system [50; 51] without increasing the computational complexity at the receiver. And these improvements mainly come from the following two aspects.  
(1) Improving the modulation bandwidth of LED. As mentioned above, the modulation bandwidth of ps-LED is limited by the slow relaxation time of the phosphor coating. As shown in Figure 10, the emission spectrum of the phosphor-based white LED consists of a sharp blue peak centered at 456 nm and a fluorescence peak from 485 to 800 nm. Thus, one of the most effective ways to improve the modulation bandwidth of a phosphor-based LED transmitter is simply removing the slow fluorescence component from the modulated signal with a blue filter [2]. And the study of [PERSON] et al. shows that by employing a blue filter in front of the receiver, the bandwidth of ps-LED can be increased substantially from 2 up to 20 MHz [52].  
(2) Restraining the ambient light. For the silicon PIN or APD photodetector, which is the common receivers of a VLC system, the response spectrum ranges from about 400 to 1050 nm and has a much higher responsivity in the visible and near-infrared photons than the blue ones (see Figure 10). Therefore, sunlight or light from other artificial lighting, such as fluorescent lamps, computer or television monitors, becomes an inevitable strong ambient light that may greatly lower the SNR and limit the transmission distance of a VLC system [2]. By employing a blue filter, it is expected such ambient light interference might be minimized effectively.  
In 2015, the theoretical study of [PERSON] et al. [53] indicated that blue filtering had a marginal effect on the performance in ps-LED based VLC for all the investigated modulation formats, including PAM, CAP and DMT, only except for the case of PAM with a decision feedback equalizer (a 1-2 dB higher margin was observed without blue filtering). [PERSON] et al. [2] proposed a high-performance blue filter (see Figure 10) with ultrawide stopband from 500 to 1050 nm, and precisely restraining the phosphorescent component and other ambient light. As shown in Figure 11, the experimental results demonstrated that both the data rate and the transmission distance were greatly improved. [PERSON] et al. [54] has applied this kind of wide stopband blue filter in a high speed VLC system. A data rate of 1.6 Gb/s was successfully achieved over a 1 m free-space transmission with a PIN photodiode using 16 QAM-OFDM modulation.
# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 3 Methodology
### Feature Extraction  
In ASR systems, we extract a feature set from the speech signals. The classification algorithm is performed on the features set instead of speech signals directly. Feature extraction provides a compact representation of speech waveforms. Classification-based feature extraction reduces redundancy and removes the irrelevant information in large datasets [40]. A large dataset requires huge memory and computation power and leads to over-fitting.  
CNN extracts feature autonomously and converts the raw audio data into mel-spectrogram [41]. We have done this conversion only for CNN (DCNN and AlexNet) as it takes an input image, processes it, and then classifies it in different categories. CNN extracts and filters an enormous number of features to get useful features for the classification of the audio alphabet. In this work, we are using filtered features, from the FC layer.  
On the other hand, BLSTM needs assistance for extracting features. In the BLSTM network, we extract the information of the given dataset using spectral features from the raw audio data. The extracted data are stored and later given as input to the BLSTM network for training, testing, and evaluation of audio alphabet [42]. First, we use mel-spectrum with BLSTM, but the results were not promising, so we opted toward handcrafted features. We extract 12 spectral features from the raw data including spectral centroid, spectral spread, spectral skewness, spectral kurtosis, spectral entropy, spectral flatness, spectral crest, spectral flux, spectral slope, spectral decrease, spectral roll-off point, and pitch. These features are widely used in machine learning, deep learning applications, and perceptual analysis. We are using these features to differentiate notes, pitch, rhythm, and melody of speech.
## 5 Conclusions  
In this paper, we have proposed a high linear and compact four-stack power amplifier implemented using 45 nm CMOS SOI technology. The operating bandwidth of the PA ranges from 20 GHz to 30 GHz. A maximum saturated output power of 20.5 dBm along with a maximum peak power added efficiency of 29 % and an output 1 dB compression point of as high as 18.5 dBm was achieved at 24 GHz, and a maximum gain of 13.7 dB was achieved at 26 GHz. The design is compact and the active area is 0.079 mm\({}^{2}\), which is very small. The specified ACLR limit of \(-\)28 dBc was measured at 13.5 dBm of the average channel power using a 100 MHz 16-QAM 3 GPP/NR OFDM signal at 26 GHz, while the EVM and PAE were measured to be 9 % and 15 %, respectively. With these results, the proposed integrated mmWave four-stack power amplifier meets the specifications for 3 GPP/NR FR2 band n258. The best performance overall in terms of EVM, PAE and ACLR was achieved with the lowest bias current at the center of band n258; i.e., 26 GHz. The proposed PA shows excellent linearity in terms of the high 1 dB compression point, high gain and small size when compared against the state-of-the-art approaches.  
