esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Focus Image Fusion Based on Decision Map and Sparse Representation
###### Abstract  
As the focal length of an optical lens in a conventional camera is limited, it is usually arduous to obtain an image in which each object is focused. This problem can be solved by multi-focus image fusion. In this paper, we propose an entirely new multi-focus image fusion method based on decision map and sparse representation (DMSR). First, we obtained a decision map by analyzing low-scale images with sparse representation, measuring the effective clarity level, and using spatial frequency methods to process uncertain areas. Subsequently, the transitional area around the focus boundary was determined by the decision map, and we implemented the transitional area fusion based on sparse representation. The experimental results show that the proposed method is superior to the other five fusion methods, both in terms of visual effect and quantitative evaluation.  
multi-focus image fusion; low-scale images; decision map; sparse representation; transitional area +
Footnote †: journal: Applied Sciences
## 5 Experimental Results
#### Performance Results
Figure 9 shows that the processing time of the proposed RFO-SVM method was significantly lower compared with FRAGMENT, DMA, KT-DDE, and SS-FONs, by 57.81%, 55.84%, 62.04%, and 66.15%, respectively. On comparing with DBE-GAM, PROADAPT, FTree, MGRM, DSGA, and VFAR, the proposed method demonstrated reductions in processing times of 68.01%, 64.54%, 57.59%, 48.24%, 58.23%, and 54.18%, respectively. Figure 8: Accuracy. Figure 7: Total allocation cost. Figure 10 shows that the proposed RFO-SVM method achieved substantial reductions in terms of total transmission costs when compared with FRAGMENT, DMA, KT-DDE, SS-FONs, DBE-GAM, PROADAPT, FTree, MGRM, DSGA, and VFAR, of 50.76%, 44.26%, 42.88%, 26.82%, 31.38%, 33.96%, 43.91%, 42.02%, 25.82%, and 51.54%, respectively.
# Single-Pixel Imaging Based on Enhanced Multi-Network Prior
## 4 Result and Discussion
### Results of EMNP on SPI System  
We have built a SPI system in the laboratory. In order to meet the requirements of hardware, we developed a binary version of EMNP-SPI. The overall architecture of the network remains unchanged during the training of the binary sampling matrix. Since the  
Figure 10: This is a figure of comparison of different structures. (**a**) PSNR results; (**b**) SSIM results.  
Figure 9: This is a figure of reconstruction quality of different methods. (**a**) PSNR of different methods on set5; (**b**) SSIM of different methods on set5.  
DMD can only input 0 and 1, the binary sampling matrix is converted into a measurement matrix that can be used by the DMD. The specific operation is to convert 1 and \(-\)1 to 0, respectively, and the measurement value Y is obtained by subtracting the measured values of the two measurement matrices.  
We image the target letters \"N\" and \"U\" on the resolution board at measurement rates of 0.01, 0.05 and 0.098. The imaging resolution is 64 \(\times\) 64. The reconstruction result of TVAL3 and EMNP-SPI is shown in Figure 11. We can find that the image reconstructed by EMNP-SPI is clearer. The image quality is better than that of TVAL3, which is consistent with the simulation results.
## 3 System Framework  
In this section, we present the framework for the proposed edge caching scheme. Figure 1 depicts the architecture of the proposed framework, comprised of several entities at different levels, where MAN servers are deployed in clusters, providing computation and storage resources to enable caching at the network edge. A MAN server can process requests directly from its local cache, neighbor MANs, ROC, or the central cloud.
