esa-sceva/satcom-chunk-collection huggingface dataset:
# Hygro-Thermo-Mechanical Analysis of Brick Masonry Walls Subjected to Environmental Actions
## 2 Hygro-Thermal Model  
This section describes the numerical model adopted for hygro-thermal simulations. The moisture transport model was validated by the authors in [37]. In the present paper, this model is extended to incorporate the thermal field. Thus, the theoretical bases and main assumptions of the extended model are briefly presented here.  
Masonry is described as a porous medium made up of a matrix of interconnected pores inside a solid skeleton. The solid phase is assumed continuous, homogeneous, inert (no chemical reactions), isotropic, and non-deformable. Moreover, pores are assumed cylindrical, with a homogeneous, isotropic distribution inside the material. The void space defined by the pore system is filled with a liquid and a gaseous phase in different proportions. The liquid phase is pure and incompressible, whereas the air phase is an ideal mixture of dry air and water vapor at atmospheric pressure. For simplicity, advective air flow is not implemented in the model and pressure gradients are considered negligible. Additionally, a local instantaneous thermodynamic equilibrium is assumed between the liquid and gaseous phases. Consequently, the contributions of liquid water and water vapor may be combined to define a global moisture content. Nonetheless, the contribution of water vapor to the total moisture content is negligible. Knudsen flow is not considered, and the effect of gravity is neglected since capillary forces are dominant.  
The volume fraction of (moist) air inside the porous space does not contribute to the overall thermal conductivity and specific heat capacity of the material. The formation of ice is out of the scope of the present work; thus, the temperature remains above the freezing point of water. Heat flux induced by moisture transport ([PERSON] effect) as well as moisture flux induced by temperature gradients ([PERSON] effect) are considered negligible. The coupling between heat and moisture fields will be further discussed in the following sections.  
The effect of imperfect contact interfaces in multi-layered cases is considered for moisture transport by means of hydraulic resistances [9]. For heat transfer, the impact of interfaces between adjacent materials is disregarded so that the temperature profiles are continuous between layers.
# FAmesh: Generating Frequency Adaptive Meshes from Single Images under 2D Hole Constraints
## 4 Result and Discussion
### Comparative Experiments on the Pix3D Dataset  
In this section, we evaluate the generalization performance of the proposed model on real-world datasets. Specifically, we use the model trained on ShapeNet [1] to test the Pix3D [2] dataset. Since the background of the real dataset will interfere with the effect of reconstruction, we adopt the same method as TMNET [18], which uses a mask to manually separate the reconstructed objects and uses padding and resize operations to resize the image to a size of 224 \(\times\) 224. Figure 10 shows the reconstruction results of our method, AtlasNet reconstruction, and TMNET on the Pix3D dataset. Compared to AtlasNet and TMNET, our method still handles objects with complex topologies well and is balanced in high and low curvature regions. This shows that our method can be well extended to real images.  
Figure 8: Comparison between basic patch and extended patch. (**a**) Input image; (**b**) with the basic patch, (**c**) with the extended patch, (**d**) Ground truth.  
Figure 9: Effect of patch size K on network performance.  
Our method can recover correct holes in real-world datasets because the pruning strategy after introducing hole constraints can to some extent eliminate the limitations of data-driven error estimation networks. This makes our method perform better than TMNET on the untrained dataset.
