esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results and Analysis
### Pressure Pulsation Analysis  
The designed speed of the impeller in the mixed-transport pump was 3500 rpm, and its frequency was F0 = 58.33 Hz. The frequency domain diagram of the pressure fluctuation of the impeller passage monitoring locations IMS1-IMS5 and DPS1-DPS5 in the diffuser passage under different IGVF working conditions is shown in Figure 8. When the IGVF was different, the change in the pressure pulsation amplitude at respective monitoring point was different. Given the work done by the impeller, the pressure increased from the impeller inlet to the diffuser outlet gradually. The main frequency amplitude of the pressure pulsation in the diffuser was greater than that in the impeller, and the pressure pulsation amplitude reached the maximum value at the impeller blade head and the middle position of the diffuser passage, which was related to the gas phase aggregation. This result is consistent with the results of gas phase distribution in the impeller. Comparison of the pressure pulsation frequency domain graphs under different gas content conditions was undertaken, and the results indicate that the main frequency amplitude increased with an increased gas content. Furthermore, the amplitudes of IMS1, IMS5, DPS1, and DPS5 were higher than other points due to the uneven distribution of the phase state and the flow impact caused by the interference between the rotating part (impeller outlet) and the stationary part (diffuser inlet). In most cases, the dominant frequency of the impeller was 583.3 Hz, and was affected by the diffuser vanes number, whose value is the product of the rotation frequency and number of guide vanes. The same situation happened in the diffuser, where the dominant frequency value was 408.3 Hz and was seven times of the rotation frequency, which is a multiple equal to the number of impeller blades. Therefore, we showed that the dominant frequency of the pressure pulsation in the diffuser and impeller was affected by the number of blades in each and was an integer multiple of the rotation frequency.  
Figure 7: Pressure distribution at diverse spans under IGVF = 20%.  
Figure 8: _Cont_.
## 4 Experiment and Result Analysis
### Image Acquisition  
The image dataset used in this study was assembled by two methods: (1) collection from the internet by a web-crawler and (2) the Roboflow Universe platform. The number of images collected through the network was 661, and the number of images from the Roboflow Universe platform was 614. Thus, the total number of original images was 1275. The image resolution varied between 1480 \(\times\) 1974 and 3883 \(\times\) 5184 pixels.  
The images include different lighting conditions (strong light, normal light, insufficient light), natural environmental factors (such as branch and leaf occlusion, fruit stacking), and different shooting distances (close-up and distant). These images include three levels of blueberry fruit maturity: ripe, semi-ripe, and unripe according to maturity. Table 3 provides representative close-up images of the maturity levels, while Figure 6 presents representative images from the dataset.  
To ensure the effectiveness of model training, this study used the LabelImg tool to annotate the collected images, generating XML annotation files in the Pascal VOC format. The data was divided into training and validation test datasets using a split ratio of 70%:10%:20%. To improve generalization and to reduce model overfitting, the dataset was augmented by common techniques such as flipping, scaling, translation, rotation, and the addition of noise, along with random combinations of these methods [28]. To minimize computational effort, data augmentation was pre-computed resulting in a total  
\begin{table}
\begin{tabular}{c c} \hline
**Training Parameters** & **Values** \\ \hline Initial learning rate & 0.01 \\ Optimizer & SGD \\ Optimizer momentum & 0.937 \\ Optimizer weight decay rate & 0.0005 \\ Number of image per batch & 4 \\ Number of epochs & 400 \\ \hline \end{tabular}
\end{table}
Table 2: Training parameters.  
\begin{table}
\begin{tabular}{c c c} \hline
**Maturity Level** & **Number of Labels** & **Sample Image** & **Visual Appearance** \\ \hline Ripe & 31,839 & Dark in color, dark blue \\ Semi-ripe & 11,097 & Lighter in color, purplish red \\ Unripe & 56,137 & Lighter in color, green \\ \hline \end{tabular}
\end{table}
Table 3: Definition of maturity levels and close-up image examples.  
of 4500 augmented images. As shown in Figure 7, the visualization results of the dataset illustrate several aspects: the top left corner of the figure shows the number of labels for each category in the dataset, and the top right-hand corner of the figure presents the distribution of bounding boxes. The bottom left-hand corner of the figure explains the distribution of the center points, and the bottom right-hand corner of the figure displays the size distribution of labels in the dataset. It can be seen from the figure that most of the targets are concentrated in the range of 0-0.2, indicating that small blueberry targets account for a large proportion of the entire dataset.
