esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Result Analysis
### Experimental Environment and Parameter Settings
#### 4.1.2 Evaluation Criteria  
For the detection task, the Pixel-wise True Positive Rate (_PTPR_) and Pixel-wise False Positive Rate (_PFPR_) are two important indicators, which reflect the coverage and judgment ability of the detection method to the object, respectively. Therefore, the above two pixel-wise evaluation indicators are used as quantitative performance evaluation criteria in this paper. _PTPR_ can be expressed as follows:  
\[PTPR=\frac{TP}{TP+FN}. \tag{11}\]  
Among them, _TP_ is the number of pixels correctly detected as obstacles; \(TP+FN\) is the pixel number of real obstacles. _PFPR_ can be expressed as follows:  
\[PFPR=\frac{FP}{FP+TN}. \tag{12}\]  
Among them, _FP_ is the number of pixels falsely detected as obstacles; \(FP+TN\) is the pixel number of real free spaces.  
In addition, the Receiver Operating Characteristic (ROC) curve is used to determine the optimal parameters of each detection method. The abscissa of the ROC curve is _PFPR_, and the ordinate is _PTPR_. By executing the detection method under different parameters, the detection results are mapped to a series of points in the plane, and the optimal parameters are determined according to the convex hull positions of the ROC curves.
# A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction
## 3 Materials and Methods
### Stroke Prediction Dataset  
The dataset was sourced from the famous benchmark dataset repository Kaggle [22]. We concentrated on participants over the age of 18 from this dataset. The dataset has 11 features and 5110 rows. Table 1 shows the descriptions for each feature, where the input to the ML is ten features, and one feature is applied for the target class.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Name** & **Description** & **Range** \\ \hline Gender & The gender of the participant is the focus of this feature. There & Male–Female \\  & are 1260 men and 1994 women in the population. & \\ \hline Age & Participants over the age of 18 are the subject of this feature. & Float \\ \hline Hypertension & This characteristic indicates whether this participant has & \(0\to\) No Hypertension \\  & hypertension. 12.54\% of participants have high blood pressure. & \(1\to\) Hypertension \\ \hline Heart Disease & If this individual has heart disease, it is indicated by this feature. & \(0\to\) No Heart Disease \\  & The participants’ prevalence of heart disease was 6.33\%. & \(1\to\) Heart Disease \\ \hline Ever Married & The number of participants who are married, which is 79.84\%, & Yes–No \\  & is represented by this feature. & \\ \hline Work Type & This feature has four categories: private, self-employed, government, and finally, never worked. & Never\_worked, Children, Private, Self-employed, or Govt\_job \\ \hline Residence type & This feature has two categories: urban and rural, representing & Urban or Rural \\  & the participant’s living situations. & \\ \hline Average glucose level & & \\ (mg/dL) & This feature tracks the participants’ average blood glucose level. & Float \\ \hline BMI (Kg/m\({}^{2}\)) & This feature records the participants’ BMI. & Float \\ \hline Smoking Status & There are three categories for this feature: smoker (22.37\%), never smoker (52.64\%), and formerly smoker (24.99\%). & Never smoked, smoked, or Formerly Smoked \\ \hline Stroke & This attribute identifies if the participant has had a stroke in the past. 5.53\% of participants have experienced a stroke. & \(0\to\) No Stroke \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stroke prediction dataset features.  
Most characteristics are categorical, except age, average blood glucose level, and BMI, which are considered numerical. The dataset includes 2994 females, 2115 males, and one other, and it includes 249 stroke patients and 4861 normal patients. The dataset was divided into a training set with 4088 rows (3901 no-stroke and 187 strokes) (80%) and a test set with 1022 rows (20%). Class 0 represents the no-stroke class in the dataset, and class 1 represents the stroke class.
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
## 4 Security Evaluation of the Proposed Methodology
### Informal Analysis
#### 4.1.7 Replay Attack (RA)  
The three entities use the random integers \(RND_{3}\), \(RND_{4}\), \(RND_{5}\), and \(RND_{6}\), together with the timestamps \(T_{S}N\), \(T_{T}A\), and \(T_{S}\), to construct the login messages \(MSG_{1}\) and \(MSG_{2}\), as well as the response messages \(MSG_{3}\) and \(MSG_{4}\). Owing to their recentness, \(SN\), \(TA\), and \(Ss\) can distinguish between the acquired and replayed communication, owing to the validity of random nonces and timestamps. As a result, the suggested method can thwart a RA.