## 3 Experimental Results and Discussing
### Experimental Results
#### 3.3.3 Observation of the Defect with a Microscope
With the load increased, the misalignment and slippage of macro-molecular chain occurred caused by the fracture of secondary bond. Fiber deformation became obvious and some parts of the fiber were disintegrated. The carrying capacity of fiber was weakened by fracture and the interaction of fractured fibers, which caused the final failure. In Figure 9d, a micrograph of fracture surface of A4 was captured. The fibers in 0\({}^{\circ}\) lay-up were all fractured and no fibers were pulled out from the matrix, which indicated that the bonding between fibers and matrix was firm. Although the fibers were fractured, the matrix can still protect the fibers and distributed the load to restrain the crack growth. Some energy was absorbed by slight debonding and slippage, which improved the mechanics performance of the composites. The stress could not traverse the fibers due to the firm bonding, so the stress transferred along the surface of fibers. The distribution of the fractured fibers had an S shape. Peeling occurred in some part of the matrix. The temperature slowly rose before the specimen fractured. During the process of defect evolution, the composites strength decreased and the deformation became obvious. The energy was also absorbed by the composites. Thus, the temperature inside the composites increased. More than one part of fiber were fractured, due to the uneven strengthen distribution along the lengthwise direction. Thus, the fibers in 90\({}^{\circ}\) lay-up had a greater fracture point than the one in [45/-45]\({}_{\mathrm{s}}\) lay-up. The temperature rise at the critical moment can be calculated by Equation (39), which can be used to identify the critical moment. As shown in Figures 9a and 9b, the air bubble at this moment had evolved into crack actually. According to the fibers distribution and Equation (3), the degree of chaos of fibers (H) and the deformation rate of bubble (F) represented the effective energy efficiency density (A) directly. The more fibers were fractured, the bigger H and F were. Most of the energy was consumed by fractured fibers, which caused the reduction of A and the scattered distribution of fibers. The temperature rise at this moment was consistent with the result of Equation (39), which verified the accuracy of the quantitative relationship between temperature rise and stress.
## 4 Varying Noise Level--Speech Command Results
### Experiment SC ASR Characteristic  
In order to determine the characteristic curve describing the speech command performance within a UAV type application environment, given the custom CMU Sphinx ASR implementation, and the set  
Figure 7: Comparing all SC trendlines.  
of speech control commands, an average of the normalised trendline plotted in Figure 7 is computed and plotted. The average trendline characteristics,  
\[y_{c}(x)=\frac{y_{N_{1}}(x)+y_{N_{2}}(x)+\cdots+y_{N_{n}}(x)}{n}, \tag{14}\]  
\[=\frac{\sum_{i=1}^{n}y_{N_{i}}(x)}{n}, \tag{15}\]  
where \(y_{N_{i}}(x)\) is the normalised value of \(y\) at \(x\) dB for the \(i_{th}\) speech command. In addition, \(n=12\) since there are 12 speech commands in this case. The result is the performance characteristic curve shown in Figure 8. This curve can be used in predicting the response of the developed speech control interface for the small multirotor UAV, setting the practical limit of the current speech control interface implementation and quantifying the effects of performance modification on the implementation. In addition, other speech control methods that uses other ASR engines with different underlying theory other than Hidden Markov Model (HMM) as is the case with the CMU Sphimx, such as Amazon Echo, Microsoft Cortana, and Apple Siri, could be effectively compared with this implementation using the characteristic performance curve. However, unlike many of the alternatives, the current implementation is low-cost and works offline without relying on network connectivity to function effectively.
## 3 System Description
### System Requirements  
In order to clarify the design of the on-board components and the communication architecture of the proposed system, the set of requirements are detailed in this section. The monitoring system is based on WSNs which are able to get information about the structural health of the railway infrastructure and transmit the results to a remote control center. This system is used to increase the safety, detect issues and control the aging process of these kinds of infrastructures. The sensors integrated in the slab track have two main functions:  
1. Provide assistance in the installation phase of the slabs. The platform should provide a mechanism to configure sensors according to the terrain conditions, establishing the parameters specified during the design stage.