## 4 Security Analysis and Discussion
### Discussion
In summary, although our scheme may not outperform existing protocols in terms of raw efficiency, it offers superior security guarantees and ensures fairness--attributes essential for the decentralized and resource-constrained nature of IoMT systems. By eliminating dependency on authenticated classical channels and avoiding classical-quantum entanglement, our scheme emerges as a robust and practical GSA-based QKA solution tailored for the stringent requirements of medical IoT environments. Our protocol is applicable to realistic quantum communication environments with inherent imperfections such as photon loss, decoherence, detector limitations, synchronization errors, and channel instabilities. The integrated error correction mechanisms ensure robust key generation performance under practical implementation conditions.
## 3 Results
### Ablation Studies  
We perform an ablation study to investigate the effect of using different loss functions in our semi-supervised setting. We demonstrate the effect of different novel loss functions used in C_q_SL model: WSBF, MIM, and Adv-GM by assessing the model performance when each novel loss functions is removed. Figure 17 shows a graphical representation of the results achieved on the ACDC dataset. In Figure 10, we illustrate the qualitative results on the ACDC dataset to visualize the effect of using all of the loss components. We can observe that the best results are achieved when all of the loss components are used. Specifically, without MIM, the loss curve oscillates, while without WSBF, the output images deviate drastically from the ground truth. Both the quantitative and qualitative results show that the design of C_q_SL improves the preservation of the subject identity and enables more accurate segmentation of cardiac structures.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & \multicolumn{2}{c}{**Clinical Indices of Healthy Volunteers**} \\ \cline{2-3}  & **UNet** & **C_q_SL** \\ \hline LV EF & 0.487 & 0.898 \(\star\star\) \\ RV EF & 0.371 & 0.723 \(\star\) \\ Myo mass & 0.427 & 0.924 \(\star\star\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: The correlation between the C_q_SL-predicted and ground truth clinical indices is significantly higher than the correlation between the U-Net-predicted and same ground truth clinical indices (\(\star\) (\(p<0.01\)), \(\star\) (\(p<0.1\))).  
Figure 16: Graphical comparison showing no statistically significant differences between clinical parameters estimated using C_q_SL segmentation and same parameters estimated using the ground truth segmentation in terms of Mean (Std. Dev.) EF (mL/mL (%)) = ejection fraction, Myo-mass (in gm) = myocardial mass.
## 3 Proposed Method
### Architecture of the Discriminator  
The goal of the discriminator is to determine whether the input image is a real image or a fake image generated by the generator, thus guiding the generator to generate a more realistic image. In our approach, PatchGAN [52] was used as a discriminator, which could reduce the model parameters and better determine the authenticity of the input image. Unlike a traditional discriminator, the output of PatchGAN is a 30 \(\times\) 30 tensor, where each element represents a 70 \(\times\) 70 image patch in the original image, which helps to preserve texture information and remove artifacts from the image, and the final output is the average authenticity of all image patches, which determines the truth or falsity of the whole image.  
The detailed structure of the discriminator is shown in Table 2. For the discriminator, the Leaky-ReLU activation function with a slope of 0.2 is used for all network layers, except for the last layer, and we used instance normalization for all network layers, except for the first and last layers.
###### Abstract  
Nanoparticle floating gate (NPFG) transistors have gained attention as synaptic devices due to their discrete charge storage capability, which minimizes leakage currents and enhances the memory window. In this study, we propose and evaluate a mesh-type floating gate transistor (Mesh-FGT) designed to emulate the characteristics of NPFG transistors. Individual floating gates with dimensions of 3 \(\upmu\)m \(\times\) 3 \(\upmu\)m are arranged in an array configuration to form the floating gate structure. The Mesh-FGT is composed of an Al/Pt/Cr/HfO\({}_{2}\)/Pt/Cr/HfO\({}_{2}\)/SiO\({}_{2}\)/SOI (silicon-on-insulator) stack. Threshold voltages (Vth) extracted from the transfer and output curves followed Gaussian distributions with means of 0.063 V (\(\sigma\) = 0.100 V) and 1.810 V (\(\sigma\) = 0.190 V) for the erase (ERS) and program (PGM) states, respectively. Synaptic potentiation and depression were successfully demonstrated in a multi-level implementation by varying the drain current (I\({}_{\text{ds}}\)) and Vth. The Mesh-FGT exhibited high immunity to leakage current, excellent repeatability and retention, and a stable memory window that initially measured 2.4 V. These findings underscore the potential of the Mesh-FGT as a high-performance neuromorphic device, with promising applications in array device architectures and neuromorphic neural network implementations.  
