esa-sceva/satcom-chunk-collection huggingface dataset:
# SousChef System for Personalized Meal Recommendations: A Validation Study
## 3 SousChef Meal Recommender System
### Problem Formulation  
Similar to other approaches [3; 4; 17; 20], we modelled meal recommendations as a multi-objective optimization problem. We consider each slot to be planned as a meal division (_Md_), which is a part of a meal such as a lunch's starter, dinner's dessert, or fruit for a mid-afternoon snack. In this context, the slots are interdependent, as the choice for a given meal division often impacts the scores for another (e.g., lunch's main dish affects the choice for dinner's main dish). The scope to be planned (_scope_) is defined as a set of meal divisions (Figure 1). Recipes (\(R_{cp}\)) are the candidates that need to be selected for each _Md_. These need to be inserted in the system beforehand and may be suitable for multiple _Mds_, meaning that a recipe for dinner's main dish may also be suitable for lunch's main dish. The meal planning context (\(M_{pc}\)) puts together all the information available about the user--including their anthropometric data, nutritional requirements, meal history, food preferences, and restrictions--and about food, including nutritional and pricing information. Hard restriction functions may be defined that consider the meal planning context and allow filtering inadequate recipes by considering the \(M_{pc}\). The scoring function to be optimized is defined by combining a set of heuristic functions (\(H\)) reflecting different objectives. A variable number of heuristics may be defined as three types according to the scope they consider: single meal divisions (\(H_{md}\)), days (\(H_{d}\)), or week (\(H_{w}\)). A weight \(w\) is also defined for each heuristic function to define its relative importance. Each function considers the meal planning context and the recipes of the scope to attribute a normalized score to each recipe: meal division heuristics consider a single recipe for a meal division, day heuristics consider all the recipes planned for a given day attributing a score to each of them, week heuristics consider the planning for the whole week and give a score to each of those recipes. The heuristic functions are combined according to Equation (1), consisting of the sum of the weighted average of the scores attributed by the heuristics of each scope. This ensures that, even though different weights may be attributed to each heuristic function, the different scopes have the same importance and are not influenced by the number and weight of the heuristics defined for each scope. The goal is to select a combination of recipes, one for each _Md_ in the selected _scope_, that maximizes the score considering the defined heuristics (Equation (1)).  
\[Maximize\frac{\sum_{h_{md}\in H_{md}}w_{h_{md}}*h_{md}(M_{pc},R_{cp})}{ \sum_{h_{md}\in H_{md}}w_{h_{md}}}+ \tag{1}\] \[\frac{\sum_{h_{d}\in H_{d}}w_{h_{d}}*h_{d}(M_{pc},R_{cp})}{\sum_{h_ {d}\in H_{d}}w_{h_{d}}}+\] \[\frac{\sum_{h_{m}\in H_{m}}w_{h_{m}}*h_{w}(M_{pc},R_{cp})}{\sum_{h _{m}\in H_{m}}w_{h_{m}}}\]
## 4 Results and Discussion
### Outlier Classification Analysis
In addition, the size of \(l\) described above must also be set in a way that meets the purpose of use, and tuning is required at the validation stage. Meanwhile, the measured communication delay value includes regional limitations, and, depending on the experimental environment, it includes even more communication delay in a wireless communication environment that crosses countries [6]. Since this study only presents a method for probabilistically determining communication delay outliers, it may not be useful if the overall communication signal is polluted by systematic abnormalities. Therefore, it is recommended to use the proposed method considering these constraints, and the failure mode according to system addition and application must be verified afterward.
# Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources
## 2 System Model  
### Network Model  
We consider a green wireless relay network as shown in Figure 1, which consists of a source node (S), a relay node (R), and a destination node (D). The source node is powered by the electric grid, and its maximum transmit power is denoted as \(p_{S}^{\max}\). The relay is powered purely by the renewable energy which is harvested from the nature and stored in a battery, with the maximum transmit power as \(p_{R}^{\max}\). The distances between source node and relay, relay and destination node, source node and destination node are \(d_{SR}\), \(d_{RD}\) and \(d_{SD}\), respectively. We consider the performance changes of the network in N slots with length T. The slot index set is denoted as \(\mathbb{N}=\{1,2\cdot\cdots,N\}\).  
