esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Comparative Experiments  
The results of the comparative experiments are reported in Table 12, in which the results of ResNet18, ResNet50, Auto-sklearn, AutoKeras, and Google AutoML Vision are from [19]. For the three AutoML models, the original paper does not provide metrics in Pre, Rec, and F1, which are marked as dashes in Table 12.  
Table 12 shows the performance of the comparative models on seven datasets in MedMNIST. In terms of accuracy (ACC), AOC-Caps achieves the best accuracy on DermaMNIST and OrganMNIST_Axial datasets, and ranks second on other datasets. CapsNet with dynamic routing and EM routing demonstrate worse performance than other methods, due to the shallow feature extraction network. However, because the data of OrganMNIST_Axial, Coronal, and Sagitttal) are collected from 3D images, the viewpoints are different. Therefore, the CapsNet with the EM routing with transformation invariance can obtain good accuracy, even with the shallow feature extraction network.  
Due to the class imbalance in certain datasets of MedMNIST, it is necessary to consider different metrics. In terms of precision (PRE) and recall (REC), the performance of all methods is lower than accuracy on datasets DermaMNIST and OrganMNIST_Sagittal because of the data imbalance between different categories in DermaMNIST and OrganMNIST_Sagittal. In this case, AOC-Caps still achieves better results. However, it should be pointed out that there is no corresponding solution to this data imbalance problem in our experiments.  
In our comparative experiments, a model with deeper layers does not necessarily achieve higher accuracy. Consider ResNet18 as an example. It outperforms ResNet50 on datasets such as DermaMNIST, OCTMNIST, OrganMNIST_Axial, and OrganMNIST_Sagittal. As part of the reason, the low resolution of the image data does not require a deep network model to extract high-level features.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset Names**} & \multicolumn{6}{c}{**AOC-Caps (without Attention)**} & \multicolumn{6}{c}{**AOC-Caps (with Attention)**} \\ \cline{2-11}  & **ACC** & **PRE** & **REC** & **F1** & **AUC** & **ACC** & **PRE** & **REC** & **F1** & **AUC** \\ \hline PathMNIST & 0.805 & 0.790 & 0.785 & 0.785 & 0.945 & **0.859** & **0.840** & 0.837 & **0.845** & 0.960 \\ \hline DermaMNIST & 0.721 & 0.405 & 0.337 & 0.382 & 0.850 & **0.786** & **0.485** & 0.417 & **0.447** & 0.890 \\ \hline OCTMNIST & 0.689 & 0.756 & 0.743 & 0.749 & 0.943 & **0.750** & **0.820** & 0.802 & 0.819 & 0.955 \\ \hline PneumoniaMNIST & 0.863 & 0.847 & 0.826 & 0.833 & 0.980 & **0.931** & **0.914** & 0.893 & 0.895 & 0.990 \\ \hline OrganMNIST\_Axial & 0.877 & 0.864 & 0.845 & 0.856 & 0.972 & **0.931** & **0.920** & **0.903** & 0.919 & 0.973 \\ \hline OrganMNIST\_Coronal & 0.832 & 0.843 & 0.830 & 0.836 & 0.955 & **0.907** & **0.911** & 0.904 & **0.904** & 0.965 \\ \hline OrganMNIST\_Sagittal & 0.711 & 0.654 & 0.630 & 0.648 & 0.955 & **0.783** & **0.731** & **0.717** & **0.723** & **0.980** \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance of AOC-Caps with/without attention module.
# Segmentation of Rice Seedings Using the YCrCb Color Space and an Improved Otsu Method
## 2 Materials and Methods
### Image Acquisition  
As shown in Figure 1A, we designed an automatic acquisition device that mainly consisted of two parts: the image acquisition device and a PC computer. Rice seedling images were obtained using a HIKVISION MV-CA050-20 UM/C camera (Hangzhou Hikvision Digital Technology, Hangzhou, China) (with a USB port. The focal length of the camera lens was 25 mm. The camera mounted on a camera support at 1.8 m above the ground took pictures of seedlings (See Figure 1B). The obtained images were transmitted to the computer through a USB cable, and were saved as 24-bit color images in RGB color space with the JPG format. The resolution of the original images is 2560 \(\times\) 1920. After the JPG images were obtained, it is necessary to shrink the size of images to reduce the computational load. In our experiment, the image size shrank from the original size of 2560 \(\times\) 1920 to 320 \(\times\) 237 pixels.
