esa-sceva/satcom-chunk-collection huggingface dataset:
# LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network
## 5 Discussion  
Despite the demonstrated effectiveness of our proposed method in MFF, we have identified three primary limitations:  
First, our decision map-based paradigm, while computationally efficient, might not perform as robustly as end-to-end approaches in handling complex scenes [26].  
Second, our current post-processing strategy relies on basic morphological operations to refine the decision map. More advanced techniques, such as image matting-based methods [73], can indeed effectively reduce artifacts along fusion boundaries. However, they introduce substantial computational overhead--typically adding 2-3 s per image pair to processing time. This additional cost conflicts with our goal of maintaining computational efficiency. Therefore, we chose lightweight morphological operations as a compromise. While our current post-processing approach generally improves fusion quality, it may occasionally yield suboptimal results in complex local regions with fine details, as illustrated in Figure 13.  
Third, our method is susceptible to high-frequency noise. Because the initial decision map is generated based on a focus measure that is sensitive to local variations, the presence of high-frequency noise in the source images can lead to an erroneous initial decision map, thereby further affecting the final decision map. This sensitivity highlights a need for more robust focus measure mechanisms that are less influenced by noise, or for a pre-processing step to denoise the source images.  
Our work opens several promising directions for future research. We could investigate more sophisticated yet efficient post-processing techniques to further enhance fusion quality in fine-detail regions. Additionally, developing a more noise-robust focus measure or integrating a denoising module could significantly improve the reliability of the decision map generation. Extending our lightweight architecture design principles to other image fusion tasks (e.g., multi-exposure, infrared-visible) might lead to a more general framework for efficient  
Figure 13: Limitations of current decision map post-processing methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & \(Q^{AB/F}\uparrow\) & \(Q_{MI}\uparrow\) & \(Q^{P}\uparrow\) & \(Q_{W}\uparrow\) & \(Q_{E}\uparrow\) & \(Q_{CB}\uparrow\) \\ \hline Group 1 & 0.7578 & 1.1448 & 0.8437 & 0.9363 & 0.9021 & 0.8036 \\ Group 2 & 0.7587 & 1.1460 & 0.8447 & 0.9389 & 0.9055 & 0.8060 \\ Group 3 & 0.7584 & 1.1459 & 0.8443 & 0.9373 & 0.9053 & 0.8057 \\ Group 4 & **0.7588** & **1.1462** & **0.8450** & **0.9400** & **0.9061** & **0.8067** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation quantitative results on the Lytro dataset [46]. Group 1: baseline with only source images; Group 2: with initial decision map; Group 3: with edge maps; Group 4: full model with both components.  
image fusion. Finally, exploring the integration of our method with specific downstream tasks like object detection could significantly boost its practical utility in real-world applications.
## 2 Materials and Methods
### Design of the eSRR  
The eSRR is designed on a 26 \(\upmu\)m SiO2 layer and the overall size of eSRR unit cell is 100 \(\times\) 100 \(\upmu\)m. Material linewidth is 4 \(\upmu\)m. The values of all parameters shown in Figure 1 are described in Table 1. Gap A4 is described as the point at which the tilt of the moving column is at its initial state. Width A1 describes the thickness of the end of the column. A3 is the distance between the eSRR and I-shaped movable column.  
The middle I-shaped metal bar can be moved in vertical and horizontal directions. Figure 1 shows the initial point of the I-shaped metal bar, while the reflection coefficient S11 frequency response for this configuration is shown in Figure 2. At this point, the bar can be moved 6 \(\upmu\)m to the right and 6 \(\upmu\)m to the left with a step size of 2 \(\upmu\)m. The maximum range is equal to 6 \(\upmu\)m because at this point the moving column touches the stationary part of the unit cell. This is the only Y-axis height for which right and left sliding is assumed to be allowed. The movable column can also be moved up and down. Taking the position of the column shown in Figure 3a as a reference, its maximum upward displacement is 16 \(\upmu\)m with a step equal to 2 \(\upmu\)m in the upward Y-axis. The maximum deflection points in the X and Y axes are shown in Figure 3. MEMS technology constructed in this way (for such constraints) could allow even more precise control of the geometry of the unit cell, and thus the selection of parameters such as the resonant frequency with greater accuracy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Denotations** & **Dimensions** \\ \hline A\({}_{1}\) & 12 \(\upmu\)m \\ A\({}_{2}\) & 12 \(\upmu\)m \\ A\({}_{3}\) & 14 \(\upmu\)m \\ A\({}_{4}\) & 12 \(\upmu\)m \\ A\({}_{5}\) & 20 \(\upmu\)m \\ A\({}_{6}\) & 52 \(\upmu\)m \\ A\({}_{7}\) & 32 \(\upmu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions included in Figure 1.  