We assume that the source node has available bits to be transmitted all the time. The relay operates in a half-duplex manner that it receives data from the base station (BS) in the first half of the time slot and forwards it in the second half [11]. In each slot, there are two links for S to transmit bits: the relay link and direct link. The link assignment indicator in the \(i\)-th slot is denoted as \(I_{i}^{i}\in\{0,1\}\), with  
Figure 1: A green wireless network with an energy harvesting relay.  
\(j\in[SRD,SD]\). I\({}_{SRD}^{i}=1\) indicates that the relay link is assigned to deliver the bits, and I\({}_{SD}^{i}=1\) represents the event that the direct link is selected. In each slot, only one of these links can be selected, so  
\[\text{I}_{SD}^{i}+\text{I}_{SRD}^{i}=1,\forall i\in\mathbb{N}. \tag{1}\]  
In addition, transmission power of source node S and relay R in slot \(i\) is \(p_{S}^{i}\) and \(p_{R}^{i}\), respectively. Static power consumption is neglected in this work.  
We consider that the channel is time-varying with small-scale fading; denoted \(h_{i}^{k}\), with \(k\in\{SR,RD,SD\}\) as the channel fading factor between two nodes. In addition, all channels share the bandwidth (B) together. Therefore, once the direct link is selected in the \(i\)-th slot, the number of bits transmitted by the source node are calculated as  
\[R_{SD}^{i}=BT\log(1+h_{SD}^{i}g_{0}d_{SD}^{-\alpha}\sigma^{-2}p_{SD}^{i}), \tag{2}\]  
according to the Shannon theorem, where \(\sigma^{-2}\) is the noise variance of node D, \(g_{0}\) is the channel fading constant, \(p_{SD}^{i}\) is the transmit power of node S and \(\alpha\) is the path loss exponent. While the relay link is selected, the total bits delivered by the source node and the relay are given as  
\[R_{SR}^{i}=B\frac{T}{2}\log(1+h_{SR}^{i}g_{0}d_{SR}^{-\alpha}\sigma^{-2}p_{SR} ^{i}) \tag{3}\]  
and  
\[R_{RD}^{i}=B\frac{T}{2}\log(1+h_{RD}^{i}g_{0}d_{RD}^{-\alpha}\sigma^{-2}p_{R}^{ i}), \tag{4}\]  
where \(p_{SR}^{i}\) is the transmit power of node S when selecting the relay link.
# Optimization of Pin Type Single Screw Mixer for Fabrication of Functionally Graded Materials
## 2 Materials and Methods
### Governing Equations  
In kinetic analysis, a fluid can be regarded as a continuous medium, and its motion adheres to the principles of conservation of mass, momentum, and energy. Among these principles, the conservation of energy is commonly employed for calculations in systems involving heat exchange flows. For the mixing and extrusion processes conducted at room temperature in this study, the mixing pastes were considered incompressible, and heat transfer in the mixing chamber was considered negligible. In addition, the two pastes utilized in this study are classified as non-Newtonian fluids due to their high viscosity. Thus, it was assumed that heat transfer could be disregarded under isothermal conditions, while the influence of gravity was taken into account. The flow of incompressible multicomponent viscous slurries was described using the simplified Navier-Stokes equations [49]. Finally,the continuity equation and momentum conservation equation governing fluid motion are depicted in Equations (1) and (2), respectively.  
\[\
abla\cdot(\rho\mathbf{v})=0 \tag{1}\]  
\[\rho\frac{\partial v}{\partial t}+\rho v\cdot\
abla\mathbf{v}=-\
abla p+\
abla\cdot \mathbf{\tau}+\mathbf{G} \tag{2}\]  
where \(\rho\) represents the fluid density with unit of kg/m\({}^{3}\), \(\mathbf{v}\) is fluid velocity. \(p\) stands for static pressure on the fluid, and \(t\) is time. \(\mathbf{\tau}\) indicates the stress tensor, and \(\mathbf{G}\) is the gravity.