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 1 Introduction
### Limitations of Traditional Detection Methods
#### 1.2.5 Machine Learning Limitations  
_Adversarial evasion:_ recent ML-based XSS detection systems are vulnerable to adversarial examples specifically crafted to bypass their detection mechanisms.  
_Feature engineering inadequacies:_ current ML approaches often struggle with selecting robust features that remain effective against evolving attack techniques.  
_Transfer learning challenges:_ [PERSON] and [PERSON] demonstrate that ML models trained on one set of applications often perform poorly when applied to different codebases with unique characteristics [17].
## 3 Results and Discussion
### Chicken Meat Preservation with Kidney Bean Protein Hydrolysates
#### 3.2.1 Physicochemical Changes during Cold Storage
However, RKH supplementation (800 \(\upmu\)g/g) increased the redness (\(a^{\star}\)) of chicken meat by 10% over the control, and BKH addition (800 \(\upmu\)g/g) increased the blueness (\(b^{\star}\)) of meat by 12% over the control. Meat whiteness is expected to increase with increasing WKH concentration, however decreased with increasing BKH concentration (Figure 5A-C). The results were confirmed by total color change (\(\Delta\)E) in Figure 6. The highest total color change showed in meat sample supplemented with WKH (400 \(\upmu\)g/g), i.e., 2.4 with relative increase 25% about control. However the lowest value was found in samples supplemented with BKH (200 \(\upmu\)g/g), i.e., 0.5. Generally, the color parameters faded after 30 days of storage, but supplementation with BKH, RKH and WKH significantly maintained about 75% of color attributes. [PERSON], et al. [91] found that all color parameters of chicken meat preserved at different temperature degrees decreased with the storage period. No available studies cast light on the changes in color parameters and sensorial traits of chicken meat samples supplemented with kidney bean protein hydrolyses. The addition of (BKH, RKH, and WKH) significantly maintained the meat quality, where BKH-supplemented chicken meat samples recorded the highest scores in tenderness and juiciness, i.e., 8.5, and 8.7 at zero-time storage (Table 8 and Figure 7), as it had the highest scores in water holding capacity in its original status. BKH also improved the taste and flavor of cooked chicken meat. A lower magnitude of meat quality improvement was recorded with RKH and WKH supplementation. Generally, all meat quality decreased after 30-day storage, but supplementation with the studied hydrolyses significantly reduced the deterioration rate by about 50-65% and reduction may be increased by increasing BKH concentration (Table 8 and Figure 7). It was observed that the sensory attributes of nuggets meat samples were significant during cold storage. However nuggets incorporated with GPP and GSCP had better shelf life and are highly acceptable than control [92].
## 4 Performance Evaluation
### Different Levels of Situational Awareness  
In this section, we discuss the role and the advantages of situational awareness in predicting beam power. The current loss models are only concerned with the relative distance [4], or with the absolute position between the receiver and the transmitter [21]. The authors of [8] also show that there is only one way for the location of the receiver to provide useful information about the beam, and that is through previously transmitted datasets. The experiments in this paper show that, in an urban environment, not only can the dataset provide location information, but there is also a more accurate way to predict  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **RMSE (dBm)** \\ \hline Random Forest & 1.726 \\ Linear Regression & 6.199 \\ Gradient Boosting & 2.814 \\ SVR & 3.645 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSEs of four regression algorithms.  
the beam power, which is to use the environmental information around the vehicle location for the prediction.  
The position information of the vehicle is provided in Section 3.1. Figure 7 shows the RMSEs of the strongest pairs of beams. The prediction method that was used is random forest regression. The dots in the figure represent the positions of the \(i\)-th vehicle, as described in Section 3.1. The results show that most of the information on the beam power is provided by the trucks in the first lane. The RMSE is now around 1.7, which is down from 4.5 for the previously described case that used only the receiver as a feature. Meanwhile, the truck position in the second lane lowered the RMSE to 1.6. However, low-altitude cars have little effect on beam-power predictions. Not only that, but when we add other types of vehicles, the RMSE drops slightly, but not to a significant degree. From this it is concluded that the position of the tall truck in the first lane has a very strong correlation to the beam-power prediction. Furthermore, when the lane-location features are not complex, the predicted results will be more accurate.