## 2 Materials and Methods
### Methods
#### 2.3.2 Model Architecture Configuration  
In complex ML tasks, it is common to use networks that have been pre-trained on large datasets, such as ImageNet, and then adjust them to the new problem. To do this, techniques such as fine-tuning and Batch Normalisation are used, both of which are essential to avoid problems such as overfitting and instability during training [47; 48].  
Figure 7: Data augmentation pipeline that was used to expand the original dataset. From each operation, an arrow signals the number of generated images.  
Fine-tuning is a technique in which a model that has already been trained on a large dataset is adapted to a new, more specific one, while maintaining the general characteristics learnt previously. During this approach, parts of the original model are frozen, while other parts are adjusted for the new dataset [2]. In practice, the lower layers of the network are responsible for capturing generic visual features (such as edges and textures), which are useful in almost any computer vision task and can therefore be kept, i.e., they will be the so-called frozen layers. The higher layers of the network, on the other hand, learn more complex and specific patterns from the original dataset and need to be adjusted (unfrozen layers) to adapt to the characteristics of the DFUs [49; 50].  
In the case of VGG16 and VGG19, the first eight layers were frozen, while the rest were thawed. On the other hand, MobileNetV2, being a lighter and more compact network, uses a different approach. As the features learnt by the model in ImageNet may not be sufficient to capture the more subtle nuances of ulcers, all its layers were thawed to allow for complete fine-tuning.  
Batch Normalisation is a standard technique in deep neural networks, the main aim of which is to stabilise and speed up training. To do this, it normalises the activations of a layer according to the mean and standard deviation of the mini-batch, adjusting these values so that the network has more stable and well-scaled activations. By ensuring that the values are always controlled, it speeds up the convergence of the model and allows the use of higher learning rates, reducing the need for detailed manual adjustment of hyperparameters [48].  
In the VGG network model, Batch Normalisation was applied after the output of the convolutional base, stabilising the activations before moving on to the dense layers. This was especially useful for unfreezing the upper layers during fine-tuning, as it prevented changes in these layers from causing large variations in training. In the case of MobileNetV2, it was also applied just after the convolutional base was output, ensuring that the model maintains stability during training, especially when unfreezing all the layers.
# Multiscale Dynamic Attention and Hierarchical Spatial Aggregation for Few-Shot Object Detection
## 4 Experiments
### Ablation Study
#### 4.5.2 Results and Analysis  
The experimental settings and corresponding results are presented in Table 4. Each row reflects the effect of adding or removing specific components of the model on the AP75 on both datasets.  
**Using TFA + CBAM** Incorporating the original CBAM module into the baseline model resulted in an improvement over the baseline AP (PASCAL VOC: 57.2%, COCO: 13.9%). However, the performance remained below that of the fully enhanced MultiScale-DynaAttention module. This suggests that while CBAM enhances feature extraction through channel and spatial attention, it struggles to address the complexities of few-shot scenarios effectively, leaving significant room for improvement.  
**TFA + MultiScale-DynaAttention** Replacing the original CBAM with the MultiScale-DynaAttention module in the TFA model led to further performance gains (PASCAL VOC: 59.8%, COCO: 14.5%). The MultiScale-DynaAttention module enhances feature extraction by incorporating multiscale convolutions and dynamic pooling adjustments, but, without the additional contributions from the HAPP structure and dual-branch detection head, the performance improvement is limited.  
**TFA + HAPP and dual-branch detection head** Applying the HAPP structure and dual-branch detection head to the TFA model without the MultiScale-DynaAttention module achieved improvements over the baseline (PASCAL VOC: 60.3%, COCO: 14.2%). This configuration handled multiscale features effectively, demonstrating the importance of  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Ablation Setting** & **AP (VOC)** & **AP (COCO)** \\ \hline Baseline Model (TFA) & 56.0 & 12.0 \\ Baseline Model (MPSR) & 57.8 & 14.2 \\ TFA + CBAM & 57.2 & 13.9 \\ TFA + MultiScale-DynaAttention & 59.8 & 14.5 \\ TFA + HAPP and dual-branch detection head & 60.3 & 14.2 \\ MDA-HAPP & 65.8 & 15.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: AP75 results under different ablation settings.  
HAPP and the dual-branch detection head. However, the absence of enhanced attention mechanisms resulted in slightly lower performance compared to the complete model.  