Figure 1: Geometrical denotations of eSRR unit cell including widths, gaps, and lengths. The orange column is described as the movable I-shaped metal bar.
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 2 Related Work
Due to the great importance of this issue, they have attracted the attention not only of the research community and industry [26, 27, 28, 29] but also of major standardization bodies, such as ETSI [30], in recent years. To our knowledge, there is no cyber range in the literature that includes exercises addressing the occurrence of poisoning attacks against ML-based detection mechanisms. In contrast, one of the proposed exercises in this work introduces this concept using a didatical approach to evangelize trainees about the inclusion of proactive defensive mechanisms to improve the security posture of the system against this type of attack.
## 7 Simulation Results
### Comparison with Existing Works  
Several classical control strategies, such as PD and PID controllers, have been extensively studied in the literature for exoskeleton control. [PERSON] et al. (2018) designed and simulated a PID controller for a lower-limb exoskeleton, achieving good performance for systems with mild nonlinearities. However, these classical PID methods face challenges when applied to more complex exoskeletons with significant nonlinearities, resulting in decreased stability and position control accuracy ([PERSON] et al. (2018). Similarly, [PERSON] et al. (2017) found that their PID controller struggled to maintain stability in the presence of dynamic disturbances during lower-limb rehabilitation ([PERSON] et al. (2021). [PERSON] et al. (2021) introduced an adaptive PID controller enhanced by particle swarm optimization, showing improved performance. However, this approach remains vulnerable to system variations and external disturbances, limiting its robustness ([PERSON] et al., 2017).  
In contrast, our approach based on LMIs provides notable improvements over these classical methods. LMIs offer a robust framework that effectively handles system nonlinearities, uncertainties, and external disturbances. Our simulations demonstrate a significant reduction in position errors compared to classical PID and PD approaches, with improved system performance and stability. Unlike classical controllers, which may struggle under varying conditions, our method offers enhanced dynamic stability and better management of static errors, making it particularly effective for exoskeletons that must operate in complex and unpredictable environments.  
Figure 8: Simulation results obtained via the fourth design method and by applying the classical PID controller proposed in Section 6.2. (**a**) Temporal variation of the angular position error \(\phi\), (**b**) temporal variation of the angular velocity error \(\dot{\phi}\), (**c**) temporal evolution of the applied PID controller, and (**d**) the applied lumped disturbance \(\Delta_{i}\).  
The LMI-based method offers a systematic, optimization-driven approach to control design, ensuring reliable performance even under significant model uncertainties and external disturbances. Its robustness is evident from simulation results, consistently surpassing classical PID and PD controllers in position control accuracy and stability. By addressing the limitations of traditional methods, this approach enhances system stability and performance, showcasing practical advantages for future experimental applications in exoskeleton systems.
## 2 Related Works
### Spatial Pyramid Pooling  
The spatial pyramid pooling model (SPP) can maintain spatial information by pooling in local spatial bins. The size of these spatial bins is proportional to the image size, so the number of bins is fixed regardless of the image size. This is in contrast to the sliding window pooling of the previous deep networks, where the number of sliding windows depends on the input size.  
Generally, to make deep networks adapt to input images of any sizes, the spatial pyramid pooling uses multi-level spatial bins to substitute max-pooling layers or average-pooling layers, which has been shown to be robust. The structure of SPP is shown in Figure 1. In each spatial bin, the response of filters is randomly pooled. In SPP, the output of each pooling level will eventually be exported to the full connected layer in parallel. For example, if there are \(N\) bins, the outputs of SPP are the \(kN\)-dimensional vectors with a fixed length, where \(k\) denotes the number of filters used in the last convolution layer. The vectors in fixed dimensions are the inputs of fully connected layers.  
Besides, if the size of the feature maps from the previous layer is \(a\times a\), at the pyramid level of the size of \(n\times n\) bins, the size and stride of the filters are respectively calculated by:  
\[\text{size}=\lceil a/n\rceil,\text{ stride}=\lfloor a/n\rfloor \tag{1}\]  
where \(\lceil\cdot\rceil\) and \(\lfloor\cdot\rfloor\) are defined as the ceiling operation and floor operation, respectively.
