esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Computational Intelligence (CI) has been addressed as a great challenge in recent years, particularly the aspects of routing, task scheduling, and other high-complexity issues. Especially for the Contact Plan Design (CPD) that schedules contacts in dynamic and resource-constrained networks, a suitable CI algorithm can be exchanged for a high-quality Contact Plan (CP) with the appropriate computational overhead. Previous works on CPD mainly focused on the optimization of satellite network connectivity, but most of them ignored network topology characteristics. In this paper, we study the CPD issue in the spatial node based Internet of Things (IoT), which enables the spatial nodes to deliver data cooperatively via intelligent networking. Specifically, we first introduce a Multi-Layer Space Communication Network (MLSCN) model consisting of satellites, High Altitude Platforms (HAPs), Unmanned Aerial Vehicles (UAVs), and ground stations, on which a Time-Evolving Graph (TEG) is used to illustrate the CPD process. Then, according to the characteristics of each layer in the MLSCN, we design the corresponding CPs for the inter-layer contacts and intra-layer contacts. After that, a CI algorithm named as Multidirectional Particle Swarm Optimization (MPSO) is proposed for inter-layer CPD, which utilizes a grid-based initialization strategy to expand the diversity of individuals, in which a quaternary search method and quaternary optimization are introduced to improve efficiency of particle swarms in iterations and to ensure the continuous search ability, respectively. Furthermore, an optimized scheme is implemented for the intra-layer CPD to reduce congestion and improve transmission efficiency. Simulation results show that the proposed CPD scheme can realize massive data transmission with high efficiency in the multi-layer spatial node-based IoT.  
Internet of Things; computational intelligence; contact plan design; multidirectional particle swarm optimization; delivery time 1
# An Innovative Hybrid Approach Producing Trial Solutions for Global Optimization
###### Abstract
After a number of studies, it was observed that differential evolution can be successfully combined with other techniques for machine learning applications, such as image classification [27], feature selection, predicting heart disease problems [28], the compression of deep neural networks [29], etc. In global optimization, hybrid methods [30] refer to techniques that combine multiple optimization strategies to solve complex problems. These methods aim to take advantage of different approaches to find the global optimum in a more efficient way, particularly when dealing with large-scale problems or strongly nonlinear optimization landscapes. A typical example of a hybrid method is the work of [PERSON] et al., who proposed a new hybrid PSO-BFGS strategy for the global optimization of multimodal functions [31]. To make the combination more efficient, they proposed an LDI to dynamically start the local search and a repositioning technique to maintain the particle diversity, which can effectively avoid the premature convergence problem. Another innovative hybrid method is the work of [PERSON] et al., where a hybrid algorithm using GA operators and a PSO formula was presented through the use of efficient operators, for example, traditional and multiple crossovers, mutations, and PSO formula [32]. In the current work, two evolutionary methods were incorporated into the final algorithm: genetic algorithms and the differential evolution method were combined into a hybrid optimization method. More specifically, through a series of steps, trial solutions are generated using the genetic operators of the genetic algorithm, as well as solutions determined by a line search procedure. Additionally, an Armijo line search method is used. This method is incorporated into our work to estimate an appropriate step when updating the trial points, and it was introduced in the work of [PERSON] [33]. The solutions produced in the previous step are used to formulate new trial solutions using a process derived from differential evolution. The remainder of this paper is divided into the following sections: in Section 2, the proposed method is described; in Section 3 the experimental results and statistical comparisons are presented; and finally, in Section 4, some conclusions and guidelines for future improvements are discussed.
## 2 Materials and Methods
### Measurement  
Optical images, Raman spectra, and PL spectra of the samples were acquired using a homemade laser scanning confocal PL-Raman spectrometer (LSCPRS). A laser with a wavelength of 532 nm and a power of 6 mW was utilized as the excitation source. The spot diameter was focused to \(\sim\)1 \(\upmu\)m using a long work-distance M plan fluorite semi-apochromat infinity-corrected objective (LMPLFLN 50X, Olympus, Tokyo, Japan). The exposure times were 3 and 10 s for the PL and Raman spectra, respectively. The emission intensity was recorded with a thermoelectrically cooled charge coupled device detector at \(-\)60 \({}^{\circ}\)C, and the wavelength was distinguished by 300 and 1800 lines per mm diffraction gratings for the PL and Raman spectra, respectively. The morphological features and height of the samples were investigated through atomic force microscopy (Ntegra, NT-MDT, Moscow, Russia) equipped with a Si probe (VIT_P/TERS, 50 N/m, NT-MDT & TipsNano, Tallinn, Estonia). The actual force constant was measured to be 45.5582 N m\({}^{-1}\). Pressure was applied to the nanoscale contact area on the sample stage through a Si tip loaded onto the AFM instrument. The pressure force (F) was calculated using F = setpoint \(\times\) 0.064 nN. The focusing position of the microscope was adjusted with reference to the tip to enable optical property measurements under different pressure conditions in contact mode.
