esa-sceva/satcom-chunk-collection huggingface dataset:
# Vision-Based Distance Measurement in Advanced Driving Assistance Systems
## 5 Proposed Method Implementation  
On the basis of the pretrained semi-supervised network for depth map estimation and the Mask R-CNN network for instance segmentation, the flow chart of the proposed distance measurement method is as follow (shown in Figure 5).  
Figure 4: Locations of the object points in the camera body coordinate system. (**a**) The object point locations of the car. (**b**) The object point locations of the pedestrian. (**c**) The fitted plane using the object points of the car. (**d**) The depth histogram of the object points of the pedestrian.  
It is important to note that when the binocular vision equipment used in offline training is different from the image sensor used in the online test, in order to obtain the absolute depth value, it is necessary to adjust and calibrate the focal length of the online sensor based on the image resolution and focal length of the training equipment. The relationship between two focal lengths is as follows:  
\[f_{test}=\frac{f_{train}w_{train}}{w_{test}} \tag{11}\]  
where \(f_{train}\) and \(f_{test}\) are the focal lengths of sensors for training and testing, respectively, and \(w_{train}\) and \(w_{test}\) are the widths of the training and test images, respectively. As mentioned above, the output of the pretrained network for depth estimation is the disparity map \(\mathbf{d}\). In order to obtain the depth value of each pixel of the input image, we must use the following equation:  
\[\mathbf{D}(i,j)=\frac{f_{test}b}{\mathbf{d}(i,j)} \tag{12}\]  
where \(\mathbf{D}(i,j)\) is the depth value of each pixel, \(\mathbf{d}(i,j)\) is the disparity value of each pixel, and \(b\) is the baseline length of the binocular vision equipment used in offline training.
## 6 Discussion and Conclusions
### Societal and Educational Implications  
The development of effective machine translation for Tarifit has significant implications beyond computational linguistics. For the 5 million Tarifit speakers worldwide, access to translation technology can bridge communication gaps in multilingual contexts, particularly for diaspora communities maintaining connections with their linguistic heritage.  
In educational settings, these tools can support Tarifit language preservation efforts by enabling content translation for educational materials and facilitating bilingual education programs. The demonstrated effectiveness of ICL approaches suggests viable pathways for  
Figure 6: Error type distribution by language.  
developing similar technologies for other endangered Berber languages, contributing to broader linguistic diversity preservation in the digital age.  
Furthermore, the accessibility of ICL methods--requiring minimal computational resources compared to traditional neural machine translation--makes this technology more democratically available to language communities that lack extensive technical infrastructure. This democratization of language technology represents a step toward more equitable representation in artificial intelligence systems. These findings align with patterns observed in ICL research for other morphologically complex languages like Manchu [12] while revealing the unique influence of historical language contact patterns in Berber language processing, where Arabic proximity effects (8.4 BLEU advantage) exceed typical related-language improvements in ICL studies.
## 2 Proposed Method
### Real-Time Operation Procedure  
Given a certain verification sample, the procedure for real-time operation is as follows:  
Step 1: Let \(i=1\).  
Step 2: Use the conditional attributes in flood period \(i\) as inputs to the DT-based rules to solve the discharge \(q_{i}\) in period \(i\), and calculate the water level \(z_{i}\) in period \(i\) using the water balance equation.  
Step 3: To ensure downstream safety, confirm that \(q_{i}\) is less than \(q^{*}\), the maximum discharge of the verification sample as regulated by conventional operation rules. If this is true, proceed to Step 4; otherwise, set \(q_{i}=q^{*}\).  
Step 4: If \(q_{i}\) is less than \(q(z_{i})\), which is the reservoir discharge capacity when the water level is \(z_{i}\), proceed to Step 5; otherwise, set \(q_{i}=q(z_{i})\).  
Step 5: Let \(i=i+1\). If \(i<T\), where \(T\) is the verification sample period count, return to Step 2; otherwise, end the operation.  
The real-time operation procedure is illustrated in Figure 1.
## 3 Circuit Design
### Analog Front-End
#### 3.1.3 High-Pass Filter Design and Optimization
In summary, in order to keep the input-referred noise due to the first stage pseudo-resistors and to the \(G_{M}\) cell in the amplifier band lower than \(1\)\(\mu\)V\({}_{rms}\), \(f_{1}\) has to be at least a factor of \(10\) smaller than \(f_{HP}\) and \(C_{HP}\) must be greater than \(2\) pF. The two pseudo-resistors in the first stage of the circuit in Figure 3 were designed to have \(f_{1}<10\) Hz (with \(f_{HP}=300\) Hz) and \(C_{HP}=7\) pF.