## 3 Vehicle Model and Experimental Setup  
### Mathematical Modelling  
To study vehicle ride comfort, a _7-dof_ vehicle model including suspension is considered to exhibit adequate characterisation. Figure 2 shows the full car ride model. \(A_{i}\), \(B_{i}\) and \(C_{i}\) correspond to the axle, suspension-to-body attachments and ground-wheel contacts, respectively.  
The vehicle ride model has 4 _dof_corresponding to wheel junces \(z_{1}\) through \(z_{4}\) and an additional 3 _dof_ for bounce \(z\), roll \(\phi\) and pitch \(\theta\), respectively. The ground displacements at four wheel-contact points are given by \(u_{1}\) through \(u_{4}\), respectively, and vertical dynamics are represented by (1)-(7).  
Figure 1: Proposed workflow of the estimation method.  
\[M_{\rm s}\ddot{z}= -K_{f}z_{\rm ff}-K_{f}z_{\rm ff}-K_{r}z_{\rm rf}-K_{r}z_{\rm rr}\] \[-C_{f}z_{\rm ff}-C_{f}\dot{z}_{\rm ff}-C_{r}\dot{z}_{\rm rf}-C_{r} \dot{z}_{\rm rr}-M_{\rm s}g \tag{1}\] \[I_{xx}\ddot{\theta}= K_{f}l_{f}z_{\rm ff}+K_{f}l_{f}z_{\rm ff}+C_{f}l_{f}\dot{z}_{\rm ff }+C_{f}l_{f}\dot{z}_{\rm ff}\] \[-K_{r}l_{r}z_{\rm rf}-K_{r}l_{r}z_{\rm rr}-C_{r}l_{r}\dot{z}_{\rm rf }-C_{r}l_{r}\dot{z}_{\rm rr}\] \[I_{yy}\ddot{\phi}= -K_{f}bz_{\rm ff}-K_{r}bz_{\rm rf}-C_{f}b\dot{z}_{\rm ff}-C_{r}b \dot{z}_{\rm ff}\] \[+K_{f}bz_{\rm ff}+K_{r}bz_{\rm rr}+C_{f}b\dot{z}_{\rm rr}+C_{r}b \dot{z}_{\rm rr}\] (3) \[M_{\rm uf}\dot{z}_{1}= K_{f}z_{\rm ff}+C_{f}\dot{z}_{\rm ff}-K_{t}z_{\rm t,ft}-C_{t} \dot{z}_{\rm t,ft}-M_{\rm uf}g\] (4) \[M_{\rm uf}\dot{z}_{2}= K_{f}z_{\rm ff}+C_{f}\dot{z}_{\rm ff}-K_{t}z_{\rm tr}-C_{t} \dot{z}_{\rm t,ft}-M_{\rm uf}g\] (5) \[M_{\rm ur}\dot{z}_{3}= K_{r}z_{\rm rH}+C_{f}\dot{z}_{\rm rf}-K_{t}z_{\rm r,tt}-C_{t} \dot{z}_{\rm tr}-M_{\rm ur}g\] (6) \[M_{\rm ur}\dot{z}_{4}= K_{r}z_{\rm rr}+C_{f}\dot{z}_{\rm rr}-K_{t}z_{\rm tr,rr}-C_{t} \dot{z}_{\rm tr,rr}-M_{\rm ur}g \tag{7}\]  
(1)-(3) represent the ride, pitch and roll dynamics, respectively, while (4)-(7) represent the four wheel jomote motions. The effective suspension travel (difference between the body motion and unsprung mass motion at individual wheel corners) and wheel travel (effective unsprung mass motion obtained by subtracting the ground excitation) of the front left/right and the rear left/right are given by (8):  
\[z_{\rm ff}=z_{1}-y_{1}, z_{\rm rr}=z_{2}-y_{2},\] \[z_{\rm rf}=z_{3}-y_{3}, z_{\rm rr}=z_{4}-y_{4}, \tag{8}\] \[z_{\rm t,ft}=y_{1}-u_{1}, z_{\rm t,fr}=y_{2}-u_{2},\] \[z_{\rm t,rI}=y_{3}-u_{3}, z_{\rm t,rr}=y_{4}-u_{4}.\]  
In (8), \(z_{1}\) through \(z_{4}\) are the vertical displacements of the suspension-to-body attachment points that are given by (9):  
\[z_{1}=z-l_{f}\theta+b\phi, z_{2}=z-l_{f}\theta-b\phi, \tag{9}\] \[z_{3}=z+l_{r}\theta+b\phi, z_{4}=z+l_{r}\theta-b\phi.\]  
In (9), it has been assumed that the vehicle pitch and roll motion happens about its centre of gravity, which is at a distance of \(l_{f}\) from the front and \(l_{r}\) from the rear axle; it is assumed that there is left-right symmetry, and the half track width of the vehicle is given by \(b\), as shown in Figure 2.  
Figure 2: Vertical dynamics of the vehicle body.
