esa-sceva/satcom-chunk-collection huggingface dataset:
# Tourism-Induced Urbanization in Phuket Island, Thailand (1987-2024): A Spatiotemporal Analysis
## 4 Discussion
### Urban Growth and Densification Trends  
Between the 1991-1995 and 2021-2024 periods, the urban expansion in Phuket transitioned from growth to more effective land use. In its stages, there were developments with low density on the outskirts driven by tourism demands and available land. As land became scarcer, the period between 2000-2005 and 2006-2010 witnessed infill developments leading to increased density, focusing on compact growth and reducing sprawl [48]. From 2011-2015 to 2016-2020 high-density constructions alongside development became prevalent with policies promoting mixed-use projects. This strategy catered to a growing population while lessening impacts. By 2021-2024 there was an emphasis on densification and smart growth, integrating residential, commercial, and recreational spaces to minimize travel needs, supporting a sustainable city lifestyle. The distinctive geography of Phuket, with its mountains encircling the sea, underscores the importance of land usage.  
The evolving urban landscape from 1987 to 2024 underscores the importance of flexible planning that harmonizes economic progress with environmental conservation and social equality [51]. Prioritizing urban growth ensures Phuket flourishes as a sought-after tourist spot while safeguarding the well-being of its residents and preserving its natural and cultural legacy. It is crucial to implement strategies for holistic development, preservation of natural resources, and enhancement of societal welfare [49]. Encouraging development, improving transportation networks, and fostering mixed-use urban areas play a pivotal role in this regard [52]. By fortifying infrastructure to accommodate population densities and embracing comprehensive urban planning practices, cities can achieve enduring sustainable progress, creating livable environments that strike a balance between advancement and conservation. The expansion of areas and increase in population density influence Phuket's economy by boosting efficiency, generating employment opportunities, and attracting investments. Nonetheless, rapid expansion can strain existing infrastructure, leading to escalated costs and gentrification. Strategic growth management boosts revenues, which further aid in infrastructure upgrades and community enrichment. Development fosters economic resilience by drawing environmentally conscious businesses and residents thereby uplifting the local economy.
## 2 Numerical Modelling
### Constitutive Material Modelling--Aluminium Skins  
The aluminium alloy of the sandwich plate skins is Al 5754-H22 and its behaviour is modelled while using a thermo-visco-plastic constitutive law, together with a coupled damage model. The Johnson-Cook model, which is used to achieve this, is defined by a flow stress \(\sigma_{\text{y}}\) with exponential strain hardening, logarithmic strain rate dependence and temperature softening. These dependencies are described by the following relation:  
\[\sigma_{\text{y}}=\left[A+B\left(\bar{\epsilon}^{\text{p}}\right)^{n}\right] \left[1+C\ln\left(\dot{\epsilon}^{*}\right)\right]\left(1-T^{*}\right), \tag{1}\]  
where \(\bar{\epsilon}^{\text{p}}\) is the effective plastic strain, \(\dot{\epsilon}^{*}\) is the normalised total strain rate, \(T^{*}=(T-T_{\text{r}})/(T_{\text{m}}-T_{\text{r}})\) is the homologous temperature and \(A\), \(B\), \(C\), \(n\) and \(m\) are material parameters. \(T_{\text{r}}\) and \(T_{\text{m}}\) are the room and melting temperatures, respectively.  
An associated strain-based failure criteria is additionally used to account for damage occurring during the blast event. This criteria is defined as  
\[\epsilon^{\text{f}}=\left[D_{1}+D_{2}\exp(D_{3}\sigma^{*})\right]\left[1+D_{4} \ln\dot{\epsilon}^{*}\right]\left[1+D_{5}T^{*}\right], \tag{2}\]  
where \(D_{i}\) are material parameters and \(\sigma^{*}\) is the ratio of pressure \(p\) to the effective stress \(\sigma_{\text{eff}}\), which is,  
\[\sigma^{*}=\frac{p}{\sigma_{\text{eff}}}. \tag{3}\]  
Fracture occurs when the plastic strain reaches the threshold value \(\epsilon^{\text{f}}\) defined in Equation (2). The material and damage parameters for the 5754-H22 aluminium alloy are taken from the literature [20; 21; 22; 23; 24; 25; 26] and listed in Table 2.  