2. Provide information on structural integrity of the slabs and possible displacement (inclination and distance), vibration, _etc_. during their use. During the installation phase, sensors integrated on board will have to give the information necessary to: 1. Calculate the distance between slabs. This distance will not exceed 1 cm with a tolerance of \(\pm\)0.1 cm. 2. Calculate the slope of the slab. It is necessary to achieve a precision of 0.1 degree. 3. Calculate the temperature of the device. An accuracy of \(\pm\)0.1 degree is enough. During the maintenance phase, the sensors will provide the information necessary to ensure the real-time knowledge of the state of the infrastructure: 1. Acceleration readings of the slab tracks when trains are passing over them. In order to detect defects or abnormal situations, the frequency of the different vibrations obtained from the acceleration data is studied. To do this the raw acceleration data are transformed to the frequency domain by means of the Fast Fourier Transform (FFT). Due to the vibrating nature of slab tracks, acceleration must be obtained with a 0.005 g resolution, accuracy of 15% and bandwidth of 1 Hz-800 Hz. 2. Displacement of the slab relative to its original position recorded during the installation phase. 3. Alarms for low battery and sensor failure.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 3 State-of-the-Art
### State-of-the-Art in Chaos Aspect
#### 3.1.2 Chaos Sources
[80] utilized the circuit as a key generator in their proposed image encryption algorithm. By contrast, [PERSON] et al. [81] proposed a similar encryption model, but instead utilized a variant of the classic Chua circuit with a PWL memristor. In both cases, analysis showed the algorithms to exhibit strong security properties. - Memristive circuits A memristor is a form of electrical component that is capable of exhibiting chaotic behavior. [PERSON] et al. [82] proposed an image encryption algorithm that utilizes 4D memristive hyper-chaos to create chaos matrices. Security analysis showed strong security and cryptographic properties. Another image encryption algorithm was proposed by [PERSON] et al. [83] using a memristive chaotic system. The presented system demonstrates a unique property known as multistability, which further improves the chaoticness of the system. Again, security analysis showed the algorithm to possess strong cryptographic properties. - Physically Unclonable Functions (PUFs) True Random Number Generators (TRNGs), although very important in cryptography, are impossible to achieve in software. To counter this fact, [PERSON] et al. [84] proposed an encryption algorithm using a hardware device, a form of physically unclonable function, to generate true random numbers. Through extensive experiments and analysis, the TRNG was successful in passing all tests required for safe use in cryptographic algorithms.
# On the Semantics of Hybrid ASP Systems Based on Clingo
## 1 Introduction  
Answer Set Programming (ASP) is about mapping a logic program onto its so-called stable models. Over the decades, stable models have been characterized in various ways [1], somehow underpinning their appropriateness as semantics for logic programs. However, over the same period, the syntax of logic programs has continuously been enriched, equipping ASP with a highly attractive modeling language. This development also brought about much more intricate semantics, as nicely reflected by the long distance traveled from the mathematical apparatus used in the original definition [2] to the one for capturing the input language of the ASP system clingo [3].  
With the growing range of ASP applications in academia and industry [4], we also witness the emergence of more and more hybrid ASP systems [5, 6], similar to the rise of SMT solving from plain SAT solving [7]. From a general perspective, the resulting paradigm of ASP _modulo theories_ (AMT) can be seen as a refinement of ASP, in which an external theory certifies some of a program's stable models. A common approach, stemming from lazy theory solving [7], is to view a theory as an oracle that determines the truth of a designated set of (theory) atoms in the program. Meanwhile, this idea has been adapted to the setting of ASP in various ways, most prominently in Constraint ASP [5], extending ASP with linear equations over integers in various ways [8, 9, 10, 11, 12, 13, 14]. Beyond this, ASP systems such as clingo or dJwhex [15] leave the choice of the specific theory largely open. For instance, clingo merely requires fixing the interaction between theories and their theory atoms. As attractive as this generality may be from an implementation point of view, it complicates the development of generic semantics that are meaningful to existing systems. Not to mention that in ASP, the integration of theories takes place in a non-monotonic context.  