# Effects of Water-Soaking Height on the Deformation and Failure of Coal in Uniaxial Compression
###### Abstract  
The stability of water-preventing coal pillar plays an important role in preventing gob water inrush. The gob side of the water-preventing coal pillar is soaked in a certain height of mine water. Different soaking heights may affect the stability of coal pillars. Few studies have been conducted on the properties of coals with different water-soaking heights. We carried out uniaxial compressive tests on coal specimens with different water-soaking heights to gain a better understanding of different water-soaking-height-induced weakening characteristics of coal. Results show that: (1) The water content of coal specimens increases with the soaking height. Water significantly weakens the strength of coal specimens. However, the extent of strength weakening of the coal specimen does not increase with the increase of the soaked height. The strength of the fully soaked coal specimen is lowest among all groups of coal specimens. The strength of the three groups of partially soaked coal specimens is between the fully soaked coal specimens and the coal specimens without being soaked in the water. In the three groups of partially soaked coal specimens, the strength of the coal specimens increases with the increase of the soaking height. (2) The acoustic emission activities of complete water soaking and nonsoaking coal specimens are relatively concentrated, occurring mainly in unstable fracture expansion stage and post-peak destruction stage, and acoustic emission exhibits main-shock mode. Partially soaking coal specimens, especially the 25% water-soaking height and 50% water-soaking height coal specimens, produces obvious acoustic emission activities during the fracture expansion stabilization phase, and then generates more acoustic emission activities during the unstable expansion stage and the post-peak stage. The acoustic emission presents foreshock--main shock mode. (3) The softening effect of the water soaking on the coal specimens is obvious. It was further found that the deformation of coal specimens with partial water soaking is not synchronized in different layers, the nonuniform deformations of partially soaked coal specimens aggravate its damage.  
water-soaking height; AE characteristics; local strain characteristics; nonuniform deformation +
Footnote †: journal: Applied Physics and Chemistry
## 2 Theoretical Background
### Residual Network  
The residual network was proposed by Microsoft Labs in 2015, and its advantage lies in the introduction of the residual module [21]. The depth of a model impacts the intricacy of the extracted features and the quantity of model parameters. Nevertheless, deeper models are susceptible to challenges such as gradient explosion or vanishing. The residual network addresses this issue by employing the residual module [22; 23]. This approach significantly improves the training depth and speed of the network while enhancing its feature extraction capability. There are several types of residual networks, such as ResNet18, ResNet50, and ResNet101. Numbers represent the number of layers in the residual network. ResNet18 has a moderate number of layers and fast training speed and belongs to the deep separable CNN architecture, which is widely used in classification tasks. In response to the relatively small scale and high real-time requirements of wire rope breakage damage classification tasks, this paper selects ResNet18 as the main network model, and its structural model is shown in Figure 2.  