## 4 Comparative Study of the Different Approaches to Obtain the VSC
As has been previously stated, when CIE standard classification is available, the sky luminance distribution can be obtained from Equation (11), and Equations (7)-(10) can be applied to calculate \(\rm L_{dh}\), \(\rm L_{dv}\) and, therefore, VSC. In this work, the CIE standard sky type in Burgos was determined between September 2016 and January 2019, following the procedure described in a previous paper [26]. The frequency of occurrence (FOC) of each sky type during the period under study is shown in Figure 5. As can be seen, all types of CIE standard skies can be found in Burgos. The most frequent sky type was sky 7, (partly cloudy sky, no gradation towards zenith, brighter circumsolar region), with a FOC of almost 13%. Sky types 12, 13, and 11, corresponding to CIE standard clear sky categories, had FOCs of around 11%, similar to the FOC of sky type 8 (partly cloudy sky, no gradation towards zenith, distinct solar corona). Only a category of cloudy sky, sky type 4 (overcast, moderately graded, and slight brightening towards the sun), had a FOC close to 10%, while the FOC values of the other overcast categories were very low. VSC values calculated from the different procedures were compared, taking the VSC values obtained by Equation (6) as a reference. Both the RMSD and the MBD parameters are shown in Table 6. As can be seen, for the different vertical surfaces facing the cardinal orientations, the statistical indices ranged between 23% and 32% for the RMSD and between \(-\)1% and 16% for the MBD. Both procedures underestimated the VSC, as shown by the negative MBD values. The highest discrepancies between the different approaches were for the east orientation. This observation agreed other works, where the results of different approaches for VSC calculation varied with the different surface orientations [15]. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**VSC**} & \multicolumn{2}{c}{**RMSD (\%)**} & \multicolumn{2}{c}{**MBD (\%)**} & \multicolumn{2}{c}{**RMSD (\%)**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Sky-Scanner**} & \multicolumn{2}{c}{**CIE**} \\ \hline South surface & 24.46 & \(-\)8.47 & 25.56 & \(-\)11.25 \\ North surface & 23.46 & \(-\)4.48 & 23.53 & \(-\)5.56 \\ East Surface & 31.93 & \(-\)15.51 & 29.85 & \(-\)15.38 \\ West Surface & 22.99 & \(-\)1.14 & 24.84 & \(-\)2.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: RMSD and MBD results from the comparison between the VSC values calculated with the different approaches. Figure 5: Frequency of occurrence (FOC, %) of CIE standard skies in Burgos, Spain, between September 2016 and January 2019.
# Spatial Multiplexing Holography for Multi-User Visible Light Communication
## 2 Design Principle of Spatial Multiplexing Hologram
### Spatial Multiplexing Hologram Using OAM for Multi-User Secure Communication
If an OAM key other than \(l_{1}\) or \(l_{2}\) is used to decode the optical field, the intensity would only retain the crosstalk part, indicating a failure to decode properly. This approach underscores the importance of precise OAM mode alignment in enhancing the efficiency and security of VLC systems.
# Study on Compressive Strength and Water Permeability of Steel Slag-Fly Ash Mixed Permeable Brick
## 4 Conclusions  
The test results showed that the water-cement ratio, steel slag, and fly ash content have significant effects on the compressive strength and water permeability of the permeable brick. Considering the compressive strength and water permeability, the best mix proportion of permeable bricks was 6.5% for steel slag, 30% for fly ash, and 25% for target porosity.  
In this study, the cement content was fixed, which is 10%. The fly ash and steel slag were replaced with cement. With the increase of substitution rate, the compressive strength of permeable brick decreases, but the increase in permeability was significant.  
The compressive strength of permeable brick could be effectively improved by increasing the steel slag content. When the content of steel slag increased from 5% to 6.5%, the corresponding 28 days curing compressive strength increased from 21 MPa to 26 MPa. When the content of steel slag was larger than 6.5%, the compressive strength of permeable brick did not change significantly. The content of fly ash was negatively correlated with the strength of permeable bricks. As the content of fly ash increased, the strength of permeable bricks decreased.  
The steel slag content had little effect on the water permeability coefficient, and the water permeable property was stable when the steel slag content was kept at about 6%. The fly ash content had a great influence on the permeability coefficient. The higher the content, the larger the porosity and the better the water permeability.  
[PERSON] and [PERSON] developed methodology, performed the analysis, write the draft manuscript and the revised version. [PERSON] reviewed and commented on the original and revised manuscripts.  
This research received no external funding.  
The authors wish to thank the anonymous reviewers for their valuable suggestions.  
The authors declare no conflict of interest.
