esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach
## 2 Methods
### Patch Preparation  
We adapted the patch extraction method described in [37], wherein the color characteristics of the stained tissue were considered. The overall patch preparation procedure is presented in Figure 1. The procedure began with the foreground mask extraction. The color format of the original WSI was converted from RGBA to the CIE L*a*b* color space, which helped separate the red-stained tissues from the background. To extract the foreground mask, we applied the Otsu thresholding method [9] to the a* channel of the image and a closed morphology to eliminate noise. This foreground mask was combined with the mask created from the given XML annotation file to create the masks for normal tissue and tumor area. The tissue and tumor masks were created by applying pixel-wise NOT and AND operations on the two masks, respectively. Notably, the mask obtained from the annotated XML file contained the background, as shown in Figure 2. This might deteriorate the network performance; therefore, we used the newly created mask to improve the accuracy, as shown in Figure 2d.  
The created tissue and tumor masks were used to obtain the WSI without a background. The segmented WSI was then cropped into small patches with a size of \(224\times 224\) pixels. The patches were extracted with 50% overlap for data augmentation. The patch-wise annotation was labeled as follows: MSI-L = 0, MSI-H = 1, and normal tissue = 2, according to MSI classifications diagnosed by the pathologists.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Growing Season** & **Grass Growth Cycle** & \multicolumn{6}{c}{**Treatments (A)**} & \multirow{2}{*}{**Means**} & \multirow{2}{*}{**Significance**} \\ (**B**) & & **(C)** & & **\({}^{*}\)O** & & **\({}^{*}\)S** & **\({}^{*}\)S** + **UGmax** & **\({}^{*}\)S** + **HA** & **\({}^{*}\)S** + **NPK** & \\ \hline \multirow{5}{*}{**2016**} & I & 499.6 & 506.5 & 573.5 & 515.3 & 553.2 & 529.6 & \\ \cline{2-8}  & II & 498.7 & 521.3 & 586.3 & 523.4 & 543.6 & 534.7 & **\(\boldsymbol{\updownarrow}\)C/B = NS** \\ \cline{2-8}  & III & 503.4 & 536.4 & 566.3 & 542.6 & 538.7 & 537.5 & \\ \cline{2-8}  & **Means** & 500.6 & 521.4 & 575.4 & 527.1 & 545.2 & & \\ \cline{2-8}  & **Significance** \({}^{**}\leftrightarrow\)**\(\boldsymbol{\updownarrow}\)**\(\boldsymbol{\updownarrow}\)**\(\boldsymbol{\updownarrow}\)**\(\boldsymbol{\updownarrow}\)** & b & a & ab & a & 533.9 & **\(\boldsymbol{\updownarrow}\)B** = NS** \\ \cline{2-8}  & **I \(\boldsymbol{\updownarrow}\)**A = 32.2** & a & a & b & a & & \\ \hline \multirow{5}{*}{**2017**} & I & 499.8 & 512.0 & 545.5 & 589.5 & 504.9 & 530.3 & \\ \cline{2-8}  & II & 503.7 & 498.8 & 555.7 & 566.4 & 522.7 & 529.5 & **\(\boldsymbol{\updownarrow}\)C/B = NS** \\ \cline{2-8}  & III & 455.5 & 481.1 & 524.4 & 526.2 & 513.9 & 500.2 & \\ \cline{2-8}  & **Means** & 486.3 & 497.3 & 541.9 & 560.7 & 513.8 & & \\ \cline{2-8}  & **Significance** \(\leftrightarrow\)**A/B = 48.3** & b & a & a & ab & 520.0 & **\(\boldsymbol{\updownarrow}\)B** = NS** \\ \cline{2-8}  & **I \(\boldsymbol{\updownarrow}\)**A = 32.2** & a & a & b & a & a & \\ \cline{2-8}  & **Means across growing seasons** & 493.5 & 509.4 & 558.7 & 543.9 & 529.5 & \\ \cline{2-8}  & **Significance** \(\leftrightarrow\)**A = 37.1** & b & b & a & a & ab & 526.9 & \\ \hline \multicolumn{8}{c}{**Means across grass growth cycles**} & \multirow{2}{*}{**} \\ \cline{2-8}  & I & 499.7 & 509.3 & 559.5 & 552.4 & 529.1 & 529.9 & \\ \cline{2-8}  & II & 501.2 & 510.1 & 571.0 & 544.9 & 533.2 & 532.1 & **\(\boldsymbol{\updownarrow}\)C = NS** \\ \hline \multicolumn{8}{c}{**Significance**\(\leftrightarrow\)**A/C = NS**} & \multirow{2}{*}{**} \\ \cline{2-8}  & & & & & & & & \\ \cline{2-8}  & **I \(\boldsymbol{\updownarrow}\)**A = NS** & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The effect of treatments on mean neutral detergent fiber (NDF) content in _Festululululur branit_ dry matter across grass growth cycles and growing seasons (g-kg-1).