## 4 Discussion
### Construction and Application of the FPGA-Based Forward Inference Acceleration Module  
The FPGA-based forward inference acceleration module mainly implements the computation of the convolutional, pooling, and fully connected layers of the CNN network. Given the different flux sizes of particles at different orbital heights in space, the required extrapolation time is not the same. Through the modular program architecture, different parallel capabilities can be flexibly constructed, and the forward inference time can be adjusted to achieve the optimal matching of resources and latency. In addition, in order to minimize the extrapolation time, all kinds of parameters and intermediate variables of the lightweight model are stored in the form of registers so that all data can be read and calculated in parallel.  
Although the acceleration algorithm can realize neutron and gamma extrapolation analyses, it does not integrate hardware preprocessing algorithms, and the resources for larger-scale models cannot meet the requirements; thus, in the future, it will be combined with a more universal and dedicated module for the construction of the architecture, such as the irradiation-resistant VITIS AI platform or other onboard AI chips to enhance performance.
# A 11 mW 2.4 GHz 0.18 \(\upmu\)m CMOS Transceivers for Wireless Sensor Networks
## 1 Introduction  
Short-distance communications have recently spurred lots of researches and developments. The RF transceiver is undoubtedly one of the key components in a communication system [1; 2; 3; 4; 5]. However, it is not possible to design a transceiver to host all types of communication systems. Depending on the applications, the topologies of transceiver ICs or systems may vary considerably in terms of the system architecture and performances such as data-rate, power consumption, physical dimensions, etc. [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. For example, Complementary Metal Oxide Semiconductor (CMOS) RF transceivers for short-range communications have been reported extensively, but it is still of a great importance to design transceivers for certain applications since no one solution can cover all the desires in different scenarios where the design strategies may vary significantly. To achieve ultra-low power consumption, many transceivers are designed without low noise amplifier (LNA) or voltage controlled oscillator (VCO) [4; 10] at the expense of functionality. For an oscillator-less design or injection-locked oscillator-based architecture, the transceivers are unable to provide programmable channel frequencies, which limit the functionality of the system. The functionality of such a systemare quite limited even though sub-mW operation is achieved [10]. For example, in the applications of wireless sensor networks (WSN), the design considerations are quite different even though low power consumption is always a key advantage. The amount of chips used in WSN applications will be massive, and there are many different protocols and communication standards involved in such applications that it is, therefore, highly desired that the transceiver is designed with fully functional blocks, such as a LNA, phase-locked loop (PLL), and the cost of the circuits should be as low as possible. For example, the design will be attractive if all of the circuits can be implemented with low cost but reliable technology, such as 0.18 \(\upmu\)m CMOS technology, which maintains a good balance among the level of integration, cost, and performance. Of course, the designs should have fewer off-chip components and a small silicon area. Therefore, the number of on-chip inductors or large capacitors (higher than tens of pF) should be kept as low as possible.  
By considering all of these factors, this paper presents a low power transceiver working on the 2.4 GHz band which is one of the most popular Industrial Scientific Medical (ISM) communication bands. The system is designed with conventional architecture, but it is optimized to maintain good balance among the considerations in WSN applications. This paper is organized as follows: Section 2 presents the system planning of proposed transceiver, followed by the implementation of circuits in Section 3; Section 4 demonstrates the experimental results of the proposed circuits and system; and Section 5 is the conclusion.
## 1 Background
[28] investigated the effects of SSI on several residential RC buildings with different capacity design principles founded on four different soil conditions and applying 20 acceleration records. Other studies, such as refs. [29,30] proposed analytical surveys on the effects of SSI in terms of fundamental period and damping ratio. In addition, ref. [31] considered both experimental and numerical results and ref. [32] investigated the effects of SSI with a 1/4-scale steel-frame structure by reproducing 34 scenarios under fixed-base and flexible-base conditions. Moreover, the interaction between SSI and BI has been relatively studied in the literature. In this regard, ref. [12] analyzed the interaction between SSI and BI by performing numerical analyses, ref. [6] studied the multi-story buildings with and without sliders in the cases of ground shock due to tunnel explosions. [PERSON] et al. [14,15] investigated the effect of SSI on the response of base-isolated buildings by considering equivalent fixed-base systems. This paper aims to overcome the previous literature, by proposing several novelties. (1) The interaction between base isolation (BI) and SSI effects are considered with advanced 3D numerical simulations, to capture the highly non-linear mechanisms between the soil, the foundation, the isolators and the superstructure. (2) The paper proposes a novel framework that consider resilience as the reference parameter. (3) The comparisons of the behaviour of the different configurations may be helpful for code provisions. The paper is structured with the presentation of the case studies (Section 2), followed by the definition of the seismic resilience calculation (Section 3). Section 4 show the results and their discussion.