The ResNet18 architecture begins by performing convolution calculations on a two-dimensional RGB image with three channels and an input size of 224 \(\times\) 224 \(\times\) 3. Then, four residual modules are used, each consisting of two convolutional layers and a skip connection. The four residual modules are stacked twice and finally inputted into a fully connected layer. The extracted broken wire damage features are used to predict the probability of classification results through a SoftMax classifier. Figure 3 illustrates the structure of the first residual module (residual module I) in ResNet18. This module starts with a 3 \(\times\) 3 convolution operation with 64 channels, followed by batch normalization and the ReLU activation function to enhance the network's convergence speed. The output of this operation is then added to the input feature map using the ReLU activation function. This process is repeated once, resulting in a convolution size of 56 \(\times\) 56 \(\times\) 64. This is followed by the convolution of the second residual module, whose residual module II is shown in Figure 4. The second residual module uses a 128-channel convolution kernel with a size of 28 \(\times\) 28. In order to add to the output feature map of the previous residual module, a 1 \(\times\) 1 \(\times\) 128 convolution needs to be added to the skip connection for up-sampling and down-sampling. The skip connection can directly connect the input to the output, allowing the network to learn residual information and better perform feature extraction and processing. The ability of residual networks to learn identity maps is mathematically represented by the following function:  
\[F(x)=H(x)-x \tag{2}\]  
Figure 2: Residual network.  
where the \(x\) represents the input vector of the neural network, \(F(x)\) represents the residual mapping that needs to be learned, \(F(x)\) adds the corresponding elements to \(x\), and the expected output vector is \(H(x)\). When \(F(x)=0\), an identity mapping can be formed.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
###### Abstract
Thus, in addition to using a testbed for verification and validation purposes of the INS, the same testbed, augmented with cybersecurity testing functionality, can be used to analyze the cybersecurity of the INS. In this paper we first investigate the constituents of the INS in terms of sub-components, services, data, data flow, communication protocols, interfaces, connections, and dependencies, to define an INS reference architecture. Then we review and analyze existing bridge testbeds in terms of tools, architectural models, capabilities, functionalities, communication protocols, research instances, standards, and frameworks, towards defining the architecture of an INS testbed capable also to be used for analysing the cybersecurity posture of the INS under study. Hence, our contribution is threefold:  
1. We provide a complete list of INS constituents, with all components and their interactions presented, along with associated international rules, regulations and standards;
2. We provide a systematic literature review of publications on bridge testbeds;
3. We propose an architecture for a Cyber-Physical Range, i.e., a cybersecurity-enabled testbed for the INS. The novelty of the contribution is the definition of a reference architecture for the INS, that provides a detailed description of all components as defined in a large number of standards, regulations, and guidelines of international organizations in the maritime domain, most notably the IMO. This, together with an analysis of existing bridge testbeds has allowed the development of an architecture for an INS testbed with cybersecurity testing capability. The development of such a testbed is a significant step towards cybersecurity experimentation in the maritime domain. The remaining of the paper is organized as follows: Section 2 provides the necessary background and discusses the related work on all topics relevant to the work herein. Section 3 discusses the constituents of the INS and their interactions. Section 4 describes our proposal for the architecture of a cybersecurity-enabled INS testbed. Finally, Section 5 summarizes our conclusions and outlines possible directions of further research.