# Durability Issues and Corrosion of Structural Materials and Systems in Farm Environment
## 2 Agricultural Concrete and Metallic Structures
Humic acid impacts the strength of concrete and accelerates the corrosion process. Even though oxalic acid has no known adverse effect on concrete at mild concentrations, it can dissolve concrete at high concentrations. The impact of organic acids on metallic structures is similar to alkali chemicals. Plants and animal oils and fats from the food industry loosen the concrete structure. The loosening process (separation of the concrete constituents) is characterized by the reaction of the fatty acids with calcium salts to form soft calcium soaps. In addition, organic and inorganic acids, ions such as magnesium and ammonia and sulfate found in industry effluents, artificial fertilizers, natural water, and effluent, increase the rate of concrete swelling, accelerate concrete dissolution and soften the concrete structures. Similar corrosive and dissolving action has also been observed in the acidic solutions with a pH of less than 6.5, CO, CO\({}_{2}\), and H\({}_{2}\)CO\({}_{3}\) and hydrogen sulfide gas originating from feed residues, and silage [1; 2].
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 4 c-Cbba  
Based on the above analysis, this section proposes an improved CBBA algorithm for communication problems, called c-CBBA, which mainly includes two strategies: the Task Preemption Strategy (TPS) and the Single-Chain Strategy (SCS). Figure 6 shows the process of c-CBBA. From the figure, it can be seen that TPS mainly acts on the consensus check stage of the basic CBBA, accelerating algorithm convergence by improving the auction rules. SCS is mainly used for inter-satellite communication. By analyzing the onboard communication topology, the communication links are pruned so as to reduce the communication cost of the algorithm in large-scale scenarios.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Iteration** & **Phase I** & **After Phase II** \\ \hline Iter1 & \(b_{1}=\{\text{taskA}\}\), \(b_{2}=\{\text{taskA}\}\) & \(b_{1}=\{\}\), \(b_{2}=\{\text{taskA}\}\) \\ Iter2 & \(b_{1}=\{\text{taskB}\}\), \(b_{2}=\{\text{taskA}\}\) & \(b_{1}=\{\text{taskB}\}\), \(b_{2}=\{\text{taskA}\}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Solution process of CBBA for the masking issues.  
Figure 5: Example of masking phenomenon, which shows the visible time windows and their conflict relationship of two satellites for task A and B, as well as the bidding value for tasks.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 6 \(\text{Sillcom}_{trd}\): Customized Optimization with Computation/ Communication Tradeoff
### Accelerating FSS keys Computation via Multi-threads  
In both \(\text{Sillcom3}R\) and \(\text{Sillcom}trd\), the computation of DDCF keys is a major bottleneck due to the high computational cost caused by the large number of fixed-key AES operations. This computational overhead significantly impacts the overall performance and efficiency of our design. To address this issue, we employ multi-threading to optimize performance. By leveraging multi-threading, we can parallelize the computation of DDCF keys, effectively distributing the workload across multiple processor cores. The multi-threading approach enhances the system's scalability and performance, enabling faster key generation and improving the overall efficiency of the computational task. Through this optimization, we aim to reduce latency and achieve a more responsive system capable of handling larger datasets and more complex computations in real time. We also provide a detailed validation of the acceleration brought by multi-threading in the experiments.