# Dynamic Coal Flow-Based Energy Consumption Optimization of Scraper Conveyor
## 5 Case Study and Validation
### Optimization Simulation and Results
#### 5.2.2 Simulation Results and Analysis
The corresponding optimized decision variables are as follows: shearer traction speed \(\mathrm{v_{q}}\) = 6.1 m/min, scraper conveyor chain speed \(v_{j}\) = 31 m/min, and drum rotation speed \(n\) = 50 r/min. The optimized objective values for energy consumption E and coal block area \(S_{a}\) are 1.2617 \(\times\) 10\({}^{9}\) KJ and 4912 mm\({}^{2}\), respectively. Further experimental validation is provided in Table 8, which presents a comparative analysis of the pre- and post-optimization results. To evaluate the effectiveness of the proposed optimization strategy, a comparison is first made between the original working condition and the selected compromise solution (Point B). Under the original parameters, the system exhibits an energy consumption of 1.9034 \(\times\) 10\({}^{9}\) KJ and a lump coal area of 13,827 mm\({}^{2}\). After applying multi-objective optimization, Point B achieves an energy consumption of 1.2617 \(\times\) 10\({}^{9}\) KJ and a lump coal area of 4912 mm\({}^{2}\), representing a 33.7% reduction in energy consumption and a 64.5% reduction in lump coal area. It should be noted that although a reduction in lump coal area is often associated with increased fragmentation, in this context, the decrease is intentional and beneficial. The optimization aims to avoid excessive formation of large lump coal, which may hinder conveying and processing efficiency. Therefore, the reduced lump coal area reflects a controlled refinement of coal structure rather than a decline in coal quality. Compared with the single-objective optimization results--Point C (minimum energy: 1.0169 \(\times\) 10\({}^{9}\) KJ, but higher lump coal area: 9989 mm\({}^{2}\)) and Point A (minimum lump coal area: 2500 mm\({}^{2}\), but high energy: 1.8086 \(\times\) 10\({}^{9}\) KJ)--Point B (*) provides a balanced trade-off between energy efficiency and coal fragmentation. It avoids the extremes of excessive fragmentation or oversized coal blocks, and thus represents a practical and effective compromise for real mining operations, aligning energy-saving goals with acceptable coal integrity.
## 3 System Architecture
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Paper** & **Correlation** & **Histogram** & **Entropy** & **NPCR** & **UACI** & **BER** & **PSNR** & **MSE** & **SSIM** & **Key Strength** \\  & **Coefficient** & **Analysis** & **Entropy** & **NPCR** & **UACI** & **BER** & **PSNR** & **MSE** & **SSIM** & **Analysis** \\ \hline \hline
[1] & \multicolumn{6}{c}{Communication cost, encryption, and decryption time analysis} \\ \hline
[10] & ✓ & & & & & & ✓ & & & \\ \hline
[6] & ✓ & & ✓ & & & ✓ & & & \\ \hline
[17] & ✓ & ✓ & ✓ & ✓ & & & & & \\ \hline
[19] & ✓ & ✓ & ✓ & ✓ & & & & & \\ \hline
[20] & \multicolumn{6}{c}{Analysis to check Accuracy, cost and devices required} \\ \hline
[5] & \multicolumn{6}{c}{Related key attack analysis, Fault injection analysis, Differential and Linear cryptanalysis} \\ \hline
[18] & \multicolumn{6}{c}{Cryptanalysis, Calculation of key generation time analysis} \\ \hline
[15] & ✓ & ✓ & ✓ & & & ✓ & ✓ & & \\ \hline
[12] & \multicolumn{6}{c}{✓} & & ✓ & ✓ & ✓ & & & \\ \hline
[16] & \multicolumn{6}{c}{} & ✓ & & ✓ & & & & \\ \hline
[11] & \multicolumn{6}{c}{} & & & ✓ & ✓ & & & & \\ \hline
[13] & \multicolumn{6}{c}{} & & & ✓ & ✓ & & & & \\ \hline
[14] & \multicolumn{6}{c}{} & & & ✓ & & & & & ✓ \\ \hline
**Proposed** & \multicolumn{6}{c}{✓} & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \multicolumn{6}{c}{**(SARNC)**} & & & & & & & & & & \\ \hline \hline \end{tabular}   
* From the above survey, it is concluded that:
* Most of the approaches involve chaos-based and/or hybrid techniques for key generation and encryption. However, of the approaches are limited to academic interest rather than real-world application, because of problems such as insufficient security analysis, flawed design methodology, and low efficiency. * Few research papers focus on key generation with minimal or no key strength analysis. * None of the researchers provided a holistic end-to-end solution ensuring the security and reliability of data at rest and during transmission. To overcome these findings, we propose a comprehensive security solution comprised of the following key points:
* The scope of our work focuses on AES, since it is widely used in the industry because of its versatility and ease of use. * Multi-layered architecture comprising key generation using neural key exchange protocol from the shared secret key. * Improving nonlinearity of subkey using the Khazad function. * Combining encryption and encoding in a single step to provide secure and reliable data transfer. * Detailed analysis comprising statistical, differential, and key strength analysis.