## 2 The Corrosion Mechanism of Reinforced Concrete  
The corrosion of reinforced concrete occurs when chloride ions from the natural environment penetrate through the concrete's pores, thereby destroying the protective layer on the steel reinforcement [21]. Generally, chloride ions enter concrete through free diffusion, hydrostatic pressure, and capillary absorption [22]. Once chloride ions penetrate the concrete cover and reach the steel surface, localized pitting corrosion is likely to occur [23]. This is because the surface of the steel is not perfectly smooth; hence, chloride ions are more likely to adhere to porous or recessed areas, leading to corrosion [24], as illustrated in Figure 1.  
The corrosion process of steel reinforcement can be described as a microcell reaction occurring under the combined effects of water and air [25]. Various electrochemical reactions take place during corrosion, but the fundamental requirement is the presence of both ion and electron pathways [26]. During this process, ferrous ions (Fe\({}^{2+}\)) produced at the positive electrode enter the solution and react with chloride ions to form ferrous chloride (FeCl\({}_{2}\)) (Equation (1)). Electrons then travel through the steel to the negative electrode, where they react with water (H\({}_{2}\)O) and oxygen (O\({}_{2}\)) to form hydroxide ions (OH\({}^{-}\)) (Equation (2)). Finally, hydroxide ions react with ferrous chloride to form the precipitate ferrous hydroxide (Fe(OH)\({}_{2}\)) (Equation (3)). Figure 1 illustrates the schematic of the corrosion reaction process of steel.  
\[\mathrm{Fe}-2\mathrm{e}^{-}\rightarrow\mathrm{Fe}^{2+} \tag{1}\]  
\[\mathrm{Fe}^{2+}+2\mathrm{Cl}_{2}\rightarrow\mathrm{FeCl}_{2} \tag{2}\]  
\[\mathrm{O}_{2}+2\mathrm{H}_{2}\mathrm{O}+4\mathrm{e}^{-}\rightarrow 4\mathrm{OH}^{-} \tag{3}\]  
\[\mathrm{FeCl}_{2}+2\mathrm{OH}^{-}\rightarrow\mathrm{Fe(OH)}_{2}+2\mathrm{Cl} ^{-} \tag{4}\]  
During the corrosion process, chloride ions (Cl\({}^{-}\)) continuously participate in the electrochemical reactions, accelerating the transformation of ferrous ions (Fe\({}^{2+}\)) into hydroxide precipitates, without being consumed themselves. Therefore, as the concentration of chloride ions increases, the electrochemical process speeds up, with chloride ions acting as a catalyst [27].
# Meta-Learning for Mandarin-Tibetan Cross-Lingual Speech Synthesis
## 6 Evaluations and Analysis
### Multilingual Speech Synthesis Evaluation
#### 6.1.1 Objective Evaluation  
We evaluated the Mel-cepstral distortion (MCD) in the objective evaluations. The MCD represents the Mel Frequency Cepstral Coefficients (MFCCs) error calculated from predicted speech samples and natural speech samples. Assuming that the MFCCs of a frame are expressed as \(y\) and the predicted MFCCs are expressed as \(\hat{y}\), the MCD of the frame can be calculated as follow:  
\[\text{MCD}(y;\hat{y})=\|y-\hat{y}\|_{2} \tag{3}\]  
where \(\|\cdot\|_{2}\) represents \(L2\) norm.  
The MCD of different seq2 seq TTS models on the test set is shown in Table 1. We can find that the MCD values of synthesized Mandarin speech of the Mandarin-Tibetan cross-lingual models, including the shared-MT, the separate-MT and the meta-MT, increase by 0.119, 0.026 and 0.019 compared with seq2 seq-M, respectively. For Tibetan speech synthesis, the findings can be analyzed that the baseline of Mandarin-Tibetan cross-lingual models, including the shared-MT and the separate-MT, help to improve the voice quality of the synthesized Tibetan speech. The separate-MT has higher modeling accuracy compared with the shared-MT. This may be due to the fact that the separate-MT alleviates the mutual interference of inputs from different languages. Furthermore, the meta-MT seems slightly better than separate-MT for Mandarin and Tibetan speech synthesis. It fulfills our expectations as meta-MT should be more flexible.