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 4 Analysis and Discussion
### Localization
#### 4.1.2 Intermittent GPS Reception  
When GPS is used under an extremely dense forest canopy, reception might only be intermittently reliable when the receiver is stationary for long periods of time or when the receiver crosses openings in the canopy. We simulated this scenario by extracting 12 coordinates from the GPS track; we extracted the starting position, ending position and randomly selected 10 coordinates along 100 m intervals from the track. Figure 10 shows six snapshots during optimization with 12 intermittent GPS coordinate readings. The algorithm converged after 21 iterations. Following local refinement, the optimized path had a RMSE of 2.7 m compared to the ground truth position track in Figure 11. This is an important application of the proposed algorithm in situations where GPS reception is only available intermittently.  
Figure 8: Converged globally aligned path with degraded GPS reception.  
Figure 9: Converged globally aligned path with non-Gaussian GPS errors.  
Figure 10: Global alignment with intermittent GPS reception. Each subfigure is a snapshot during optimization.
# Real-Time Prediction of Fuel Consumption Based on Digital Map API
## 4 Development and Verification of Application  
### Fuel Consumption Prediction Application Interface and Functions  
In order to realize the fuel consumption model proposed above, a smartphone application based on Android was developed. The application is used to gain specific trip information and offer the fuel consumption prediction of each route.  
The users need to enter the details of their engine displacement, and the origin and destination, then the trip time in different speed intervals of each route can be obtained through the API of the Baidu Map (a digital map in China), and the fuel consumption prediction can be served by the proposed model. The flow chart of the fuel consumption prediction is shown in Figure 7. It includes three parts, the mobile phone program, the server and the network communication between them. Mobile phone programs are used to interact with users, such as by obtaining the trip paths selected by users and displaying the fuel consumption prediction. The server is used to calculate fuel consumption. The transmission contents between mobile phones and the server are trip paths selected by users and the fuel consumption prediction. Figure 8 shows the interface of the application: the selection of the origin and destination, the selection of trip paths, and the corresponding fuel consumption prediction.
# Enhancing Security of Online Interfaces: Adversarial Handwritten Arabic CAPTCHAs Generation
## 3 Methodology
### Arabic Handwritten CAPTCHA  
[PERSON] and [PERSON]'s Alquraishi (2018) model has been improved by adding handwritten meaningful words to the meaningless words generated by their model.  
The details of these models are provided below.  
Generation of Arabic Handwritten Words  
The handwritten word generation model produces text in two categories. The first category focuses on generating meaningless character strings that do not correspond to real Arabic words. The second type creates meaningful words by randomly selecting real Arabic terms from a dictionary.  
* Generation of Arabic handwritten meaningless words: [PERSON] and [PERSON]'s Alquraishi (2018) model involves programmatically generating handwritten Arabic words and assembling them into CAPTCHA images. This is done through a series of Hypertext Preprocessor (PHP) scripts. PHP is a general-purpose scripting language that can be utilized to create intuitive and dynamic scripts, automating the entire process.  
PHP first selects a random word by combining characters from predefined Arabic letter arrays. The word is converted to Unicode representation. Individual letter images are retrieved from the Arabic letters MySQL database. This database contains over 250 photos of letters in various positions/styles.  