The material compressibility (bulk behaviour) at high strain rates is accounted for by a linear polynomial equation of state that relates the pressure \(p\), the density \(\rho\), and the initial density \(\rho_{0}\) as  
\[p=C_{1}\frac{\rho}{\rho_{0}} \tag{4}\]  
where \(C_{1}\) is a material constant.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Units** & **Value** \\ \hline Density, \(\rho_{0}\) & kg/m\({}^{3}\) & 2685 \\ Young’s modulus, \(E\) & GPa & 70.33 \\ Poisson’s ratio, \(\
u\) & – & 0.3 \\ Initial yield stress, \(A\) & MPa & 185 \\ Strain hardening coefficient, \(B\) & MPa & 130.5 \\ Strain rate coefficient, \(C\) & – & 0.003 \\ Strain hardening power constant, \(n\) & – & 0.424 \\ Temperature sensitivity, \(m\) & – & 2.519 \\ Room temperature, \(T_{\text{r}}\) & K & 293 \\ Melting temperature, \(T_{\text{m}}\) & K & 873 \\ Damage parameter, \(D_{1}\) & – & 0.005 \\ Damage parameter, \(D_{2}\) & – & 0.845 \\ Damage parameter, \(D_{3}\) & – & \(-1.89\) \\ Damage parameter, \(D_{4}\) & – & 0.0897 \\ Damage parameter, \(D_{5}\) & – & 7.97 \\ EOS constant, \(C_{1}\) & GPa & 70.33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material parameters for the Johnson-Cook constitutive and damage laws, for the Al 5754-H22 aluminium alloy [20; 21; 22; 23; 24; 25; 26].
# Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics
## 5 Discussions  
### Parameter Discussion  
In this section, the influence of the parameters involved in the algorithm mentioned in this paper will be discussed. The experiments are carried out on ML-100k data set, where about 20,000 rating data are used to test the influence of the parameters in the proposed algorithm.  
(1) About the nearest neighbor  
First, the reasonable number of the nearest neighbor \(K\) is discussed, which is one of the key factors for the recommendation algorithm to achieve good results. The MAE and RMSE of the proposed method under different \(K\) are shown in Figure 6. The results in Figure 6 show that with the increase of the number of nearest neighbors of the target user, the MAE and RMSE of the algorithm show a downward trend and gradually tend to be saturated. Therefore, the nearest neighbor \(K\) is set as 35 in this study, to keep a relatively high accuracy and low computation.  
(2) About the user characteristic parameters  
Secondly, the user characteristic parameters \(\alpha\), \(\beta\), and \(\delta\) are discussed. In this experiment, the parameter adjustment step is set as 0.1. Because \(\alpha+\beta+\delta=1\), to keep all the three parameters bigger than 0, all the three parameters are set between \([0.1,0.8]\). The MAE and RMSE of the proposed method under different \(\alpha\), \(\beta\), and \(\delta\) are shown in Figure 7. It can be seen that the MAE and RMSE are the minimum when \(\alpha=0.5\), \(\beta=0.2\), and \(\delta=0.3\).  
(3) About the model fusion parameters  
Thirdly, the similarity weighted fusion parameters \(\xi\) and \(\mu\) are discussed. In this experiment, the parameter adjustment step is set as 0.05. The MAE and RMSE of the proposed method under different \(\xi\) and \(\mu\) are shown in Figure 8. It can be seen that the MAE and RMSE are the minimum when \(\xi=0.8\) and \(\mu=0.2\). This proves that the user  
Figure 6: The influence of \(K\) on the proposed method: (**a**) of MAE; (**b**) of RMSE.  
Figure 7: The influence of the user characteristic parameters \(\alpha\), \(\beta\), and \(\delta\) on the proposed method. (**a**) of MAE; (**b**) of RMSE.  
similarity calculated by the TF-IDF method has the main influence on the recommendation algorithm, and the recommendation effect can be improved by properly fusing the user characteristics similarity.