# Hardware Model Checking Algorithms and Techniques
## 3 Bounded Model Checking
#### Example of Bounded Model Checking  
Let \(\mathcal{M}=\langle V,\mathcal{I},T\rangle\) be a transition system described by the following:  
\[V=\{v_{0},v_{1}\} \tag{45}\]  
\[\mathcal{I}=\
eg v_{0}\land\
eg v_{1} \tag{46}\]  
\[T=(\
eg v_{0}\land\
eg v_{1}\wedge v_{0}^{1}\land\
eg v_{1}^{1})\lor \tag{47}\] \[(v_{0}\land\
eg v_{1}\land\
eg v_{0}^{1}\wedge v_{1}^{1})\quad\lor\] \[(\
eg v_{0}\wedge v_{1}\land\
eg v_{0}^{1}\wedge v_{1}^{1})\quad\quad\lor\] \[(v_{0}\wedge v_{1}\land\
eg v_{0}^{1}\land\
eg v_{1}^{1})\]and \(P\) be an invariant property over \(V\):  
\[P=\
eg v_{0}\vee\
eg v_{1} \tag{48}\]  
Figure 5 illustrates the transition systems here described, for reference. The initial state, 00, is marked with the leftmost ingoing edge. The only bad state, 11, is highlighted with a line hatched filling.  
An algorithm based on bounded model checking is capable of identifying a counterexample to the target property in four steps, by applying iteratively the BMC formula described in Definition 83.  
In the first step, the algorithm would pose the following query:  
\[(\
eg v_{0}\wedge\
eg v_{1})\wedge(v_{0}\wedge v_{1}) \tag{49}\]  
which is UNSAT.  
In the second step, the algorithm would pose the following query, in which, for the sake of compactness, we represent with \(T(V^{i},V^{i+1})\) the aforementioned transition relation, at a given timeframe:  
\[\begin{split}(\
eg v_{0}\wedge\
eg v_{1})\wedge\\ T(V^{0},V^{1})\wedge\\ (v_{0}^{1}\wedge v_{1}^{1})\end{split} \tag{50}\]  
which is still UNSAT.  
In the last step, the algorithm would pose the following query:  
\[\begin{split}(\
eg v_{0}\wedge\
eg v_{1})\wedge\\ T(V^{0},V^{1})\wedge\\ T(V^{1},V^{2})\wedge\\ T(V^{2},V^{3})\wedge\\ (v_{0}^{3}\wedge v_{1}^{3})\end{split} \tag{51}\]  
which is SAT, hence identifying the counterexample to the property, which does not hold in state 11.
###### Abstract  
To solve the problem that noise seriously affects the online monitoring of parts signals of outdoor machinery, this paper proposes a signal reconstruction method integrating deep neural network and compression sensing, called ADMM-1 DNet, and gives a detailed online vibration signal monitoring scheme. The basic approach of the ADMM-1 DNet network is to map the update steps of the classical Alternating Direction Method of Multipliers (ADMM) into the deep network architecture with a fixed number of layers, and each phase corresponds to an iteration in the traditional ADMM. At the same time, what differs from other unfolded networks is that ADMM-1 DNet learns a redundant analysis operator, which can reduce the impact of outdoor high noise on reconstruction error by improving the signal sparse level. The implementation scheme includes the field operation of mechanical equipment and the operation of the data center. The empirical network trained by the local data center conducts an online reconstruction of the received outdoor vibration signal data. Experiments are conducted on two open-source bearing datasets, which verify that the proposed method outperforms the baseline method in terms of reconstruction accuracy and feature preservation, and the proposed implementation scheme can be adapted to the needs of different types of vibration signal reconstruction tasks.  
deep neural network; ADMM-1 DNet; compressed sensing; deep unfolding network Article
# CycleGAN-Driven MR-Based Pseudo-CT Synthesis for Knee Imaging Studies
## 2 Materials and Methods
### Cycle-Consistent Generative Adversarial Network  
As previously mentioned, in this work we leverage a sophisticated and innovative deep learning framework known as a CycleGAN. The CycleGAN is a state-of-the-art deep neural network architecture that has exhibited remarkable capabilities in the domain of medical image synthesis. Actually, it is designed to tackle the complex challenge of converting MR images into pseudo-CT images given difficult, unpaired, or unregistered MR-CT datasets. This approach is unique in that it harnesses the power of adversarial networks and cyclic consistency to ensure that the final synthesized pseudo-CT images not only look authentic but also possess structural accuracy.