The letters are arranged on a blank canvas using coordinates calculated from baseline positioning functions. Random wrinkles are applied and overlapped to make the handwriting appear natural. Joining points between letters are highlighted with colored ellipses. Baseline differences are accumulated to adjust letter placements vertically.  
Figure 1: The proposed methodology for generating adversarial handwritten Arabic CAPTCHAs.  
An example of generated CAPTCHA images of meaningless words is shown in Figure 2.  
* Generation of Arabic handwritten meaningful words: To enhance realism and usability, the word generation process is enhanced. Instead of combining random letters, meaningful Arabic words are selectively constructed. An external dictionary file containing over 10,000 common words is incorporated into this process. The script is modified to retrieve validated words randomly from this dictionary, instead of the predefined character arrays. Additional validation is added to filter out words containing rare characters unsupported by the letter image database. Punctuation and diacritics are also stripped for simplicity. This improves the linguistic validity and readability of the generated CAPTCHAs for human users. As real words, they are more intuitive to decipher compared to random strings. An example of generated CAPTCHA images of meaningful words is shown in Figure 3.
# Depletion-of-Battery Attack: Specificity, Modelling and Analysis
## 6 Performance Analysis
In the second case, the energy consumption of packet transmission is increased ten times, as well, \(e_{A}=10e_{0}\). All other system parameters are the same. It can be seen that MTTF is the same in both cases. The most adversely DoB scenario for the victim sensor depends on time. This is verified through simulation results demonstrated in Figure 7, i.e., the harm from DoB caused by spoofed packets exceeds the harm from DoB caused by transmitting power increasing up to the MTTF point, and after this, we get the inverse situation. Obviously, SSF gets closer and closer to zero as time increases. We also examine the tradeoff between DoB detection success and false alarm. The system model assumptions are the same as those discussed in Section 3. Under a DoB attack, the energy consumption per time unit is three times higher than the initial value. The false alarm rate takes the values 0.01, 0.05, 0.1, 0.2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenario** & \(Min\) & \(Q\) **1** & _Median_ & _Mean_ & \(Q\) **3** & _Max_ \\ \hline
**Case 1** & 209.6 & 221.4 & 225.2 & 225.2 & 228.8 & 241.5 \\
**Case 2** & 202.0 & 220.0 & 226.0 & 225.8 & 231.0 & 257.0 \\
**Case 3** & 160.4 & 190.2 & 199.8 & 200.2 & 209.6 & 247.6 \\
**Case 4** & 178.0 & 196.0 & 201.0 & 200.8 & 206.0 & 229.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed statistics for sensor lifetime. \(Min\) is the smallest value in the sample. \(Max\) is the largest value in the sample. _Mean_ is the sample mean. _Median_ is the second quartile. \(Q\) 1 and \(Q\) 3 are the first and third quartiles. Figure 6: Behavior of the system survivability function, depending on \(m\). In Figure 8, the optimal threshold is shown as a function of the false alarm (left plot) and the DoB detection probability (right plot). For example, if the required false alarm rate is 0.05 and \(T_{lag}=5\), then the optimal threshold value equals 11. If \(h\) is larger than the mentioned value, then the required false alarm rate cannot be supported. If \(h<11\), then the false alarm probability is less than 0.05; however, the intrusion detection probability is not minimized. The best detection result under given conditions is achieved with \(h=11\), it is 0.82. Generally, if the selected threshold exceeds the optimal threshold then the false alarm rate is reduced; however, it reduces the intrusion detection probability as well. From Figure 8, observe that the optimal threshold is an increasing function of \(T_{lag}\). This is due to the fact that the change point detection rule is based on sum of non-negative numbers. Let us remark that the quality of detection algorithms can be essentially improved by increasing admissible lag. If in the example above, the required false alarm rate \(\widetilde{\alpha}=0.01\) and \(T_{lag}=15\), then the attack is detected with confidence 99.9%. However, it makes sense if the sensor battery capacity is large enough. Figure 8: Efficiency of DoB detection. Figure 7: System survivability and varied types of DoB.
# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
## 3 Dataset and Traffic Usage Features
### Correlation Analysis  
In this subsection, we use Pearson [45], Spearman [46], and Kendall [47] correlation coefficients to measure the correlation between IPv4 traffic and IPv6 traffic. All of them are widely used in correlation analysis [48; 49; 50].  
The Pearson correlation coefficient between the two time series \(X\) and \(Y\) is defined as  
\[\rho_{X,Y}=\frac{E[(X-\mu_{X})(Y-\mu_{Y})]}{\sigma_{X}\sigma_{Y}} \tag{2}\]  
where \(\rho_{X,Y}\) is the Pearson correlation coefficient varying between \(-1\) and \(1\), \(\mu_{X}\) and \(\mu_{Y}\) are the means of \(X\) and \(Y\), and \(\sigma_{X}\) and \(\sigma_{Y}\) are the standard deviations of \(X\) and \(Y\).  
The Spearman rank correlation coefficient between two time series \(X=\{x_{1},x_{2},...,x_{n}\}\) and \(Y=\{y_{1},y_{2},...,y_{n}\}\) could be calculated as  
\[\rho_{X,Y}=1-\frac{6\sum d_{i}^{2}}{n(n^{2}-1)} \tag{3}\]  
where \(\rho_{X,Y}\) is the Spearman rank correlation coefficient varying between \(-1\) and \(1\), \(d_{i}\) is the difference between the ranks of corresponding values of \(x_{i}\) and \(y_{i}\), and \(n\) is the length of time series.  
The Kendall rank correlation coefficient between two time series \(X=\{x_{1},x_{2},...,x_{n}\}\) and \(Y=\{y_{1},y_{2},...,y_{n}\}\) is  
\[\rho_{X,Y}=\frac{\frac{1}{2}n(n-1)-d}{\frac{1}{2}n(n-1)} \tag{4}\]  
where \(\rho_{X,Y}\) is the Kendall rank correlation coefficient varying between -1 and 1, and \(d\) is the number of discordant pairs in all \(\{x_{i},y_{j}\}\), \(n\) is the length of time series.  
A value of \(\rho_{X,Y}=0\) indicates that the two series are completely uncorrelated. The closer the correlation coefficient is to \(1(-1)\), the stronger the positive (negative) correlation is. As shown in Table 2, the smallest correlation coefficients between IPv4 downstream traffic and IPv6 downstream traffic are 0.804 and 0.740 for DHU and ECNU, respectively. The smallest correlation coefficients between IPv4 upstream traffic and IPv6 upstream traffic are 0.699 and 0.660 for DHU and ECNU, respectively. Therefore, there is a strong positive correlation between the IPv4 traffic and IPv6 traffic in both universities. Additionally, we can see that the correlation between downstream traffic is stronger than that between upstream traffic.
# Analysis of Sealing Characteristics of Lip Seal Rings for Deep-Sea Separable Pressure Vessels
## 2 Seal Calculation Model
### Material Model  
The material used for both lip seals and convex seals is NBR. This material can be modeled as an incompressible hyperplastic material that exhibits complex boundary conditions and non-linearities in its mechanical properties [8]. The currently employed finite element models of rubber materials include [9; 10; 11] the Neo-Hookean strain energy function based on statistical thermodynamics, the Lord-Shulman and Green-Lindsay theories for the generalized thermoelasticity model [12] and the Mooney-Rivlin model, the Klosenr-Segal model, and the Ogden-Tschoegl model based on phenomenological theory. In this paper, we employed the two-parameter Mooney-Rivlin model and expressed the strain function as [13]:  
\[\text{w}=C_{10}(I_{1}-3)+C_{01}(I_{2}-3) \tag{2}\]  
where w is the strain energy density function; \(I_{1}\) and \(I_{2}\) are the two main invariants of the strain tensor; \(C_{10}\) and \(C_{01}\) are material parameters related to the material modulus of elasticity E and material hardness H. The Equation representing the data fitting relationship is as follows [14; 15]: the values of \(C_{10}\), \(C_{01}\), and the modulus of elasticity \(E\) are determined by calculation to be 1.46, 0.37, and 10.98 MPa, respectively.  
\[\left\{\begin{matrix}\text{lg}E=0.0198H-0.5432\\ E=6(C_{10}+C_{01})\\ C_{01}=0.25C_{10}\end{matrix}\right. \tag{3}\]  
The upper and lower spherical shells are constructed from FRP, which possesses the following physical parameters: elastic modulus \(E\) = 18.2 GPa, Poisson's ratio = 0.25, allowable stress = 241 MPa, density = 1.8 g/cm\({}^{3}\). Due to the considerably higher modulus of elasticity of the upper and lower spherical shells in comparison to that of the seal material, their deformation is deemed negligible in relation to the seal.