# Reader-Tag Commands via Modulation Cutoff Intervals in RFID Systems +
## 4 Modulation Cutoff Intervals Process
### Counting Protocols  
The MCI process can also be utilized in counting protocols in which the actual IDs of the tags are not necessary. The main concern of counting protocols is the number of single reply slots, which indicates the number of tags. Therefore, MCI can be applied to indicate three commands: NACK (indicating multiple tag reply slots), ACK (indicating a single reply slot), and no reply (empty slot). Consequently, the same MCI process circuit (shown in Figure 7) can be used to indicate these three commands. After initializing the tags to start the counting process, in any slot:  
* If the preamble (a standardized bit pattern that is sent by the tags before sending their data) is received error-free in a given slot, the reader stops its CW for seven clock cycles to indicate a single slot.
* If the preamble is received incorrectly, the reader stops its CW for nine clock cycles to indicate a collision slot.
* Finally, if the time gap in the standard passed with no tags replying, the reader stops its CW for eleven clock cycles to indicate an empty slot.  
In the MCI process, the tag reaction is adaptive based on the protocol that is under execution. For example, as shown above, stopping the CW for seven clock cycles may indicate a collision slot in anti-collision protocols or a single slot in counting protocols. Another advantage of the MCI process is its compatibility with explicit (traditional) commanding if needed. For example, in some protocols, the reader may send commands with specific data to the tags, not only the ones illustrated in the figure above. In such a case, the MCI process circuit can dedicate a specific cutoff interval, for example, a cutoff for 15 clock cycles, to inform the tag to expect an explicit data/command from the reader, as shown in Figure 9.  
Figure 8: Three different scenarios of reader commands in anti-collision protocols with MCI process.
## 4 Conclusions  
Accurately predicting the stiffness degradation of wind turbine blades is crucial for ensuring their long-term reliability and reducing high experimental costs. This study proposes an effective method to predict the stiffness degradation of wind turbine blades  
Figure 6: Ablation experiments. (**a**) Comparison of prediction results under Pinball-MAPE and MAPE loss functions. (**b**) Comparison of prediction results using original data and data augmentation.  
by improving the N-BEATS model, and its superior performance is validated through multiple experiments.  
First, the N-BEATS basic block structure was improved, and the Pinball-MAPE loss function was introduced to make the model more suitable for the stiffness degradation prediction task in this study. In addition, a dataset was constructed based on the stiffness degradation model and used for model validation. Compared to other well-established deep learning models, the improved N-BEATS demonstrated the best prediction accuracy. Meanwhile, partial data re-training was conducted on unseen sequences to verify the model's generalization capability under unknown working conditions.  
Second, this study employs two data augmentation methods--time series combination and random noise injection--that successfully expand the dataset and effectively enhance the model's generalization ability. This method effectively addresses the common issue of data in wind turbine blade testing data, allowing the model to adapt to diverse data environments and tackle challenges posed by limited original data.  
In engineering applications, the proposed model, combined with historical blade stiffness degradation data and early-stage experimental data, enables the prediction of the subsequent stiffness evolution of wind turbine blades. This allows for the assessment of whether the blades still meet operational requirements. The proposed approach reduces the need for physical testing during blade evaluation, thereby lowering assessment costs and improving engineering efficiency. Furthermore, this method can be extended to the fatigue life prediction of composite materials, providing data-driven decision support for structural health monitoring. It is applicable to wind energy, aerospace, and other composite material structures requiring long-term reliability assessment.  