As a matter of fact, most formal accounts of existing hybrid systems are implementation driven and lack a clear elaboration of the underlying semantic principles. We address this by separating the semantic foundations of hybrid languages from the corresponding systems' design decisions. As a result, we present a framework in which we can characterize such decisions semantically. More precisely, we provide an extension of logic programsto incorporate very general abstract theories for which no predefined syntax is assumed. We extend the semantics of stable models for these programs, dealing with two types of theory atoms called _defined_ and _external_. We further classify some types of abstract theories, which we call _structured_, where we may distinguish variables inside atoms, and the semantics can be defined in terms of denotations (via functions assigning values to those variables). Finally, we explain how several existing hybrid ASP systems, such as clingcon, clingo[DL], and clingo[LP], can be accommodated as instances of this general framework.
## 2 Mathematical Programming Model
### MILP Model Formulation  
This section establishes a mixed-integer linear programming (MILP) model for the integrated scheduling problem of Earth observation satellite imaging and data transmission. The notations used for parameters in the proposed MILP model are presented in Table 2, and the decision variables of the MILP model are shown in Table 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Definition** \\ \hline \(S\) & Satellite Collection \\ \(G\) & Ground station set \\ \(T\) & Task Set \\ \(M\) & Number of Tasks \\ \(O_{k}\) & The orbit Set of satellite \(k\) \\ \(time_{i}\) & Execution time of imaging task \(i\) \\ \(slt_{i,j,k}\) & The time required for satellite \(k\) to complete the transition to task \(j\) after finishing task \(i\) \\ \(c_{i}\) & The benefit value of task \(i\) \\ \(g_{i,k}\) & The side view angle of satellite \(k\) performing task \(i\) \\ \(maxg_{i}\) & The maximum side view angle of target \(i\) \\ \(Energy_{k}\) & The energy limit for a single activation of satellite \(k\) \\ \(Memory_{k}\) & The maximum storage capacity of satellite \(k\) \\ \(m\_unit_{k/0/1}\) & The data generated or freed by satellite \(k\) per unit time \\ \(e\_unit_{k,0/1}\) & Energy consumed per second for satellite imaging or data transmission \\ \(timed_{i}\) & The time at which task \(j\) is transmitted from the satellite to the ground station \\ \(ws_{j,m,k}\) & The start time of the time window for task \(j\) executed by satellite \(k\) at orbit \(m\) \\ \(we_{j,m,k}\) & The end time of the time window for task \(j\) executed by satellite \(k\) at orbit \(m\) \\ \(wsd_{j,m,k}\) & Start of the time window for ground stations in satellite \(k\)-orbit \(m\) \\ \(wed_{j,m,k}\) & End of the time window for ground stations in satellite \(k\)-orbit \(m\) \\ \(slewts_{i}\) & Transition time between two adjacent communication tasks at Earth station \(i\) \\ \(slewts_{i}\) & Transition time between two adjacent communication tasks at Earth satellite \(i\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Notation of parameters for the proposed MILP model. Figure 1: Diagram of the example scenario.
## 4 Results
### Comparison of Algorithm Performance
#### 4.3.4 Complexity Analysis  
The handover strategy proposed in this paper combines the MPNN and DQN algorithms, and the time complexity of each message passing of the message passing neural network is O(E), where E is the number of edges. Assuming K message-passing iterations, the time complexity is O(E \(\times\) K), and the time complexity of the deep Q network is mainly in the Q-value computation and updating, and the computational complexity of each time step is O(F), where F is the number of parameters of the neural network. Assuming there are T moments, the time complexity is O(T \(\times\) F). Since our proposed strategy does not learn every moment, but learns a time slot, the time complexity of the strategy is O(E \(\times\) K + T \(\times\) F/5). The MPNN part stores the edges and the information on the edges, and the space complexity is O(E).The DQN part of the neural network has a storage complexity of O(F). Then, the space complexity of the strategy is O(E + F). The time complexity of the handover strategy based on the DQN algorithm is O(T \(\times\) F) and the space complexity is O(F). The handover strategy based on maximum elevation angle, and based on maximum remaining service time performs one conditional judgment and selection operation per time step and, assuming that there are N satellite candidates, the time complexity of each time step is O(N). With T time moments, the total time complexity is O(N \(\times\) T), and the conventional algorithms usually do not require additional complex data structures with a space complexity of O(1).  