**Using the fully enhanced model** The fully enhanced model, combining MultiScale-DynaAttention, HAPP, and the dual-branch detection head, achieved the best performance (PASCAL VOC: 65.8%, COCO: 15.7%). This represented an absolute improvement of 9.8% on VOC and 3.7% on COCO compared to the TFA baseline. These results confirm that the synergy of all proposed improvements is essential for achieving optimal performance in FSOD.
## 3 Prototype Specification
Transmission signals for 52 subcarriers are superposed through an 8192 point inverse FFT (IFFT) and then the overlap-add method [24] is applied to obtain the time domain continuous waveform. The multicarrier signals are transmitted after being up-converted to the intermediate frequency (IF). Resolution of the digital-to-analog (D/A) converter is 16 bits. Figure 5 then shows a block diagram of the receiver with BAA. Dynamic range of the receiver is 82 dB and resolution of the analog-to-digital (A/D) converter is 12 bits. Each receiver port extracts the signals received on each subcarrier through the overlapped FFT. Extracted subcarriers are individually RRC filtered in the frequency domain and converted to the time domain symbols via 8192 point IFFTs. Each symbol is downsampled to the 50 kHz symbol rate. It is also composed of four sample points. BAA weight for each subcarrier is calculated using the downsampled (but oversampled) symbols according to the pre-assigned subcarrier level (i.e., high or low), and then multiplied to the received signals. CMA (for high-level subcarriers) and PI-CMA (for low-level subcarriers) are implemented as BAA algorithms. After BAA is applied, symbol timing is detected using the unique word (UW) after carrier frequency recovery using a Costas loop [25]. It is performed per subcarrier and the prototype receiver utilizes a successfully estimated one. Finally, decimated symbols are demodulated and decoded to information bits as output after parallel-to-serial (P/S) conversion. Figure 4: Transmitter employing STPA. S/P: serial-to-parallel; RRC: root raised cosine; IFFT: inverse fast Fourier transform; IF: intermediate frequency; D/A: digital-to-analog. Figure 5: Receiver employing BAA. A/D: analog-to-digital; P/S: parallel-to-serial.
## 3 Results and Discussion
### Optical Diagnosis of the Plasma Source
#### 3.4.1 VUV-Radiance Measurements  
The emission spectra in the 110-200 nm range using a VUV scanning monochromator are presented in Figure 9. A set of atomic lines of hydrogen (121.5 nm), oxygen (130.5 nm) and nitrogen (149.5 nm and 174.5 nm) is detected, emitting powerful VUV radiation with energies from 7 eV to 9.5 eV. In addition, the continuous profile of the molecular argon excimer is detected for the Ar-CAP from 110 nm up to 140 nm. These species are of interest due to their energy that can be transferred to the surface under treatment. These values of dissociation energies under consideration are between 4.3 eV (for atomic H and Ar) to 9.8 eV (for atomic N and O) [78, 79]. It has to be noted that the helium excimer is located below the detection limit of the applied system and hence no information on it can be stated here [37].  
When comparing the intensities of He-CAP and Ar-CAP, a much more intense VUV emission in argon could be found. While Ar-CAP radiance could be quantified to reach up to 350 mW/(nm mm\({}^{2}\) sr), no radiance level could be determined for He-CAP. The VUV radiance results present another indication that the RONS production is better observed for the Ar-CAP.
## 3 Static Load Tests
### Loading Conditions and Instruments  
During the tests, each specimen was loaded at the preset displacement rate (0.6 min/min) on a 5 MN pressure testing machine (PTM, Shandong Jianzhu university, Jinan, China) with a servo hydraulic controller. Besides, two linear variable displacement transformers (LVDTs) were symmetrically fixed on the left and right sides of the specimen. The relative displacement of each connecting plate along the direction of contact surfaces was recorded by a TDS-530 data logger. Figure 4 shows the positions of the LVDTs on MC-45 specimens.  