## 4 Experimental Results and Discussion
### Research Data  
From April to June 2023, the Petrel-L glider underwent sea trials in the South China Sea. Figure 8 illustrates its area and trajectory during the oceanic experiment, encompassing a longitudinal span from 110\({}^{\circ}\) E to 117\({}^{\circ}\) E and a latitudinal span from 16\({}^{\circ}\) N to 21\({}^{\circ}\) N. Additionally, an inset in the upper left corner displays a deployment photograph of the glider. Throughout the 70-day trial period, the glider functioned without any reported issues, successfully compiling a dataset comprising 1110 individual profiles.  
Figure 9 displays the depth and rudder angle data for the Petrel-L glider over a specific period, corresponding to profiles 480 to 483. As shown, both the depth (indicated by the blue line) and the rudder angle (represented by the red line) fluctuate with gliding time, particularly during the descent and ascent phases when the RRU operates frequently. Prolonged underwater operations can lead to biofouling, the accumulation of RRU errors, and unpredictable disturbances from ocean currents, all of which may induce lateral imbalances in the glider. These imbalances can affect the stability of heading-keeping. Consequently, it is essential to make dynamic predictions for the roll center's imbalances, particularly focusing on predicting rudder angles. By accurately forecasting these angles, the frequency of rudder adjustments can be minimized, thereby significantly enhancing the glider's heading-keeping performance.  
To ascertain the authenticity and reliability of the dataset, several stringent prerequisites must be satisfied before utilizing the raw UG data for estimating the mean rudder angle:  
1. Eliminate the time-series raw data obtained when the buoyancy adjustment unit is operating. Data collected during buoyancy adjustments capture rapid changes that are unrepresentative of the glider's steady-state gliding dynamics. By removing these intervals, the dataset retains only stable conditions, reducing noise and preventing the model from learning patterns that could reduce reliability.
2. Eliminate the raw data during UG's surface positioning and communication periods. During surface positioning, environmental influences such as wave and current impacts cause irregular rudder adjustments. Excluding these segments allows the model to focus on stable underwater data, eliminating inconsistencies that might compromise prediction authenticity.
3. Since the mean rudder angle differs between ascent and descent, process the data for these two phases separately. The hydrodynamic conditions for ascent and descent differ, leading to distinct control requirements for the rudder angle. Treating  
Figure 8: The sea trial area of the Petrel-L underwater glider.  
Figure 9: Changes in rudder angle and depth over time.  
these phases separately ensure that phase-specific patterns are accurately captured, enhancing the model's capacity to provide robust and realistic parameter predictions.
# Evaluation of Adjustment Effects of Highway Guide Signs Based on the TOPSIS Method
## 5 Evaluation Model and Application  
In the evaluation indicator system for highway guide signs, the proximity of each indicator's quantified results to its ideal value directly reflects the effectiveness of guide sign placement. This \"proximity to ideal solution\" characteristic aligns perfectly with the core concept of the TOPSIS method. By calculating the distances between evaluated objects and the positive/negative ideal solutions, TOPSIS can precisely measure how closely guide sign configurations approach the ideal standard, thereby enabling scientific evaluation of their effectiveness. Consequently, this study employs the TOPSIS method to construct the evaluation model, ensuring the objectivity and accuracy of assessment results.
## 3 Architecture of the Platform and Related Software and Hardware Aspects
### Implementation Aspects and Associated Configuration and Monitoring Processes  
As already mentioned in the introduction, the software of the MANET platform has been implemented in Python on standard Raspberry Pi-based commodity hardware running the Linux OS, using one board computer per network node. Here, we discuss a number of implementation aspects relating to wireless communications and networking, configuration and monitoring of the platform, and hardware capabilities. We also comment on a number of MANET routing protocols that have been implemented on the platform for use in experiments.