The proposed MPNN-DQN method has higher computational and time complexity compared to traditional heuristic methods and some simpler DRL-based methods due to the additional processing of GNN layers. However, this complexity is justified by the improved performance and adaptability in dynamic satellite network environments, as shown in the experimental results.  
The training size and computational time for each strategy is given in Table 4. The machine learning-based algorithms (DQN + GNN and DRL) involve a large amount of training time and data, and require higher computational resources while delivering better performance. In contrast, handover strategies based on traditional methods such as Max Elevation and Max Servetime are computationally inexpensive, but may not achieve the same level of optimization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Training Size** & **Computing Time** & **Training Required** \\ \hline DQN + GNN & 5000 episodes & 24 h & Yes \\ DRL & 5000 episodes & 18 h & Yes \\ Max-Elevation & N/A & N/A & No \\ Max-ServeTime & N/A & N/A & No \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computing Time and Training Size.  
Figure 11: Comparison chart of delay.
## 1 Introduction
### Data-Driven Air Pollution Models  
Air pollution models are typically developed through three approaches: physics- and expert-based methods and data-driven methods. Physics-based approaches directly model the underlying physical processes related to air quality variables [17]. Examples include the Urban Airsheed Model (UAM) [18] and the Community Multiscale Air Quality (CMAQ) model [19]. Expert-based approaches, such as the expert elicitation process [20], elicit knowledge from specialists for modelling and analysis [21]. Data-driven methods establish models by identifying relationships and trends in historical data sets. Combinations of these models, for example, including expert information into data-driven methods, can also be used.  
Physics-based approaches are typically sensitive to several factors, including computational resources, the scale and quality of the parameters involved, and dependency on large databases of several input parameters, some of which may not always be available [22; 23]. Likewise, the use of expert systems is not always straightforward and it is often difficult to find agreement among experts on how the uncertainties of different variables can be adequately accounted for [20]. Recently, more practitioners have resorted to data-driven approaches, such as neural networks, as alternatives to physics- and expert-based methods [24]. Typically, data-driven methods do not require an in-depth understanding of air pollutant dynamics and other explanatory variables. However, the use of expert or known information may guide the inclusion of key variables and provide information about the nature of the expected relationship (e.g., linear or non-linear). Such methods can take advantage of the growth in low-cost air pollution sensing networks and computing technologies [25].  
In particular, BC analysis has been investigated extensively such as described in [26; 27]. BC modelling is also well developed, which mostly consists of physics-based models. Examples include the global transport model [28], regional climate-atmospheric chemistry model [29], land use regression models [30], and the BC mixing state model [31]. Recently, BC has been modelled using statistical distributions [32] and the linear mixed-effect model [33]. However, these models do not act as a proxy where BC concentrations can be estimated using other measured variables.  
While data-driven methods have been extensively used as other air pollutant estimators, a major issue is the lack of proper interpretation of the results due to the usage of non-transparent models (e.g., black-box (BB) models). Uncertainty analysis is also often neglected when the model outcomes are point-based estimates [24]. To overcome such issues, we develop a white-box (WB) model to predict BC mass concentrations using PM and PN data. The developed model for the BC proxy is then integrated with a Bayesian framework to address over-fitting and uncertainty quantification issues that typically arise in a modelling process. A Bayesian BB model is also developed for comparison.
## 3 The Security Mechanism
### Local Detection
#### 3.1.1 LD-1 Mechanism
Next node \(v\) sends its own ID and the authenticated location \(\mathrm{LOC}_{v}^{T_{v}}\) to node \(u\). Similarly, node \(u\) verifies the validity of node \(v\) using the same processes. Node \(v\) will receive a signed message \(SIG_{LK_{u}}(n_{u}\parallel n_{v}\parallel 2)\) from node \(u\) after passing authentication. Then it will regenerate a signature to compare with the signed message that was just received. If two signatures are same, node \(v\) would admit node \(u\) is a legitimate node and an authentic neighbor.