## 1 Introduction
This spectral range does not significantly affect the life of cultivated plants. In nature, light in the yellow and green ranges is mainly important for shade-loving plants growing in the lower tiers of forests, as well as for some aquatic plants [21]. UV radiation also does not play a significant role in the life of cultivated plants; it should be noted that in the solar spectrum only 1-2% of the energy falls on UV radiation photons [22]. Based on the above, in greenhouses under conditions of insufficient insolation, the most logical would be the photoconversion of UV radiation, green and yellow light, into blue and red light [23]. Currently, many greenhouse coatings have been developed that carry out partial photoconversion [24; 25; 26]. Earlier in our Biophotonics centre, based on quantum dots and gold nanoparticles, a photoconversion coating was developed for greenhouses located in the Arctic regions [27]. The nanoparticles in the polymer were arranged in the form of linear aggregates (like beads), which made it possible to significantly enhance the field of the optical wave. In this case, the absorption in the plasmon resonance line of gold nanoparticles (green region of the spectrum) increased several times, which ultimately leads to a significant heating of the polymer. In addition to heating, the polymer is capable of photoconversion, including the red region of the spectrum that is important for plants productivity (maximum emission at 630 nm). It has been shown that these polymer films are capable of accelerating the growth of a number of crops at the initial stages of development at low temperatures. In this work, photoconversion films based on the highly resistant and vapor-proof fluoropolymer LF-32 LN have been created. These films are not capable of heating; nanoparticles in these films do not form linear aggregates in the form of beads, they are evenly spaced. In this work, a quantum dots with quantum yield of more than 20% was synthesized, with emission in the blue (\(\lambda_{\text{max}}=460\) nm) and red (\(\lambda_{\text{max}}=650\) nm) spectral regions. The fluorescence of the quantum dots we synthesized now overlaps with the spectral window of photosystem II of higher plants, which provides significant advantages. It is shown that the developed photoconversion films not only make it possible to increase the rate of plant development at an early stage, but also to obtain a larger yield. The use of the developed film could significantly increase the productivity of greenhouses located in conditions of insufficient average daily illumination.
# A Deep Learning-Based Approach for Extraction of Positioning Feature Points in Lifting Holes
## 2 Methods
### The Point Extraction Method Based on Line Features
#### 2.4.3 Features Points Fitting  
In certain cases, due to inadequate segmentation results, there may be some outliers within the given cluster groups, or even within the target region itself. The line fitting results are susceptible to the distribution of points, and to avoid the influence of such special cases, VLFGM is proposed. Its principle is as follows:  
For a given set of points \(\mathcal{P}=\{P_{1},\cdots,P_{n}\}\), where two points \(P_{i}=(x_{i},y_{i})\), \(P_{j}=(x_{j},y_{j})\) form a line \(P_{i}P_{j}\), the equation of this line can be expressed as follows:  
\[y=\frac{y_{j}-y_{i}}{x_{j}-x_{i}+\varepsilon}x+y_{i}-\frac{x_{i}(y_{j}-y_{i})} {x_{j}-x_{i}+\varepsilon} \tag{17}\]  
where \(\varepsilon\) is a very small floating point number to prevent division by zero. Let \(A\) represent the slope of the line and \(B\) represent the intercept. The distance \(D(q)\) from any other point \(P_{q}=(x_{q},y_{q})\) in the set to the line L can be calculated as follows:  
\[D(q)=\frac{\left|Ax_{q}-y_{q}+B\right|}{\sqrt{A^{2}+1^{2}}} \tag{18}\]  
In order to directly and simply select the principal component points, we assign weights to each point in the point set based on their distance to the line. This approach considers points closer to the line as more important reference points, and the fitted line will be biased towards the intersection with points having higher weights due to their higher vote count. Specifically, we allocate scores to all points based on their distances,such that closer points have a higher proportion of scores, while farther points have lower weights. This mapping relationship is detailed in Table 3.  