## 4 Evaluation Scope
### Target Domain
#### 4.1.7 Science and Education  
Various studies in our sample addressed the science and educational sector [36; 38; 43; 56; 58; 60; 66], where explanations are studied in the context of learning and research and knowledge assessment. A distinctive feature of this domain is that users are particularly interested in understanding issues and are often eager to learn something new. Therefore, explanations contribute not only to pragmatic goals but also align with the educational interests of the target audience.  
Regarding this, [PERSON] et al. [56] evaluated the role of explanations concerning math exercise recommendations. Also, explanations are evaluated in entertainment settings such as decision-making games [66] or learning games [38]. Explanations are also evaluated to improve scientific literature recommender systems [58].  
A more serious issue arises when AI systems are used to assess students' intelligence and abilities. Since such evaluations can have significant consequences for educational careers, these decisions must be correct, fair, and accountable. For this reason, several studies have evaluated how explanations can influence the decision-making process in student admission [43] and student application recommendation [36] and using explanations for answering the Law School Admission Test (LSAT) [60].
## 3 Results
### Evaluating the Efficiency of the YOLO v10 XE Model in Analyzing Building Roof Topologies
#### 3.2.3 Analysis of Misclassified Examples  
Despite the high accuracy achieved by the YOLO model in detecting the two classes of roofs, a small number of identification mistakes remain, attributable to the complexity of roof shapes and the variety of objects that can be misclassified as roofs. One of the most common errors is the misclassification of a roof, especially in the case of unusual roof forms that differ from traditional shapes, such as roofs with complex geometry (e.g., roofs with additional superstructures) (Figure 8a). Additionally, the presence of supplementary objects that may be constructed from a similar material to the roof (e.g., sheet metal, shingles) can result in erroneous recognition by the model. The development under discussion is also influenced by highly compact development, where the roofs of individual buildings lose their extent (e.g., one roof for several buildings).  
Another factor that impacts the quality of the classification is the presence of objects that have a roof covering but do not constitute buildings, such as sheds, gazebos, or other small structures (Figure 8b). The model may misclassify these objects as part of a larger building structure because their roofing may resemble a classic roof shape. Additionally, there are instances where objects lacking a roof may be erroneously classified as such, particularly when they exhibit a rectangular form with a distinct delineation, which may imply the presence of a roof in the analyzed area (Figure 8c).  
Furthermore, there is a paucity of recognition of certain roof types. This is primarily due to the unconventional configuration of the roof, which significantly deviates from conventional designs (e.g., conical roofs or roofs with numerous additional elements) (Figure 8d). Errors of this nature are particularly evident in instances where the roofing is diminutive in size, occurs on an unconventional structure, or is composed of materials similar to those utilized on building roofs. The development of a multimodal system that will make use of additional data, such as ALS LiDAR or oblique images, will necessitate the improvement of the YOLO model to eliminate these errors. Moving forward, there is a need to focus on enhancing the capacity to distinguish objects not only based on their geometry but also considering additional features such as height, slope angle, and material details. This will help to minimize misclassification. With further development and integration of diverse data sources, the system will be capable of more accurately identifying roofs and differentiating between objects with similar structures.
# Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models
## 4 Proposed Work
### Stacking Ensemble Learning  
Ensemble-based systems are formed by combining diverse models together to give robust predictions and to minimize the probability of erroneous predictions [55; 56]. This kind of procedure has been extensively used by humans in daily lives, such as asking the opinions of several experts before making a decision, e.g., in the context of medical treatments. Ensemble methods include stacking, as well as boosting and bagging [56], and have been applied successfully in various regression and classification applications [57; 58] or in the field of reinforcement learning [59] where an ensemble of agents is voting on the optimal action.  
Before stacking ensembles, there were many methods to integrate various models together. For example, in the MAET (model averaging ensemble technique), probabilitiesfrom multiple classification models or estimators are combined in equal proportions. Though it is a simple mechanism, it may not be a good proposal regarding the flexibility of the final model. This is because different models will contribute equivalently to the final prediction. An alternative way is to combine the predictions from several models in variable proportion is depending upon a trust score. This trust score or confidence score can be computed depending on the performance of the individual models on unseen training data. This allows better models to contribute more and poor models to contribute less. This technique is called a weighted average ensemble.  