# Damping Estimation of an Eight-Story Steel Building Equipped with Oil Dampers
## 4 Application to the Investigated Building
### Model Updating of the Building
Two updated models are utilized for this response prediction: In the first updated model, the damper stiffness is not considered, the modal data utilized for correcting mass and stiffness matrices is {\(\omega_{i}\), \(\mathbf{\phi}_{i}\), \(p_{i}\), \(\xi_{s,i}\), \(i\) = 1,2}, while, in the second updated model, the data is {\(\omega_{s,i}\), \(\mathbf{\phi}_{i}\), \(p_{i}\), \(\xi_{s,i}\), \(i\) = 1,2}. To distinguish them, they are labeled by updated model I and II, respectively. Figures 8 and 9 present the measured and predicted floor acceleration and damper force, respectively. The results in these figures correspond to the longitudinal direction updated models. For clarity of illustration, the response predictions during the strong shaking phase (30 s\(\sim\)60 s) are shown, and the results of the two updated models are plotted separately. The normalized root mean squared error (NRMSE) between the measured and predicted response, defined as  
\[e=\sqrt{\text{mean}\big{[}(y-\overline{y})^{2}\big{]}}/\sqrt{\text{mean}[ \overline{y}^{2}]} \tag{34}\]  
is utilized to quantify the goodness of fit between them, in which \(\overline{y}\) and \(y\) are the measured and predicted response, respectively. The results for the two updated models are listed in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Longitudinal Direction**} & \multicolumn{2}{c}{**Transversal Direction**} \\  & **1 st Mode** & **2 nd Mode** & **1 st Mode** & **2 nd Mode** \\ \hline \(p_{B,i}\) & 45.8910 & \(-\)12.0991 & 45.9156 & \(-\)14.0551 \\ \(p_{i,i}\) & 47.4219 & \(-\)15.3921 & 48.9527 & \(-\)15.3241 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons of the predicted mode participation factors by updated mass matrices. Figure 7: Updated stiffness matrices for the building itself: (**a**) Longitudinal direction; (**b**) Transversal direction. It can be seen from Figures 8 and 9 that the predicted responses by updated model II match very well the measured data with NRMSE values close to 0.2, which indicates a high precision in response prediction. On the other side, the updated model I has non-negligible phase-lags errors in all response predictions with larger NRMSE values up to 0.7. A similar trend of calculated NRMSEs is also observed for the transversal direction updated models, as shown in Table 5. It verifies that, at least in this instance, the damper stiffness or the estimated equivalent natural frequencies, though small, can not be omitted in model updating for the building itself. Moreover, the high precision predictions of updated model II also show that the estimated equivalent damping ratios and natural frequencies are accurate, which addresses the concern at the end of the last section.
# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 3 Descriptive Analysis
### Spatial Extent and Geographical Distribution  
The reviewed articles were categorized into five classes of spatial extent: local, multi-local, regional, national, and global scale. In this review, we define local scale as the level of forest unit (national park, forest district, forest reserve, etc.). Locations distributed across a country, different countries or continents without a specific spatial extent are referred to as the multi-local level. The regional level includes research at multiple locations within or across several provinces (states) of the country or cross-border studies in several countries. The national spatial level examines burnt forest within the state boundaries of the study country. Studies at the worldwide level were categorized as global scale. The statistical analysis indicated that the majority of the reviewed articles were conducted on the local (48%) and regional spatial scales (28%) (Figure 4). We identified 30 national (9%) and 28 multi-local (9%) scale studies of forest burnt area with the use of remote sensing, as well as 18 global studies (6%).  
Figure 4: Distribution of selected articles at different spatial scales.  
Figure 3: Top 20 out of 48 peer reviewed journals where the selected papers were published.  
The studies considered in this review were conducted in many forest sites across the world including countries from America (North, Central, and South), Europe, Asia, Australia, and Africa (Figure 5a). Selected publications from 2000 to 2020 focused on forest fires in 39 countries. Journals with 10 or more publications originate from the USA (93), Spain (49), Canada (29), Greece (21), Brazil (17), Russian Federation (15), Italy (13), Portugal (12), Australia (10), and the People's Republic of China (10) (Figure 5b). In total, 269 articles (84%) described different approaches to the estimation of forest burnt area in ten of these countries with the use of remote sensing techniques. The case studies carried out in the USA, Spain, and Canada cover about 53% of all the reviewed publications. Other countries with more than two publications are also worth mentioning, particularly India (7), France (5), South Africa (4%), the Republic of Congo (3), Indonesia (2), South Korea (2), Turkey (2), Argentina (2), Malaysia (2), Peru (2), Israel (2), Chili (2), and the Central African Republic (2) (Figure 5b).  
Figure 5: Geographic distribution of study site locations conducted per country: (**a**) global map of reviewed studies; (**b**) total number of studies per country (>1).
## 3 Numerical Simulation of the Debris Impact on Satellite Tank
### Perforation of Front and Back Walls  
When the debris impacted the front wall of the satellite tank, the stress concentration occurred at the impact point. The initial pressure of the shock wave was 1.22 GPa, which exceeded the yield strength of titanium (\(\sigma_{s}\) = 820 MPa). The failure elements were deleted, and circular perforation was generated on the front wall. The radial motion of the liquid hydrazine produced a pressure field that extruded the front wall. The deformation range of the front wall enlarged, resulting in an outward bulge.  
The shock wave first arrived at the back wall and exerted pre-stress on it. However, the pressure of the shock wave decreased to 390 MPa, which was less than the yield strength of titanium. Plastic deformation occurred and an outward bulge was generated. After the transmission of the shock wave, the high pressure hydrazine in front of the debris acted on the back wall, and exerted a pressure load on it, which magnified the bulge height. The pressure applied by high pressure hydrazine on the back wall was also less than the yield strength, and no perforation was found. The penetration of debris was the main cause of perforation on the back wall.  