neuromorphic; synaptic weight; mesh-type floating gate transistor; flash transistor Article
## 6 On-Site Constructions of the Vibrated Grout-Enriched CSGR  
### On-Site Test  
The dam aggregate consists of the natural sand, gravel and rock material excavated from the riverbed. During construction, it is not subjected to screening or washing, and particles larger than 150 mm are removed. To ensure safe construction practices, digital mixing and intelligent control techniques can be employed [6]. Previous indoor testing has demonstrated that when the slurry addition rate is 8%, the vibrating-compacted slurry CSGR material meets the design requirements of C\({}_{90}\)15W6F50. However, in actual construction, the characteristics and construction nuances of CSGR materials often result in deviations from indoor test results. Hence, field testing is conducted to determine the optimal slurry addition rate.  
The field rolling test section is situated along the dam axis, spanning from pile number K2 + 330.38 to K2 + 425.38 within the dam body. Upon the completion of the test, this section is directly integrated into the CSGR dam for permanent structures. The specific area for the vibrated grout-enriched CSGR test is delineated in Figure 10(a), focusing on the dam between pile numbers K2 + 330.38 and K2 + 360.38. The test procedure employs full-section flat paving, with a paving layer thickness of approximately 50 cm.  
For the vibrated grout-enriched CSGR test area, which constitutes the upstream protective layer, the width measures 70 cm. The special grouting and vibrating equipment operates according to the configuration depicted in Figure 9. From bottom to top, the slurry rates are set at 9%, 8%, and 7%, as illustrated in Figure 10(b).  
The test results are presented in Table 9, indicating that the on-site strength of CSGR meets the design specifications for Cemented Material Dam (CMD) engineering. Table 10 displays the test results for CSGR with added slurry and vibration. It is observed from Table 10 that the slurry mix ratio prepared indoors aligns with the engineering requirements for vibrated grout-enriched CSGR. When determining the amount of slurry for construction cost considerations, a preference should be given to an 8% grouting ratio. However, if quality assurance is prioritized, a 9% slurry addition rate is recommended. At this rate, the total cementitious material quantity is 187 kg/m\({}^{3}\), with a total water demand ranging from 126 kg/m\({}^{3}\) to 152 kg/m\({}^{3}\).
# Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry
## 3 Proposed Detection Scheme  
With the principle of our proposed method introduced in Section 2, we have the following findings: as the two cross-track antennas are exploited to observe the same area, there exists obvious difference between jammed and non-jammed pixels of interferometric phase; therefore, the cross-track interferometry can be utilized to identify the false targets in the SAR image.  
Figure 3 shows the flow chart of the proposed detection scheme by two cross-track SAR antennas. After SAR imaging, the dual jammed SAR images are combined to derive the interferometric phase. In the interferogram, the interferometric phase of the false targets can be distinguished from that of the surrounding true elevation. Then, the interferogram is filtered and the false phase can be detected through frequency detection. Ultimately, the corresponding deceptive part in the SAR image is extracted.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency & 9.6 GHz \\ Chirp rate & \(1.3\times 10^{13}\) Hz/s \\ Pulse duration & 10 \(\upmu\)s \\ Sampling frequency & 145 MHz \\ Sensor velocity & 7604 m/s \\ Squint angle & 0\({}^{\circ}\) \\ Altitude & 514.8 km \\ Baseline length & 200 m \\ Baseline inclination & 0\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters of the InSAR System.  
Figure 3: Flowchart of the proposed detection scheme.  
Figure 2: Comparison between the interferometric phase before and after SAR deceptive jamming: (**a**) interferometric phase before jamming; and (**b**) interferometric phase after jamming.
## 3 Results and Discussion
### Antimicrobial Properties  
#### 3.7.1 Antibacterial Activity  
The PETE-Cu(\({}^{0}\)) samples were subjected to antimicrobial activity tests against Gram-negative _Escherichia coli_ (ATCC11229) and Gram-positive _Staphylococcus aureus_ (ATCC 6538). The inhibition experiment results for PETE and PETE-Cu(\({}^{0}\)) are presented in Figure 8.  