As shown in Figure 5, the bolt force sensor (BFS) was manufactured in the following steps: First, a blind hole (diameter: 2 mm; depth: 28 mm) was drilled at the center of hexagon bolt head. After the hole was cleaned up, a strain gauge (KFB-3-120-C20-11, Kyowa electronic Instruments, (Shanghai) Trading Co., Ltd., Shanghai, China) was embedded into the hole and fixed with glue; Next, the BFS was calibrated by the tensile testing machine and TDS-530 data logger (Shandong Jianzhu university, Jinan, China). The strain that corresponds to the target clamping force of the BFS was calculated through linear regression [28]. Here, the clamping force of the bolt was applied in turn to 50%, 100% and 110% of the design value (\(P=155\) kN) [27]. The test setup is illustrated in Figure 6 below.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Bolt**} & \multicolumn{3}{c}{**Nut**} & **Washer** \\ \hline Grade & Yield strength/MPa & Tensile strength/MPa & Elongation/\% & Reduction ratio in area/\% & Minimum tensile load/kN & Grade & Proof load/kN & Grade \\ \hline
10.9 & \(\geq\)900 & 1040 to 1240 & \(\geq\)14 & \(\geq\)40 & 245 & 10 H & 245 & HRC-45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The material properties of 10.9 S HSBs.  
Figure 4: The positions of the linear variable displacement transformers (LVDTs) on MC-45 specimens.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Specimen** \\ **ID** \\ \end{tabular} } & \multirow{2}{*}{\(\mathbf{\theta}/^{\circ}\)} & \multirow{2}{*}{\(\mathbf{\lambda}\)} & **Surface** & **Target** & **Connecting** & **Pressure Plate/mm** & **Pressure Plate/mm** \\  & & & **Treatment** & **Clamping** & **Force /kN** & \(\mathbf{l\times w\times t_{1}}\)** & \(\mathbf{w\times a\times b\times c\times t_{2}}\) \\ \hline NMC-35 & 35 & 0.70 & Butter & & & & \(170\times 130\times 116\times 30\times 40\) \\ MC-35-01–06 & 35 & 0.70 & Sand blasting & & & & \(170\times 130\times 116\times 30\times 40\) \\ NMC-45 & 45 & 1.00 & Butter & & & \(170\times 153\times 92\times 30\times 40\) \\ MC-45-01–06 & 45 & 1.00 & Sand blasting & & & & \(170\times 153\times 92\times 30\times 40\) \\ NMC-60 & 60 & 1.73 & Butter & & & \(170\times 181\times 50\times 30\times 40\) \\ MC-60-01–06 & 60 & 1.73 & Sand blasting & & & \(170\times 181\times 50\times 30\times 40\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters of specimens.
# A Dual-Polarized and Broadband Multiple-Antenna System for 5G Cellular Communications
## 5 SAR Evaluation  
The Specific Absorption Rate (SAR) measures the energy absorbed by the human body upon exposure to radio frequency (RF) electromagnetic fields. It is presented in watts per kilogram (W/kg), and it indicates the rate at which energy is absorbed by tissue. It is a critical metric for ensuring that electronic devices, particularly mobile phones, comply with safety standards to minimize potential health risks [58]. SAR values are influenced by device design, usage scenarios, and environmental conditions. Regulatory bodies like the FCC and ICNIRP have established SAR limits to protect public health, typically set at 2 W/kg, to prevent excessive energy absorption that can lead to tissue heating [59]. The SAR distribution for the selected antenna elements in talk-mode scenarios has been analyzed and is illustrated in Figure 16. This figure highlights the antenna elements with both the highest and lowest SAR levels observed in the scenarios. Specifically, Figure 16 reveals that, during talk mode, Antenna 2 exhibits the highest SAR level, whereas Antenna 7 shows the lowest SAR value. This variation is linked to the arrangement of the antenna array as follows: Antenna 2 is positioned closer to the hand phantom compared to Antenna 7. Consequently, it can be inferred that a shorter distance between the antennas and the phantom results in an elevated SAR level, while a greater distance corresponds to a lower SAR level.  
Figure 15: Measured/simulated comparison for (**a**) ECC and (**b**) TARC.