# Algorithms for Optimal Power Flow Extended to Controllable Renewable Systems and Loads
## 2 Materials: Optimal Power Flows Considering Uncertainty Cost Functions  
### Optimization Problem Statement  
Optimal power flow is, as its name implies, an optimization problem defined as follows [17; 18]:  
\[\text{Minimize}\;_{x,u}\;f(x,u) \tag{1}\] \[\text{Subject to:}\;h(x,u)=0\] (2) \[g(x,u)\leq 0\;, \tag{3}\]  
where u is a set of decision variables and x is a set of dependent variables. F is the scalar objective function, h represents a set of equality constraints and g a set of inequality constraints.
# Dynamic Behavior Analysis and Stability Control of Tethered Satellite Formation Deployment
## 4 Stable Deployment Laws of Tether Release Rate and Tether Tension Control
### Tether Release Rate Control
#### 4.1.1 Fixed Angle \(\theta\)  
The system's in-plane angle is assumed to be a fixed angle of nonrotating motion. Corresponding to actual conditions, a Global Positioning System (GPS) rotates around the earth at a fixed angle in order to produce a stable state. \(\theta=\theta_{0},\ \theta^{\prime}=\theta^{\prime\prime}=0\) are substituted into Equation (26), which can be written as follows.  
\[\frac{l^{\prime}(\
u)}{l(\
u)}=\frac{e\sin\
u}{1+e\cos\
u}-\frac{3\sin 2 \theta_{0}}{4(1+e\cos\
u)} \tag{27}\]  
The log ratio of tether length, \(\ln[l(\
u)/l_{0}(\
u)]\), can be obtained by integrating Equation (27).  
Figure 6 shows that Equation (27) achieves a unified analytic solution when \(\theta_{0}=k\pi/2\) (\(k\) is an integer).  
Father length is positively related to orbital eccentricity, and the abscissa corresponding to the highest point is \(\
u=(2k+1)\pi\).  
Figure 6: Log ratio of tether length versus \(\
u,\theta=const\).
# Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement
## 4 Experiments
### Performance Evaluation of Detection on Vishrone Datasets  
Hyper-parameter k. In this paper, one hyper-parameters \(k\) is used to select the candidate positive and negative samples from each pyramid level. As shown in Table 3, we use different values of \(k\) to train the detector. We observe that too large \(k\) will cause lots of low-quality candidates, which decreases the AP, and too small \(k\) will result in insufficient samples. Overall for the visdrone2020 dataset, the best performance can be obtained when k is taken as 9.  
Comparison. As shown in Table 4, we compare our proposed method to other famous algorithms on the Visdrone2020 benchmark. Following the benchmarks, we evaluate the performance of our method with the \(AP_{IoU=0.50:0.05:0.95}\), \(AP_{IoU=0.50}\), \(AP_{IoU=0.75}\), \(AR_{max=1}\), \(AR_{max=10}\), \(AR_{max=100}\) and \(AR_{max=500}\) scores, which are designed in Visdrone2020. Specifically, \(AP_{IoU=0.50:0.05:0.95}\) is computed by averaging over all 10 IoU thresholds over all and of across all categories, which is then used as the primary metric for ranking algorithms. \(AP_{IoU=0.50}\) and \(AP_{IoU=0.75}\) are computed at the single IoU thresholds of 0.5 and 0.75 over all categories, respectively. The \(AR_{max=1}\), \(AR_{max=10}\), \(AR_{max=100}\) and \(AR_{max=500}\) scores are the maximum recalls given of 1, 10, 100 and 500 detections per image, averaged over all categories and IoU thresholds.  
Figure 10: Visualization of the super-resolved features generated by weight-GAN.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(k\) & **5** & **7** & **9** & **11** & **13** & **15** \\ \hline \(AP\) & 40.54 & 41.98 & **42.12** & 41.09 & 40.88 & 39.48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table analysis of different values of hyper-parameter k on the Visdrone2020 car class.  
Compared with the state-of-the-art CornerNet method, our method that uses the Sample Balance Strategy and weight-GAN Sub-network performs better and improves the AP by 0.74%. In terms of sample selection strategies meanwhile, we compare our method to the state-of-the-art FCOS+ATSS method. Our methods, both using SBS only, as well as using SBS and WGAN, improve the AP by 0.03% and by 0.78% respectively. This proves the effectiveness of the SBS method in the detection of objects from drones. Besides, when compared with Perceptual GANs, our method weight-GAN also achieves a better performance. The results of our detection method are shown in Figure 11.  