The similar pictures have been obtained for PETE and PETE-Cu(\({}^{0}\)) against _Staphylococcus aureus_ species.  
The results of tests on the antibacterial activity of PETE-Cu(\({}^{0}\)) composites against _Escherichia coli_ and _Staphylococcus aureus_ are illustrated in Table 8.  
The results revealed strong visible inhibition zones of bacterial growth on Petri dishes (Figure 8), and therefore proved antimicrobial protection of metallized surface of PETE-Cu(\({}^{0}\)) against representative Gram-negative (_Escherichia coli_) and Gram-positive (_Staphylococcus aureus_) bacteria (Table 8).
# Integrated Multifunctional Laryngoscope for Medical Diagnosis and Treatment
## 2 Materials and Methods
### Probe Design  
The probe had a common path design, including the distal objective and the relay system. As shown in Figure 2, there were two lenses in the distal objective: a plano-concave lens L1 with a focal length of \(-9.8\) mm and an achromatic doublet L2 with a focal length of \(10\) mm. The interval between the two lenses was 15 mm. A GRIN (Gradient-index) lens (Gradient Lens Corporation, Rochester, NY, USA) with a length of one pitch (218 mm) was used as a relay. The homemade optical fiber bundle was placed between the inner metal tube and the outer metal tube and was connected to a LED white light source, which provided additional lighting for the white light endoscope; the fiber used for illumination is a multimode polymers fiber bundle with a 600 \(\upmu\)m core. The diameter and length of the probe were 8 and 250 mm, respectively. The working distance of the probe was set to 15 mm.
# A Novel Spectral-Spatial Classification Method for Hyperspectral Image at Superpixel Level
## 1 Introduction
To our knowledge, most of the existing HSI classification methods based on superpixels are pixel level, and few of them are superpixel level. Therefore, it is significant to propose new powerful and effective spectral-spatial classification approaches at superpixel level in the field of remote sensing. For the HSI classification method at superpixel level, two key problems need to be considered: (i) How to segment the HSI into the superpixels? (ii) How to properly measure the similarity between two superpixels? As for the first problem, two representative segmentation algorithms, simple linear iterative clustering algorithm (SLIC) [42] and the entropy rate superpixel (ERS) [43], are usually applied to divide the given HSI into multiple non-overlapping homogeneous regions. Although these two methods show good performance in the segmentation of HSI, the PCA method must be performed in advance, and the problem of selecting the optimal parameter in SLIC and ERS is also encountered. In addition, it is fundamentally important to properly measure the similarity between a pair of superpixels in the superpixel-level HSI classifier because it is closely related to the classification results. However, the appropriate definition of the similarity between two superpixels is non-trivial, due to the complexity of spectra and spatial structure of the pixels in the superpixel. To address the problems mentioned above, this study chooses SLIC as an attempt first because it has the advantages of fast calculation, convenient use, and accurate matching with the contour of objects [42]. By taking into account the spectrum differences of two pixels and their correlation,we introduce a new distance metric to measure the similarity between two pixels. Based on a novel sorting strategy and the suggested measurement, SLIC has been improved so that it can be straightly used to partition HSI into superpixels, without consulting PCA and requiring parameters. Compared with our earlier improvement of SLIC [28], the improved SLIC here has good computability and understanding. Furthermore, motivated by the idea of local mean-based pseudo nearest neighbor (LMPNN) rule, we also define a new metric to measure the similarity between a pair of superpixels. Specifically, we first adopt the LMPNN rule to calculate the distance from a pixel to a superpixel. Then the superpixel-to-superpixel similarity is obtained by extending the LMPNN rule to the case of set-to-set. Under the assumption that pixels within a superpixel should be the same class label, the classification is carried out by marking each unlabeled superpixel as the same label as its closest labeled superpixel. The main contributions of the proposal are concluded as following:  
* The SLIC algorithm is improved so that it can be directly used to divide any dimensional HSI into superpixels without using PCA and parameters. * The superpixel-to-superpixel similarity is defined properly, which is unrelated to the shape of superpixel. * A novel spectral-spatial HSI classification method at superpixel level is proposed.