Conceptualization, J.P.A. and T.R.; methodology, J.P.A.; measurements, J.P.A., A.S. software, A.S.; validation, J.P.A., A.S., M.H. and J.R.; formal analysis, J.P.A. and T.R.; writing--original draft preparation, J.P.A.; writing--review and editing, J.P.A., A.S., M.H., J.R., T.R., A.P.; visualization, J.P.A.; supervision, T.R. and A.P.; project administration, J.P.A., T.R. and A.P.; funding acquisition, J.P.A., T.R. and A.P. All authors have read and agreed to the published version of the manuscript. This research has been financially supported Academy of Finland research projects MIMEPA (grant 323779) and 6 Genesis Flagship (grant 318927).  
Reported data is not publicly available.  
The authors would also like to acknowledge Global Foundries for the silicon processing and technical support.  
The authors declare no conflict of interest.
## 5 Experiments and Results  
### Implementation Details  
The proposed model was trained and tested on an NVIDIA RTX 3090 GPU using the PyTorch 3.7 deep learning framework, enabling efficient and accelerated computation. The Adam optimizer was employed with an initial learning rate of 0.005 and a weight decay of \(10^{-2}\). A batch size of 35 was utilized to balance memory efficiency and model convergence. The training was conducted over 500 epochs to ensure thorough learning and optimal parameter adjustment.  
To enhance the adaptability of the learning rate during training, a StepLR scheduler was applied with a step size of 9 and a decay factor (\(\gamma\)) of 0.95. This scheduling strategy gradually reduced the learning rate, aiding the model in achieving finer convergence and mitigating potential oscillations around optimal solutions. The learning rate scheduler dynamically adjusted the learning rate at regular intervals, ensuring stability in the optimization process and facilitating effective convergence.
## 3 Results and Discussion
### Comparative Analysis
Figure 23: The measured and simulated CCL for mmWave MIMO configuration: (**a**) simulated CCL; (**b**) measured CCL. After the analysis, it was observed that the size of our proposed patch for mid-band and mmWave antennas is comparatively smaller than that of the other reported antennas [18; 19; 20; 21; 22; 23]. In addition to that, the proposed antenna is operational for the 4G LTE, lower 5G and mmWave 5G applications. The proposed design covers the complete sub-6 GHz (5G band) and 24.25-27.5 GHz mmWave band. It was also found that our proposed design scheme has the highest (27.9 dB) isolation in mid-band MIMO case, as well as in mmWave 2D planner antenna case (29 dB). In addition to that, the proposed design has desirable MIMO characteristics (all within allowed limits). Therefore, our proposed integrated antenna configuration can be considered as a good candidate for the reported applications.
## 3 Short-Reach Systems Classification and Enabling Technologies
### Short-Reach Systems' Overview and Classification  
This subsection focuses on the overview and classification of the short-reach systems. They can be grouped in line with the associated technological features and limiting factors. A typical optical communication scenario is illustrated in Figure 2.
## 2 Related Works
### Attention Mechanism  
The attention mechanism assigns different weights to the pixel according to the spatial or channel relationship between the pixels in the feature map to enhance the representation of the feature, and it mainly includes three categories: a channel attention mechanism (e.g., an SE Block [42] and ECA Block [43]), spatial attention mechanism (e.g., a CA Block [44]) and hybrid attention mechanism (e.g., a CBAM Block [45]). The attention mechanism can improve the detection accuracy in NSIs with a few parameters and computation burden increase for detectors. The SE Block squeezes and then extends channel information through two full connection layers in order to learn the relationship of global channel information and effectively improve the detection performance, but the relationship between local channel information is not considered. The ECA Block learns the relationship between local channels through 1-D convolution with an adaptive convolution kernel, which promotes the detection performance but ignores the relationship of global channel information. In the CA Block, the information is extracted by average pooling in horizontal and vertical directions, respectively, and then concatenated and fused by 2-D convolution. The fused information is split into two parts and each part is further extracted by the convolution layer, respectively. The hybrid attention mechanism CBAM Block combines the channel and spatial attention mechanism. Both the CA Block and CBAM Block bring an obvious improvement in the detection accuracy in the natural scene dataset, but their complex structure increase the inference time. Meanwhile, it is difficult for them to use a few parameters to extract the spatial information of ORSIs due to their more complex background and less spatial feature information for detected objects in ORSIs.  