# Improved Trimming Ant Colony Optimization Algorithm for Mobile Robot Path Planning
## 2 Traditional Ant Colony Optimization (ACO) Algorithm
### State Transition Probability  
Let the total number of ants in the colony be \(M\), the number of nodes be \(n\), and the total number of iterations be \(T\). Ant \(k\) (\(k\) = 1, 2, \(\ldots\), \(M\)) determines its next node to visit based on the pheromone concentration on the paths between the nodes. At the initial time step, the pheromone concentration on the paths between all nodes is set to the same value, denoted as \(\tau_{ij}(t)=\tau_{0}\). Let \(P_{ij}^{k}(t)\) denote the probability that ant \(k\) moves from node \(i\) to node \(j\) at the \(t\)-th iteration.The formula for \(P_{ij}^{k}(t)\) is given by Equation (1).  
\[P_{ij}^{k}(t)=\begin{cases}&\frac{[\tau_{ij}(t)]^{*}[\tau_{ij}(t)]^{\beta}}{ \sum_{j\in\epsilon_{k}}[\tau_{ij}(t)]^{*}[\tau_{ij}(t)]^{\beta}}&,j\in a_{k}\\ 0&,\;\textit{otherwise}\end{cases} \tag{1}\]  
\[\eta_{ij}(t)=\frac{1}{d_{ij}} \tag{2}\]  
In Equation (1), \(\tau_{ij}(t)\) represents the pheromone concentration retained on the path from node \(i\) to node \(j\) at the \(t\)-th iteration, and \(\eta_{ij}(t)\) denotes the heuristic information from node \(i\) to node \(j\) at the \(t\)-th iteration. The formula for \(\eta_{ij}(t)\) is given by Equation (2), where \(d_{ij}(i,j=1,2,\ldots n)\) represents the Euclidean distance between node \(i\) and node \(j\). The set \(a_{k}\) denotes the collection of node positions that ant \(k\) may potentially move to from node \(i\). The parameter \(\alpha\) is the pheromone heuristic factor, which reflects the importance of pheromone concentration in path selection, and the ants tend to choose the path with large pheromone concentration; \(\beta\) is the expectation heuristic factor, indicating the significance of heuristic information in path selection, and the ants tend to choose nodes that are close to the goal point.
# Neighborhood Deviation Attack Against In-Context Learning
## 4 Proposed Algorithm  
### Method Overview  
We propose a novel text-only MIA method targeting ICL examples of LLMs. The core objective is to determine whether a specific sample is included in the context examples used for the LLM ICL, thereby revealing privacy vulnerabilities in contextual information processing. Compared with traditional approaches, our method ingeniously integrates membership inference with neighbor sample generation techniques. It relies solely on model outputs without requiring access to internal parameters, significantly improving attack stability and accuracy. This provides a powerful tool for assessing the privacy risks of LLMs in practical applications.  
The method consists of four main steps: generation of the neighbor prefix, predictive completion, calculation of the metric deviation, and sample membership determination. These steps are detailed in Sections 4.2-4.5. The detailed algorithm is provided in Algorithm 1. To make our method easier to understand, a flowchart capturing our algorithm process is shown in Figure 2.  
```
0: target sample \(\mathbf{S}_{\text{target}}\), LLM \(f_{\text{target}}\), attack model \(f_{\text{attack}}\), number of neighbors \(N\), metric calculation function \(f_{m},m\in\{\text{Similarity},\text{BLEU},\text{PPL},\text{ROUGE-L}\}\)
0: Membership status (Member or Non-Member)
1: Split the target sample into prefix \(\mathbf{p}\) and suffix
2:\(\{\mathbf{p}^{1},\mathbf{\hat{p}^{2}},\ldots,\mathbf{p}^{N}\}\gets neighbor \_generate(\mathbf{p})\triangleright\)Generate neighbor prefixes
3:for each prefix \(\mathbf{p}\) and \(\mathbf{\hat{p}^{j}},i=1,\ldots,N\)do
4:for each metric \(m\in\{\text{Similarity},\text{BLEU},\text{PPL},\text{ROUGE-L}\}\)do
5:\(\mathbf{S}\gets f_{\text{target}}(\mathbf{\hat{p}^{j}})\triangleright\)Generate completion
6:\(c_{m}\gets f_{m}(\mathbf{S}_{\text{target}},\mathbf{S})\)
7:\(c_{m}\gets f_{m}(\mathbf{S}_{\text{target}},\mathbf{\hat{S}^{i}})\) \(\triangleright\)Calculate metric score
8:endfor
9:endfor
10:for each metric \(m\in\{\text{Similarity},\text{BLEU},\text{PPL},\text{ROUGE-L}\}\)do
11:\(\delta_{m}\gets c_{m}-\frac{1}{N}\sum_{i=1}^{N}c_{m}^{i}\)\(\triangleright\)Calculate deviation for each metric
12:endfor
13: Membership \(\leftarrow\)\(f_{\text{attack}}(\delta_{\text{Similarity}},\delta_{\text{BLEU}},\delta_{\text{PPL}},\delta_{\text{ROUGE-L}})\)