Therefore, the total vote count for the line \(P_{i}P_{j}\) is indicated as  
\[V=\sum\limits_{q=1}^{n}\psi(D(q)),q\
eq i,j \tag{19}\]  
When a line receives the maximum number of votes, it indicates that it is the best fit line with the majority of points close to it. Thus, this line is considered as the optimal fitting line. Let us denote the starting point as \((x_{0},y_{0})\), and n as the number of sides of the polygon. Naming all the vertices in a clockwise manner from 0 to \(n-1\). Once the four fitting lines are obtained, it is easy to determine the coordinates of the four vertices. For a given set of four vertices, which enclose a polygon, the centroid of the polygon can be quickly computed by:  
\[Area=\frac{1}{2}\sum\limits_{i=0}^{n-1}|(x_{i}y_{i+1}-x_{i+1}y_{i})| \tag{20}\]  
The coordinates of the centroid are given by:  
\[\overline{x}=\frac{\sum\limits_{i=0}^{n-1}|(x_{i}+x_{i+1})(x_{i}y_{i+1}-x_{i+1 }y_{i})|}{6\times Area},\overline{y}=\frac{\sum\limits_{i=0}^{n-1}|(y_{i}+y_{ i+1})(x_{i}y_{i+1}-x_{i+1}y_{i})|}{6\times Area} \tag{21}\]
## 7 Conclusions and Future Work  
In this paper, the tracking of all multipath parameters using SPA was made possible through the front-end consideration of the communication systems. This process was followed by discretizing channel parameters and assigning them Bernoulli random variables. The primary limitation of the algorithm is its complexity in detection of path patterns when the number of interfering paths increases significantly. The use of quantum computing algorithms for path pattern detection could offer a potential solution to this challenge. In future work, we aim to compute the optimal discretization levels.  
Conceptualization, A.M. and A.Z.; methodology, A.M. and A.Z.; software, A.M. and A.Z.; validation, A.M. and A.Z.; formal analysis, A.M. and A.Z.; investigation, A.M. and A.Z.; resources, A.M. and A.Z.; data curation, A.M. and A.Z.; writing--original draft preparation, A.M. and A.Z.; writing--review and editing, A.M. and A.Z.; visualization, A.M. and A.Z.; supervision, A.M. and A.Z.; project administration, A.M. and A.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Operations** & **LUT Accesses** & **Comparisons** \\ \hline \((4N^{2}N^{{}^{\prime}}+8N^{{}^{\prime}}+4)(L-1)\) & \(6N(L-1)+4\) & \(\frac{N^{2}N^{{}^{\prime}}}{2}-NN^{{}^{\prime}}+N+2N^{{}^{\prime}}-6\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational Load at each time epoch for B-PAM Modulation.  
Figure 5: 3-JDD in the concentrated pilots, including the lower and upper bounds (LDPC and relative solution).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Author [PERSON] was employed by the SELTA Digital Platforms Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Results and Discussion
### Scenario 2: Sources with Mixed Stable Distribution
Figure 14: Detailed simulation results for reference signals from mixed distribution (49) with \(\tau=0.75\), \(\alpha_{1}=2.0\), and \(\alpha_{2}=1.6\); (**a**) [PERSON]’s algorithm [24]; (**b**) Th-FxLMS algorithm [25]; (**c**) modified FxLMK algorithm; (**d**) INSS-FxLMS algorithm [26]; (**e**) effect of step-size parameter in the proposed FxRLS-based impulsive ANC algorithms; (**f**) performance comparison between various algorithms: (1) [PERSON]’s algorithm (\(\mu=1\times 10^{-6}\)); (2) Th-FxLMS algorithm (\(\mu=1\times 10^{-5}\)); (3) modified FxLMK algorithm (\(\mu=1\times 10^{-4}\)); (4) INSS-FxLMS algorithm (\(\mu=1\times 10^{-3}\)); (5) previous CCSS-NMFxLMS algorithm (\(\mu_{1}=1.0,\mu_{2}=1\times 10^{-2}\)); (6) FSS-MFxRRLS algorithm (\(\mu=0.1\)); (7) proposed CCSS-MFxRRLS (\(\mu_{1}=1.0,\mu_{2}=0.05\)).