Stacked generalization works by deducing the biases of the classifiers with respect to a provided training set. This deduction proceeds by classifying in a second space whose inputs are the predictions of the original classifiers when taught with part of the training set and trying to predict on the holdout dataset for performance assessment. Stacked generalization can be seen as a sophisticated version of cross-validation [32]. In the second feature space, a meta-learner can be trained from scratch to efficiently combine the predictions of the individual models or base models and finally predict the most probable output. This selection of meta-learner greatly impacts the performance of the ensemble model. In the present study, we have used a fully connected neural network as a meta-learner. The aforementioned base models are frozen and only the meta-learner is trained again on the training set to decrease the generalization error and to make better predictions. Figure 3 shows the general architecture of a two-level ensemble model.
# A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic
## 5 Performance Evaluation and Results
### Network Scenario and Simulation Environment Settings  
The proposed approach is designed for IoT-based smart city networks that take high and dynamic traffic conditions into account. Such heavy and dynamic traffic may arise from emergency situations, such as emergency alerts. Therefore, the simulation network scenario includes application attributes of emergency monitoring, such as chemical leakage in the industry and healthcare monitoring in the hospital. By deploying the sensor nodes and smart embedded devices in the monitoring area, the emergency monitoring network can be built to regularly response about unusual situations. It should be emphasized that the proposed approach is designed not only for emergency monitoring in hospital and industry, but also for other similar IoT applications in smart city vision.  
For simulation testing, the Tmote sky devices were configured with a CC2420 wireless transceivers, which are IEEE 802.15.4 compliant devices with a data transfer rate of 250 kbps. The network topology is static, and 25 devices were deployed in the network field, including LLN border router (LBR). The proposed work was implemented in the Contiki 2.7 operating system [38]. It is an open-source operating system that is designed for resource-constrained devices of IoT. The simulation tests were conducted in Cooja simulator [41]. It is a Java-based cross-layer simulator of Contiki OS. Moreover,  
Figure 9: A simple functional context model of context-oriented objective function (COOF).  
we used constant bit rate (CBR) traffic flow, and each packet size was 40 bytes. During the test, each simulation run lasts for 10 min. Over the simulation period, the source node was freely selected for each simulation test and kept them stable. In our simulation experiments, we have set \(\alpha=0.4\). All if the simulation tests were run in 10 iterations to avoid random errors. Finally, the average result has been extracted and presented. Table 3 shows the remaining simulation parameter.
# MEMS-Switched Triangular and U-Shaped Band-Stop Resonators for K-Band Operation
## 5 Conclusions  
This paper proposed switched triangular and U-shaped band-stop resonators implemented by MEMS technology, employing an RF MEMS SPDT to select the path for the RF output. The individual resonators were: (i) Sierpinski-based triangular structures obtained by increasing the internal complexity of a full metal patch using empty internal sub-triangles and (ii) U-shaped structures. Both switched resonators were designed in CPW configurations and are suitable for more complex structures involving coupled individual resonators. Devices working at 20 GHz and 26 GHz approx. were studied for potential K-Band operation, i.e., in the band presently more appealing for satellite applications, proving quite good performances and suitable optimizations. Promising results were obtained for both the resonator configurations, although some optimizations are still needed to improve the electrical matching of the entire structure. The SPDT exhibited a good performance in terms of the insertion losses, with a measured value for the transmission parameter S\({}_{21}\) of around \(-1\) dB in a wide frequency range, thus confirming the wideband capabilities of ohmic RF MEMS switches. A further engineering step is necessary for improving the insertion losses of the full devices, keeping control of the parasitic contributions and discontinuities. On the other hand, the basic requirements for selecting two different frequencies were demonstrated by a rejection ratio of nearly 15 dB between the ON/OFF states of the switches. Furthermore, additional work is needed to predict the resonance frequency better when a high complexity of the Sierpinski configuration is used.  
Contributions from the authors were the following: technology and program co-management, F.G.; layout design and participation to technology: E.P., G.C., J.I. and G.T.; design and test: G.M.S.; funding acquisition, device design and measurement, R.M. All authors have read and agreed to the published version of the manuscript.  