In order to more efficiently highlight the feature related to the detection task in ORSIs with a better robustness, a novel channel attention mechanism named a DCA Block was proposed to enhance the representation of the object feature in ORSIs through combing global and local channel information with a slight inference time increase.
## 2 System Composition and Protocol Design  
This protocol is mainly intended to solve two problems in IoMT. One is to ensure the QoS of high-priority patients, the fairness of low-priority patients is often sacrificed, such as patients with chronic diseases, and patients in remote rural areas have not received attention. Another problem is the shortage of spectrum resources as medical equipment increases.  
The protocol proposed in this paper is based on a single Basic Service Set (BSS) in IoMT. As shown in Figure 1, the AP is located in the geometric center of the BSS, each patient wearing the sensor acts as an STA, and the STA distributes uniformly in the coverage area of the AP. There are \(n\) patients in total, and we divide the patients into two categories: \(n_{1}\) critical patients and \(n_{2}\) mild patients. This paper uses the OFDMA centralized partitioning method to reduce the difficulty of channel estimation, which divides the entire channel into \(r\) subchannels. Each subchannel is composed of a set of consecutive subcarriers. In addition, this is a CR network in overlay mode, so STAs have cognitive ability. Because the uplink data transmission is multi-to-one, the MAC protocol design is complicated. Therefore, this paper focuses on uplink data transmission.  
The protocol in this paper is designed to guarantee both the QoS of critical patients and the fairness of mild patients and maximize spectrum resource utilization. The main idea of the protocol is shown in Figure 2. Firstly, mild patients and critical patients compete fairly for subchannels, and then the patients who succeed in the competition transmit packets on the assigned subchannel, which guarantees the fairness of mild patients. Secondly, the critical patients who fail in competition transmit packets on the randomly chosen remaining subchannels, guaranteeing QoS for the critical patients. Finally, the critical and mild patients who succeed in the competition and the critical patients who transmit packets on the remaining subchannels are collectively named PUs, and the mild patients who fail in the competition are named SUs. The SUs transmit packets while waiting for feedback and the next round of the backoff phase of the PUs. Thus, spectrum resource utilization is improved.  
The protocol is designed according to the following two parts: PU channel access and data transmission and SU channel access and data transmission.
## 3 Results and Discussion  
### Color Parameter Distribution  
The color parameters were manually extracted, as shown in Figure 3. The regions of the brilliance, fire, body color, and darkness are represented by \"L, H, B, D\", respectively. The horizontal axis was represented by \"Sample Category-Color Region-Color Parameter\". For example, \"LBR-L-L\" represents the lightness (**L**) in the brilliance region of the oval-shaped, faceted sapphires.  
Among the various color parameters, the lightness value of the brilliance region was the highest, followed by \"fire\", then body color, and darkness. The \"fire\" region had the highest **a** value, followed by the body color, the darkness, and the brilliance. The chroma (**C**) and absolute values of **b** in the \"fire\" region were the highest, followed by body color, with the darkness and brilliance having similar absolute values of **b** and **C**. In the CIE 1976 L* a* b* uniform color space, there were three coordinate axes, including the L* axis, a* axis, and b* a* axis. The L* axis represents lightness, with the black at the bottom end corresponding to a lightness of L* = 0 and the white at the top end corresponding to a lightness of L* = 100. The a* axis and b* axis together represent color characteristics, with the positive direction of the a* axis representing variations toward red (magenta), the  
Figure 3: Distribution of the color parameters for the manually extracted colors of sapphires. (**a**) A box plot of the lightness for each region of the oval- and pear-shaped sapphires; (**b**) a box plot of the color parameter **a** for each region of the oval- and pear-shaped sapphires; (**c**) a box plot of the color parameter **b** for each region of the oval- and pear-shaped sapphires; and (**d**) a box plot of the chroma for each region of the oval- and pear-shaped sapphires.  
negative direction representing variations toward green, the positive direction of the b* axis representing variations toward yellow, and the negative direction representing variations toward blue.