For object detection tasks in UAV scenarios, there are many small targets whose feature maps usually are not easy to be distinguished by the following regression and classification branches. In order to improve the detector's performance, we propose the weight-GAN method to enhance the local feature of small targets and introduce the sample balance strategies.
# Simplified Plastic Hinge Model for Reinforced Concrete Beam-Column Joints with Eccentric Beams
## 4 Application of Simplified Plastic Hinge Model
### Comparison of Predictions to Test Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multicolumn{2}{c}{**Test Results**} & \multicolumn{2}{c}{**Predictions with**} & \multicolumn{2}{c}{**Predictions with**} & \multicolumn{2}{c}{**Predictions with**} & \multicolumn{2}{c}{**Predictions with**} \\  & \multicolumn{2}{c}{**Results**} & \multicolumn{2}{c}{**ACI 318-19**} & \multicolumn{2}{c}{**ACI 352R-02**} & \multicolumn{2}{c}{**NZS 3101-2006**} & \multicolumn{2}{c}{**Eurocode 8**} \\ \cline{3-10}  & & \(\delta_{T}\) (\%) & \(\delta_{u}\) (\%) & \(\delta_{T}\)/\(\delta_{u}\) & \(\delta_{u}\) (\%) & \(\delta_{T}\)/\(\delta_{u}\) & \(\delta_{u}\) (\%) & \(\delta_{T}\)/\(\delta_{u}\) & \(\delta_{u}\) (\%) & \(\delta_{T}\)/\(\delta_{u}\) \\ \hline \multirow{3}{*}{[PERSON] et al. [25]} & JXO-B1 & 4.68 & 4.55 & 1.03 & 3.83 & 1.22 & 4.55 & 1.03 & 4.55 & 1.03 \\  & JXO-B5 & 4.24 & 1.28 & 3.31 & 3.05 & 1.39 & 3.90 & 1.09 & 4.65 & 0.91 \\ \hline \multirow{3}{*}{[PERSON] et al. [30]} & JE-0 & 3.39 & 4.11 & 0.82 & 2.55 & 1.33 & 4.11 & 0.82 & 4.11 & 0.82 \\  & JE-55 & 3.50 & 1.20 & 2.91 & 2.11 & 1.66 & 3.06 & 1.14 & 4.11 & 0.85 \\  & JE-55S & 3.50 & 2.74 & 1.28 & 3.85 & 0.91 & 4.11 & 0.85 & 4.55 & 0.77 \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] [31]} & S0 & 6.03 & 4.79 & 1.26 & 4.50 & 1.34 & 4.79 & 1.26 & 4.79 & 1.26 \\  & S50 & 5.02 & 4.21 & 1.19 & 4.50 & 1.11 & 4.79 & 1.05 & 4.79 & 1.05 \\  & W0 & 6.01 & 4.67 & 1.29 & 5.10 & 1.18 & 4.96 & 1.21 & 4.96 & 1.21 \\  & W75 & 6.01 & 5.10 & 1.18 & 5.10 & 1.18 & 5.03 & 1.19 & 4.96 & 1.21 \\  & W150 & 5.03 & 5.53 & 0.91 & 5.36 & 0.94 & 5.25 & 0.96 & 4.96 & 1.01 \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] [32]} & JC & 5.52 & 4.92 & 1.12 & 5.22 & 1.06 & 4.92 & 1.12 & 4.92 & 1.12 \\  & JE & 5.20 & 5.51 & 0.94 & 5.29 & 0.98 & 5.14 & 1.01 & 4.92 & 1.06 \\ \hline \multirow{3}{*}{[PERSON] et al. [34]} & C2 & 2.96 & 2.97 & 0.99 & 2.00 & 1.48 & 2.97 & 0.99 & 2.97 & 0.99 \\  & E0 & 1.95 & 1.39 & 1.40 & 1.49 & 1.31 & 1.86 & 1.05 & 2.78 & 0.70 \\ \cline{1-1}  & E2 & 2.18 & 1.39 & 1.57 & 1.61 & 1.35 & 2.00 & 1.09 & 2.97 & 0.73 \\ \cline{1-1}  & E5 & 2.70 & 1.37 & 1.98 & 1.81 & 1.49 & 2.23 & 1.21 & 3.28 & 0.82 \\ \hline \multirow{3}{*}{Mean COV} & - & 1.45 & & 1.25 & & 1.07 & & 0.97 \\ \cline{1-1}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of deformation capacity between test results and predictions.