14:Return Membership status
```  
**Algorithm 1** Neighbor Deviation Attack.
## 4 Conclusions  
We have conducted experimental evaluations of an OWC system that uses an eye-safe 850 nm SM-VCSEL for a 2.5 m link. The system employs PAM-\(M\) in conjunction with an NN-based DFE, also known as the MLPDFE in this study. The SM-VCSEL offers distinct advantages, including lower RIN and lesser non-linearity, allowing for higher communication speeds compared to MM-VCSELs.  
We compared the BER performances of the MLPDFE against that of the TRDFE for the SM-VCSEL setup under conditions of strong and weak non-linear distortions. Our results indicate that the NN-based equaliser consistently delivers superior performance across most investigated scenarios, particularly in VCSEL-based OWC systems with high non-linear distortion and PAM constellations of eight or more levels.  
Specifically, the MLPDFE outperforms the TRDFE for PAM-4 modulation at lower ROPs, whereas it excels with PAM-2 at higher ROPs. With the MLPDFE, we achieved impressive bit rates of approximately 28 Gbps, 29 Gbps, 22.5 Gbps, and 5 Gbps using multilevel PAM schemes of 2, 4, 8, and 16 levels, respectively, at the 7% second-generation super FEC (S-FEC) limit. While higher modulation levels may not always offer the best bit rate because of their dependence on the available SNR, these results underscore the efficacy of the MLPDFE in optimising the performance of SM-VCSEL-based OWC systems across various modulation schemes and non-linear distortion levels.  
Conceptualisation, I.N.O.O. and S.R.; methodology, I.N.O.O. and S.R.; software, I.N.O.O.; validation, I.N.O.O. and S.R.; formal analysis, I.N.O.O. and S.R.; investigation, I.N.O.O.; resources, I.K., M.M., D.P., W.M., H.H. and J.T.; data curation, I.N.O.O. and S.R.; writing--original draft preparation, I.N.O.O.; writing--review and editing, I.K., D.P., W.M., M.M., J.T., H.H. and S.R.; visualisation, I.N.O.O.; supervision, S.R., J.T. and H.H. All authors have read and agreed to the published version of the manuscript.  
This work is a contribution by Project REASON, a UK government-funded project under the Future Open Networks Research Challenge (FONRC) sponsored by the Department of Science, Innovation and Technology (DSIT). The work at CSC and ICS Ltd has been partly funded by UK Research and Innovation via Project \"Quantum Foundry\" under the UK National Quantum Technologies Programme. This work is also supported by the UK Engineering and Physical Sciences Research Council under grant EP/Y037243/1 (TITAN Extension).  
Not applicable  
The data supporting the conclusions of this article will be made available by the author (I.N.O.O.) on request.  
Compound Semiconductor Centre Ltd. and Integrated Compound Semiconductors Ltd. had no role in the study design, in the collection, analysis or interpretation of data, in the writing of the manuscript; or in the decision to publish the results.
## 7 Comparative Discussion on Various Energy-Efficient Navigation Systems
### Reactive Navigation Systems  
Reactive navigation systems depend on real-time data collected by the sensors to respond and navigate the environment. To provide quick reactions to environmental variations, they frequently apply techniques for obstacle identification and avoidance [48]. Due to the emphasis on quick decisions and lack of need for lengthy mapping or planning, reactive navigation systems are often proven energy efficient. However, reactive navigation methods are not always optimized for long-term energy conservation and can fail in complex circumstances. The characteristics of the environment, the required level of optimization, and the unique needs of the MR's job all influence the decision between PSO-based navigation and reactive navigation. Reactive navigation is best suited for real-time adaptability and simple surroundings, whereas PSO-based navigation is better suited for global optimization and dealing with difficult conditions.