The perforation and bulge on the front wall is displayed in Figure 8a, and the perforation and bulge on the back wall is displayed in Figure 8b. The maximum diameter of perforation is represented by \(d_{max}\), and the bulge height is represented by \(\delta\). The perforation on the front wall was mainly  
Figure 7: Pressure–time curves from element A to element G.  
caused by the high-pressure shock wave, the maximum diameter of which was \(d_{max}\) = 10.23 cm. The perforation on the back wall was mainly caused by debris penetration at the velocity of 263 m/s, the maximum diameter of which was \(d_{max}\) = 6.90 cm. Consequently, the diameter of perforation on the back wall was smaller than that on the front wall.  
Taking the distance from the impact point \(L\) as the abscissa and the bulge height \(\delta\) as the ordinate, the curves of the bulge height varying with distance on the front and back walls were plotted in Figure 9. The curve was symmetrical with respect to the impact point (\(L\) = 0) and its shape was close to \"M\". The bulge range on the front wall was larger than that on the back wall, but the bulge height on the front wall was smaller than that on the back wall. The reason is that the bulge on the front wall was mainly caused by liquid hydrazine extrusion, while the bulge on the back wall was the result of the shock wave, high pressure hydrazine, and debris penetration. The bulge height at the edge of perforation was the largest. The maximum bulge height on the front wall was \(\delta\) = 1.12 cm, and the maximum bulge height on the back wall was \(\delta\) = 1.58 cm.
# Optimized Architecture for Efficient OFDMA Network Design
## 3 Analytical Model for OFDMA Networks
### Markov Chain Representation  
The analytical model proposed in [25] is based on the multidimensional continuous-time Markov chain (CTMC) theory. In this model, each state, denoted as \(S_{n}\), represents the scenario where there are n active stations at a given moment. \(N\) denotes the total number of stations in the system, which sets the upper bound on the number of active stations, ranging from \(S_{0}\) (no active stations) to \(S_{N}\) (all stations active). For example, state \(S_{0}\) corresponds to the situation where no mobile stations are active, meaning all devices are in the OFF period (i.e., the pause between two downloads) [26].  
A transition from state \(S_{n}\) to state \(S_{n+1}\) occurs when a mobile station in the OFF period switches to the ON state (data transfer). This transition occurs at an arrival rate of \((N-n)\lambda\)where \((N-n)\) represents the number of stations in the OFF period, and \(\lambda\) is defined as the inverse of the average reading time:  
\[\lambda=1/t_{\mathit{off}} \tag{1}\]  
Conversely, a transition from state \(S_{n}\) to \(S_{n-1}\) happens when a mobile station in the ON state completes its data transfer. This departure transition occurs at a rate \(\mu(n)\), which corresponds to the total departure rate when there are n active stations. The estimation of \(\mu(n)\) is a key challenge, as it depends on the selected scheduling policy. Thus, this model focuses on evaluating \(\mu(n)\) to derive the performance parameters for each scheduling policy [27, 28].  
Figure 1 presents the structure of the multidimensional continuous-time Markov chain (CTMC) model used in this study. It illustrates the different states \(S_{0}\),\(S_{1}\),\(S_{2}\),\(\ldots\), where each state n corresponds to the number of active stations in the system. Figure 1 also depicts the transitions between these states, governed by the arrival and departure rates, as stations switch between ON (data transfer) and OFF (idle) periods.
## 2 Related Works
Its architecture minimizes computing complexity, making it suited for resource-constrained environments and real-time applications. Despite its benefits, MobileNet lacks built-in interpretability, which is vital in clinical diagnostics where understanding the reasoning behind predictions is essential. Furthermore, it is susceptible to image quality fluctuations [12]. Recent research has also investigated explainable AI and hybrid models, emphasizing their influence on model dependability. For example, domain-specific visualization approaches combined with Grad-CAM have increased diagnostic transparency [13]. Similarly, preprocessing processes geared towards resource-constrained circumstances allowed MobileNet to reach remarkable performance metrics, with automated techniques attaining Top-1 accuracy of 90% for early-stage detection [14]. These innovations underscore the potential for integrating cutting-edge methods, such as advanced CNN architectures, transfer learning, and visualization techniques, into skin cancer diagnostics to enhance detection accuracy, efficiency, and usability. While these approaches improve diagnostic performance and model explainability, challenges such as high computational costs, limited applicability in resource-constrained environments, and the need for robust real-world validation remain areas for further exploration. Nonetheless, these advancements collectively expand access to healthcare through telemedicine and automated screening solutions, paving the way for more inclusive and reliable diagnostic systems.