Conceptualization, S.Y. and J.G.; methodology, S.Y.; software, S.Y.; validation, S.Y., Y.Y., J.Z. and L.M.; formal analysis, Y.Y.; investigation, J.G.; data curation, J.Z.; writing--original draft preparation, S.Y.; writing--review and editing, S.Y., J.G., Y.Y. and L.M.; visualization, S.Y.; supervision, J.G.; project administration, Y.Y. and J.Z.; funding acquisition, J.G. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
# Research on Two-Dimensional Digital Map Modeling Method Based on UAV Aerial Images
## 4 Experimental Validation
### Image Stitching  
The source images captured by drones contain moving objects, such as individuals riding electric bicycles and cars. Representative images were selected for stitching comparison, with Figure 9 illustrating the results of the image stitching process. Specifically, Figure 10a-c depicts the results of image stitching using traditional energy functions, while Figure 10d-f shows the results obtained using the improved the novel energy function. It is evident from Figure 10a-c that the results of image stitching with traditional energy functions exhibit noticeable illumination variations and ghosting artifacts at their edges. In contrast, the use of the improved the novel energy function for image stitching effectively addresses the illumination, exposure, and ghosting issues that traditional algorithms could not resolve. Subsequently, SNR analysis was conducted on the image stitching results, revealing SNR values of 13.1504, 12.7692, and 11.9154 for Figure 10a-c, and SNR values of 36.2802, 35.9015, and 36.3165 for Figure 10d-f. The average SNR of the stitched images  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Extraction** & **Coarse Matching** & **Fine Matching Point** & **Time Consumption** & **Registration** \\
**Algorithm** & **Point /Pairs** & **/Pairs** & **(Seconds)** & **Accuracy Rate (\%)** \\ \hline SIFT & 403 & 236 & 4.861 s & 58.6\% \\ SURF & 617 & 378 & 4.282 s & 61.3\% \\ C-SURF & 1382 & 1092 & 7.153 s & 79.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of feature registration results among the three algorithms.  
Figure 9: The results of the feature matching: (**a**) SIFT feature registration results, (**b**) SURF feature registration results, (**c**) C-SURF feature registration results.  
based on the novel energy function exceeds 36, indicating excellent image quality and laying a solid foundation for target extraction tasks based on panoramic images.  
Therefore, the image stitching method proposed in this paper, which is based on the improved the novel energy function, not only resolves the ghosting issues present in the stitching process of sequential images captured by drones but also addresses exposure problems. This provides a strong foundation for the subsequent production of two-dimensional digital maps.  
Figure 10: Image stitching results: (**a–c**) the results of image stitching using traditional energy functions; (**d–f**) the results obtained using the improved novel energy function; (**g**) the overall fusion result graph based on the new improved energy function.
# Multi-Layer Feature Fusion-Based Community Evolution Prediction
## 4 Experiment
### Parameter Selection  
MCEP has two parameters, the number of community roles \(K\) and the number of community mesoscopic features \(L\). Obviously, choosing an appropriate number of roles and community mesoscopic features can effectively improve the effect of the algorithm. When the number of roles is too small, the algorithm cannot completely divide the community roles in the community, which will reduce the effect of the algorithm. When the number of roles is too large, the algorithm may easily divide the roles of the nodes incorrectly, and the number of mesoscopic features. Therefore, an appropriate number of roles and mesoscopic features are very important for the algorithm.  
This section studies the influence of the number of roles and the number of community mesoscopic features on the MCEP. The datasets used are DBLP and ia-reality-call. The results of the experiment are shown in Tables 4 and 5.  