# Towards Deep Integration of Electronics and Photonics
## 4 From Macroscopic to Microscopic Integration
The described single-photon transistor can be used for both optical computations and quantum information processing. Integration of principles and devices described here with the quantum computing paradigm and distributions of functions between photons and electrons in new quantum circuits remain two important questions to be answered.
# xLSTM-Based Urban Traffic Flow Prediction for Intelligent Transportation Governance
###### Abstract  
Urban traffic congestion poses persistent challenges to mobility, public safety, and governance efficiency in metropolitan areas. This study proposes an intelligent traffic flow forecasting framework based on an extended Long Short-Term Memory (xLSTM) model, specifically designed for real-time congestion prediction and proactive police dispatch support. Utilizing a real-world dataset collected from over 300 vehicle detector (VD) sensors, the proposed model integrates vehicle volume, speed, and lane occupancy data at five-minute intervals. Methodologically, the xLSTM model incorporates matrix-based memory cells and exponential gating mechanisms to enhance spatio-temporal learning capabilities. Model performance is evaluated using multiple metrics, including congestion classification accuracy, F1-score, MAE, RMSE, and inference latency. The xLSTM model achieves a congestion prediction accuracy of 87.3%, an F1-score of 0.882, and an average inference latency of 41.2 milliseconds--outperforming baseline LSTM, GRU, and Transformer-based models in both accuracy and speed. These results validate the system's suitability for real-time deployment in police control centers, where timely prediction of traffic congestion enables anticipatory patrol allocation and dynamic signal adjustment. By bridging AI-driven forecasting with public safety operations, this research contributes a validated and scalable approach to intelligent transportation governance, enhancing the responsiveness of urban mobility systems and advancing smart city initiatives.  
xLSTM; traffic flow forecasting; AI-based urban management Article
## 3 Methodology
### Number of Packets Received (NPR)  
This section presents a comparative analysis of the number of packets received (NPR) when employing the neighbor node analysis technique against alternative methodologies across multiple iterations illustrated in Figure 11. Empirical findings reveal that the NBEER design strategy yielded suboptimal outcomes in comparison to the conventional approach. Notably, NBEER's approach marginally outperformed Co-UWSN and CEER in terms of NPR. However, the NPR improvement achieved by NBEER through iterative rounding was only modestly superior to the initial NPR. While the CEER method aimed to achieve a reasonable NPR, the NBEER method demonstrated the ability to attain a higher NPR compared to other approaches.  
In accordance with Figure 10, the NBEER methodology emerges as a commendable strategy for optimizing energy efficiency and prolonging the lifespan within the framework of an UWSN. The comprehensive outcomes of the experiment reveal that the CEER technique exhibited unsatisfactory performance across all NPR assessment criteria [15]. Co-UWSN performed better than CEER, and ultimately, our proposed approach outperformed all alternative methodologies, yielding superior outcomes.  
Figure 10: Comparison of average values of alive nodes of NBEER with other state-of-the-art protocols.
## 5 Challenges and Future Research Directions
### Performance Evaluation in Federated Learning  
Investigating the implications of transmission bandwidth on Federated Learning (FL) delay performance is one of the primary challenges in 6G enabled UAV networks. Despite the fact that processing capacity are becoming more capable, wireless communication bandwidth has not increased considerably. As a result, the bottleneck has migrated away from compute and toward communication. As a result of the limited communication capacity, there may be a greater communication delay, resulting in extended FL convergence times. As a result, communication-efficient FL is a hot topic for research right now and in the future [136].