# Processing of Single-Walled Carbon Nanotubes with Femtosecond Laser Pulses
## 1 Introduction
[PERSON] and coworkers ablated dense SWCNT based bulk materials at two wavelengths, with single and multi-shot laser exposure. They reported less damage with shallow material removal, and high damage demonstrated by micro craters by single and multi-shot laser irradiation, respectively [27]. On the other hand, [PERSON] et al. [28] studied the selective removal of a specific category of SWCNT thin film by resonant ablation through femtosecond pulses. Most recently, the transformation of SWCNT structures into other allotropic carbon structures with effective coalescence through femtosecond laser irradiation was reported by [PERSON] et al. [2]. Despite the enormous potential, the joining of SWCNTs has been a less focused area, and no reports were published that focused on the joining of SWCNT networks in the form of thin film by processing via femtosecond irradiation, to the best of our knowledge. There are several benefits of using the femtosecond laser technique. The irradiation of the femtosecond laser generally has an ultrafast impact that results in non-thermal melting of solids, and thereby is promising for the joining of nanostructures. For instance, the short pulse duration delivers localized high peak power through the laser spot, thus minimizing the surrounding heat diffusion and thermal damages [29; 30]. Furthermore, the ultrashort time scale of the femtosecond laser thus non-thermally induces the changes in the structure of SWCNTs, and thereby transforms it into a different allotropic form [31; 32]. Thus, the unique properties of the femtosecond laser could be beneficial for joining the amazing one-dimensional material of SWCNTs through femtosecond laser irradiation. Therefore, in this work, a simple and effective strategy has been employed to join the SWCNTs by femtosecond laser irradiation for potential apparitions as highly conducting interconnects. Detailed scanning electron microscopy (SEM) was performed to analyze the ablated nanotubes, and the effect of laser processing parameters on joining/burning of SWCNTs.
# Deep Learning-Based Flap Detection System Using Thermographic Images in Plastic Surgery
###### Abstract  
In reconstructive surgery, flaps are the cornerstone for repairing tissue defects, but postoperative monitoring of their viability remains a challenge. Among the imagistic techniques for monitoring flaps, the thermal camera has demonstrated its value as an efficient indirect method that is easy to use and easy to integrate into clinical practice. This provides a narrow color spectrum image that is amenable to the development of an artificial neural network in the context of current technological progress. In the present study, we introduce a novel attention-enhanced recurrent residual U-Net (AER2U-Net) model that is able to accurately segment flaps on thermographic images. This model was trained on a uniquely generated database of thermographic images obtained by monitoring 40 patients who required flap surgery. We compared the proposed AER2U-Net with several state-of-the-art neural networks used for multi-modal segmentation of medical images, all of which are based on the U-Net architecture (U-Net, R2U-Net, AttU-Net). Experimental results demonstrate that our model (AER2U-Net) achieves significantly better performance on our unique dataset compared to these existing U-Net variants, showing an accuracy of 0.87. This deep learning-based algorithm offers a non-invasive and precise method to monitor flap vitality and detect postoperative complications early, with further refinement needed to enhance its clinical applicability and effectiveness.  
surgical flaps; thermographic image; deep learning; image segmentation 2024  
2
# A Multi-Task Learning and Multi-Branch Network for DR and DME Joint Grading
## 3 Proposed Method
### Feature Fusion Module
#### 3.2.3 Atrous Spatial Pyramid Pooling Module  
ASPP first appeared in the semantic segmentation network Deeplab [38], which can extract global features of the input images through dilated convolution with different expansion rates. On the one hand, the discriminative features of DR grading are hemorrhage, hard exudate, soft exudate, etc., which are distributed in various locations of retinal images. It is necessary to consider the global nature when extracting lesion features. On the other hand, larger dilation rates would lead to the loss of smaller lesion features, which is unfavorable to DR grading. So, to get better grading performance, the modified ASPP is designed in this paper to extract the global context information. The schematic diagram of the modified ASPP module is shown in Figure 6.  
As seen in Figure 6, the modified ASPP has a five-branch architecture. The underlying features \(F_{DR}\) are firstly processed by the dilated convolution with different expansion rates  
Figure 5: CFEN Module. It is the detailed structure of the CFEN module for DME classification, which consists of two branches.  
Figure 6: Schematic diagram of the modified ASPP structure. It consists of five branches, and the expansion rates of these five branches are 1, 3, 5, 7, and 7 respectively.  
to obtain the image feature maps with different receptive fields. The expansion rates of these five branches are 1, 3, 5, 7, and 7 respectively. The size of the convolution kernels of these five branches is 1 \(\times\) 1, 3 \(\times\) 3, 3 \(\times\) 3, 3 \(\times\) 3 and 3 \(\times\) 3. Then, for the fifth branch, we apply the average pooling and 1 \(\times\) 1 operations to generate the feature map. Finally, these five feature maps are concatenated together and a 1 \(\times\) 1 convolutional operation is used to obtain the output feature \(G_{DR}\).