## 2 Heliostat Orientation Control
### Sun Position and Heliostat Angles  
Due to the fact that the relative position of the sun in the sky changes throughout the day, it is necessary to use a solar tracker in order to know the location of the sun at any time. The position of the sun with respect to the observer can be described by a reference system of horizontal coordinates using two angles: the azimuth angle and the elevation angle [1]. The angles of the solar vector \(\stackrel{{\rightarrow}}{{S}}\) are denoted by \(A_{s}\) and \(E_{s}\), respectively, as shown in Figure 6a. The azimuth angle is measured in relation to the South (\(0^{\circ}\)), and it is negative to the East (\(-90^{\circ}\)) and positive to the West (\(90^{\circ}\)). The elevation angle of the sun ranges from the horizon (\(0^{\circ}\)) to the zenith (\(90^{\circ}\)).  
For the heliostat to reflect the solar irradiance towards the central receiver, the heliostat surface normal vector \(\stackrel{{\rightarrow}}{{N}}\) must be the bisector of the angle formed by the fixed vector pointing to the receiver from the reflective surface of the heliostat \(\stackrel{{\rightarrow}}{{T}}\) and the solar vector [1] (Figure 6b). The azimuth and elevation angles of the solar vector are given by the Grena [38] algorithm, which has a maximum error  
Figure 5: Center of Gravity (**a**) and Center of Sums (**b**) defuzzification methods.  
Figure 6: Solar vector (**a**) and vectors and angles of the heliostat (**b**).  
of 0.0027\({}^{\circ}\). The solar position algorithm takes the geographical coordinates of the heliostat and the current date and time of the day as input data. The algorithm also uses the monthly average local values of temperature and atmospheric pressure to calculate the atmospheric refraction correction of the elevation angle of the sun. The azimuth and elevation angles of the target vector are obtained by using spherical coordinates. The normal vector is obtained by the addition of the unit vectors of the solar and target vectors.  
\[\overset{\rightarrow}{N}=\left(\begin{array}{cc}\hat{S}_{x}+\hat{T}_{x}& \hat{S}_{y}+\hat{T}_{y}&\hat{S}_{z}+\hat{T}_{z}\end{array}\right) \tag{10}\]  
where \(\hat{S}\) and \(\hat{T}\) are given by (11) and (12).  
\[\hat{S}=\left(\begin{array}{cc}sin(A_{s})cos(E_{s}),&cos(A_{s})cos(E_{s}),&sin (E_{s})\end{array}\right) \tag{11}\]  
\[\hat{T}=\left(\begin{array}{cc}sin(A_{t})cos(E_{t}),&cos(A_{t})cos(E_{t}),& sin(E_{t})\end{array}\right) \tag{12}\]  
Finally, the azimuth and the elevation angles of the normal vector are given by (13) and (14).  
\[A_{h}=tan^{-1}\left(\begin{array}{c}\overset{\rightarrow}{N_{y}}\\ \overset{\rightarrow}{N_{x}}\end{array}\right) \tag{13}\]  
\[E_{h}=tan^{-1}\left(\begin{array}{c}\overset{\rightarrow}{N_{z}}\\ \sqrt{\overset{\rightarrow}{N_{x}}^{2}+\overset{\rightarrow}{N_{y}}^{2}} \end{array}\right) \tag{14}\]
# Performance Evaluation of VANET Routing Protocols in Madinah City
## 3 Methodology  
### OpenStreetMap  
The map of Madinah city was exported with the OpenStreetMap tool--an open-source geographic database of the world. Figure 2 shows the extracted map on the Java OpenStreetMap Editor (JOSM), a java tool to inspect and edit OSM maps. From this tool, ID extraction is possible of the edges (roads) and their connections to create custom trip routes of vehicles traveling from points of interest to the Prophet's Mosque in the center of the exported map.  
The selection criteria comprised of factors such as the inclusion of main highway routes towards the center, the Prophet's Mosque. These highways connect to a primary Ring Road that encircles the point of interest in the center of the city. In addition to all the highway routes, the roads within the encircling Ring Road need to be included as passenger vehicle trips end near the hotels at this location or the drop-off points near the mosque.  
Some of these highways connecting to the Ring Road are primary sources of external traffic towards the city. For example, the west and south-west highways are the primary routes for traffic entering the city for pilgrims coming from Makkah city via road and from the main hub airport for the Hijaz region and pilgrims traveling to the country, King Abdulaziz International Airport (KAIA). In addition, the eastern highway welcomes visitors from the Madinah train station, the final stop for the high-speed haramain railway project connecting Makkah, Jeddah (KAIA airport), and King Abdullah Economic City (KAEC).