Co-funded by the Italian Space Agency Project <<METAMES>>, Grant No. ASI n. 2019-12-U.0--bando di finanziamento di progetti di ricerca industriale e/o sperimentale 'Nuove Idee per la Componentistica Spaziale del Futuro (\"TRL\"), Codice Unico di Progetto (CUP) F84118000380005.  
**Data Availability Statement:** Raw data available upon requirement.  
We sincerely thank [PERSON], of CNR-IMM and University of La Sapienza, Rome, Italy, for providing SEM images, in the framework of the NANOMICROFAB Open Research Facility.  
The authors declare no conflict of interest.
## 3 Methods
### Spatial Analysis of Errors  
The prediction errors can often show a unique spatial pattern. These errors may be either randomly distributed in space or spatially clustered. Hence, the spatial autocorrelation characteristics of the prediction errors were also examined using the Local Geary index (Gi*) [66], which expresses the degree of spatial association of an area with its neighboring areas:  
\[{G_{i}}^{*}=\frac{\sum_{j=1}^{n}w_{ij}x_{j}-\overline{x}\sum_{j=1}^{n}w_{ij}}{ S\sqrt{\left[\frac{n\sum_{j=1}^{n}w_{ij}^{2}-\left(\sum_{j=1}^{n}w_{ij} \right)^{2}}{n-1}\right]}}\, \tag{4}\]  
where \(x_{j}\) is the attribute value of the neighboring area \(j\), \(\overline{x}\) is the average value of \(x\), \(w_{ij}\) is the spatial weight between a target area \(i\) and its neighboring area \(j\), \(n\) is the number of areas, and \(S\) is the standard deviation of \(x\). The Gi* statistic is calculated for each area as a z-score. A positive, high value of Gi* indicates a hot spot with high clustering, and a negative, low value indicates a cold spot with low clustering. A value of zero indicates neutral or random spacing.  
Figure 3: Parameter optimization process for a deep neural network (DNN) model in this study.
# Absorption Enhancement in Hyperbolic Metamaterials by Means of Magnetic Plasma
## 1 Introduction  
Surface plasmon polaritons (SPPs) are treated as the electromagnetic excitations occurring at the boundary separating two different substances. It is worthwhile mentioning that the real part of the dielectric function changes the signs on the interface. A conventional boundary separating a conducting and a dielectric medium was considered in [1]. Recently, astonishing advancement has been obtained in the field of SPPs. In particular, many investigations implying SPPs in metamaterials and photonic crystals have been conducted because of the latter's ability to dramatically tune SPPs' features [2].  
Metamaterial research has attracted the attention of optical engineers and material scientists due to the variety of possible applications such as imaging [3], cloaking [4], sensing [5], waveguiding [6], and simulating space-time phenomena [7]. Hyperbolic metamaterials (HMMs) are named because of the topology of the isofrequency surface. The linear dispersion and isotropic performance of propagating plasmons cause a spherical isofrequency surface. The exotic behavior of modes possessing large-magnitude wavevectors is considered as the most fascinating feature of such substrate. In vacuum, such large wavevector waves are evanescent with exponential decay.  
The tunable properties of plasmas offering some peculiar advantages paved the way for the increasing interest in plasma photonic crystals and plasma composites. Plasmas possess major advantages in comparison with other conventional materials. Doing so, one may use the applied power supply for producing plasmas and adjusting the gas pressure or temperature of plasmas, aiming to engineer the dynamic shift in permittivity and calibrate the amplitude on the complex plane.  
Herein, we consider an interface of a hyperbolic metamaterial and magnetic plasma, aiming to give rise to tunable properties including absorption enhancement. To attainthe mentioned goal, we consider two different types of hyperbolic metamaterials, i.e., nanolayered metamaterials and nanowire metamaterials. The inclusion of a plasma layer opens wide avenues for investigations. The influences of the applied magnetic field, collision frequency of plasma, background material dielectric constant, and thickness on the dispersion relations of SPPs are explored and discussed. This paper is structured as follows. Firstly, we present the theoretical background, followed by the Results section.