As shown in Tables 4 and 5, we can see that with the increase of \(K\) and \(L\), the effect of the MCEP algorithm presents a gradual upward trend on the average. From the perspective of the number of roles, when K is 5, the MCEP algorithm can achieve the best effect. When K is greater than 5, the effect of MCEP increases slowly. Therefore, the value of K in the subsequent experiments in this paper is 5. From the perspective of the number of community mesoscopic features, when the number of roles is fixed at 5, the best community mesoscopic features on the two datasets are slightly different. On the DBLP dataset, when \(L=20\), the prediction effect is the best. On the ia-reality-call dataset, when \(L=15\), the prediction effect is the best. Regardless of the dataset, when the number of mesoscopic features  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Event**} & \multicolumn{4}{c}{\(K=3\)} & \multicolumn{4}{c}{\(K=5\)} & \multicolumn{4}{c}{\(K=7\)} \\ \cline{2-11}  & \(L=5\) & \(L=10\) & \(L=15\) & \(L=20\) & \(L=5\) & \(L=10\) & \(L=15\) & \(L=20\) & \(L=5\) & \(L=10\) & \(L=15\) & \(L=20\) \\ \hline Survive & 94.90\% & 96.29\% & 97.10\% & 97.05\% & 96.94\% & 97.21\% & 98.18\% & 97.99\% & 97.15\% & 97.30\% & 97.82\% & 97.79\% \\ Diss  
is large, the effect of MCEP is better. This may be because the community mesoscopic features involve complex relationships among communities, so MCEP needs a larger value to capture the community mesoscopic features. In the subsequent experiments, the number of features between communities is selected from 15 and 20.  
From the perspective of the prediction accuracy of a single community event, the number of roles and the number of community mesoscopic features that make the evolutionary event achieve the best results are different in different events and different datasets, and there is no obvious rule. This shows that different community evolution events have their unique characteristics, so it is difficult to find a fixed pattern that can achieve the best results in all events.
## 6 Discussion
### Dynamic Modelling
#### 6.2.3 Load Cell Velocity  
As the load cell velocity is increased, the initial inertial force recorded in the load vs. displacement graphs increases. This was expected as the greater the velocity of the load sphere, the greater the amount of kinetic energy transferred from the moving load sphere to the stationary concrete encasing, as kinetic energy is directly proportional to the square of velocity. The equation below represents the relationship between kinetic energy, mass and velocity.  
\[E_{k}=\frac{1}{2}mv^{2}\]  
Furthermore, the greater the velocity of the load cell, the greater the resisted shear force of the bolt and its respective displacement at the bolt's ultimate stress. This occurs as the bolt must resist a greater force, due to an increased input.  
As the load cell velocity increases, the absorbed energy also increases. This is expected as kinetic energy is proportional to the square of velocity applied, thus, if the velocity is doubled, the absorbed energy is theoretically quadrupled. For the 28 mm diameter bolt,  
Figure 25: Failure of dynamically loaded bolt.  
with an applied load cell velocity of 200 mm/s, 13.3 kJ of energy is absorbed, whilst at a load cell velocity of 400 mm/s, 43.3 kJ of energy is absorbed.  
\[\frac{\textit{Absorbed Energy}\ v_{\mathrm{=400\ mm/s}}}{\textit{Absorbed Energy}\ v_{\mathrm{=200\ mm/s}}}=\frac{43.3\ kJ}{13.3\ kJ}=3.26\]  
The results show that by doubling the velocity of the load cell, the absorbed energy is marginally less than the expected quadrupled value. This was identified due to material damping, which causes a fraction of the energy to dissipate rather than being transferred as kinetic energy upon impact loading
# On-the-Fly Olive Tree Counting Using a UAS and Cloud Services
###### Abstract  
Unmanned aerial systems (UAS) are becoming a common tool for aerial sensing applications. Nevertheless, sensed data need further processing before becoming useful information. This processing requires large computing power and time before delivery. In this paper, we present a parallel architecture that includes an unmanned aerial vehicle (UAV), a small embedded computer on board, a communication link to the Internet, and a cloud service with the aim to provide useful real-time information directly to the end-users. The potential of parallelism as a solution in remote sensing has not been addressed for a distributed architecture that includes the UAV processors. The architecture is demonstrated for a specific problem: the counting of olive trees in a crop field where the trees are regularly spaced from each other. During the flight, the embedded computer is able to process individual images on board the UAV and provide the total count. The tree counting algorithm obtains an \(F_{1}\) score of 99.09% for a sequence of ten images with 332 olive trees. The detected trees are geolocated and can be visualized on the Internet seconds after the take-off of the flight, with no further processing required. This is a use case to demonstrate near real-time results obtained from UAS usage. Other more complex UAS applications, such as tree inventories, search and rescue, fire detection, or stock breeding, can potentially benefit from this architecture and obtain faster outcomes, accessible while the UAV is still on flight.  
U  
A 18 January 2019; Accepted: 3 February 2019; Published: 5 February 2019