esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Beacons Dynamic Transmission over TSCH
## 2 Time Slotted Channel Hopping Background and Related Work
### TSCH Background
If a node is configured to send all enhanced beacon (EB) frames on the same \(chOffset\), due to the channel hopping nature of TSCH, this \(chOffset\) translates to a different frequency on different slotframe cycles. As a result, EB frames will be sent on all available frequencies, increasing the probability of successful reception. The TSCH coordinator is in charge of forming the TSCH network. The coordinator establishes important values such as the network ID, the slotframe size, and the frequency hopping sequence (the \(\mathcal{F}\) vector), which will be shared by the nodes incorporated in the network. Then, it creates the network by setting the \(ASN\) to 0, a value that increases by one at the end of each slot [12]. The coordinator and other nodes associated with the network (also known as synchronizers) periodically transmit EBs, which include important information needed to manage the time-synchronized network. If a node wants to join the network, it must turn on its radio and start looking for possible EB messages, going through the channels of the channel hopping sequence. Upon receipt of a valid EB, the node associates with the network and begins sending EB frames periodically to announce the presence of that network. Figure 2 shows a linear TSCH network whose coordinator node is node 0, and each node can only communicate with its adjacent neighbor. The actions of sending and receiving EBs are illustrated, as well as the scanning of available channels (in this case, four channels) in search of EBs. As shown in the figure, the coordinating node starts sending EBs. Nodes seeking to associate with the network \"listen\" to each channel for a certain amount of time and only manage to associate when an EB is being transmitted on the channel that is being scanned. Once the EB is received, they become synchronizers and send EBs to broadcast the presence of the network. However, the IEEE 802.15.4e standard does not define the EB  
Figure 1: Slotframe structure example with parameters: slotframe length 7 slots, \(slotOffset=3\), and \(m=16\) available channels. advertising policy; that is, it does not indicate which cells in the slotframe to use to send EBs, nor does it define the rate at which they should be sent.
## 3 Solution
### Master Stage
#### 3.2.6 Reduction of the Hyper-Ellipse Radius  
The radius in the VSA represents a variable that balances the exploration and exploitation of the solution space during the iterative procedure. The original VSA approach proposed in [30] recommends the implementation of the incomplete Gamma function to continuously reduce the radius of the hyper-ellipse; however, to simplify the implementation of this algorithm in any programming environment, an exponential adaptation is used in this research for the radius variation reported in [32] and as defined in (17).  
\[r_{t+1}=\sigma_{0}\bigg{(}1-\frac{t}{t_{\max}}\bigg{)}e^{\big{(}-\gamma_{1} \frac{t}{t_{\max}}\big{)}}, \tag{17}\]  
where \(\gamma_{1}\) is an adjustable parameter that governs the speed of reduction of the radius. This parameter has been used heuristically in previous literature as 6 [8] because it emulates incomplete Gamma function with 95% of similarity. Please note that \(t_{\max}\) is the maximum number of iterations performed in the optimization process.
# Sage-Husa Algorithm Based on Adaptive Double Forgetting Factors
## 2 Adaptive Sage-Husa Filtering Algorithm  
### Sage-Husa Algorithm with the Forgetting Factor  
Consider the following nonlinear system:  
\[X_{k}=f(X_{k-1},U_{k-1})+W_{k-1} \tag{1}\]  
\[Z_{k}=h(X_{k},U_{k})+V_{k}, \tag{2}\]  
where \(X_{k}\) is the n-dimensional state vector of the system; \(Z_{k}\) is the m-dimensional measurement vector; \(f\) and \(h\) are the state transition and the measurement functions, respectively; and \(u_{k}\) is the input variable of the system. \(W_{k-1}\) and \(V_{k}\) are assumed to be uncorrelated Zero-mean white Gaussian noise with covariances \(Q_{k-1}\) and \(R_{k}\), respectively, represented as \(E(W_{k-1})=E(V_{k})=0\), \(E\Big{(}W_{k-1}W_{k-1}^{T}\Big{)}=Q_{k-1}\), \(E(V_{k}V_{k}^{T})=R_{k}\), and \(E\big{(}W_{k-1}V_{k}^{T}\big{)}=0\).  
The Sage-Husa algorithm, jointly proposed by [PERSON] and [PERSON], facilitates online estimation of the measurement noise covariance matrix and the process noise covariance matrix. This enables the estimation of the statistical properties of the measurement noise and the process noise based on the recent measurement update information, thereby enabling the optimization of the filter. The Sage-Husa algorithm of a nonlinear system can be described as:  
\[P_{k|k-1}=\Phi_{k-1}{P_{k-1}}{\Phi_{k-1}}^{T}+Q_{k-1}, \tag{3}\]\[\hat{X}_{k|k-1}=f\Big{(}\hat{X}_{k-1|k-1},U_{k-1}\Big{)}, \tag{4}\]  
\[e_{k}=Z_{k}-h\Big{(}\hat{X}_{k|k-1}\Big{)}, \tag{5}\]  
\[R_{k}=(1-d_{k})R_{k-1}+d_{k}\Big{(}e_{k}e_{k}{}^{T}-H_{k}P_{k|k-1}H_{k}{}^{T} \Big{)}, \tag{6}\]  
\[K_{k}=P_{k|k-1}H_{k}{}^{T}\Big{(}H_{k}P_{k|k-1}H_{k}{}^{T}+R_{k}\Big{)}^{-1}, \tag{7}\]  
\[P_{k|k}=(I-K_{k}H_{k})P_{k|k-1}(I-K_{k}H_{k})^{T}+K_{k}R_{k}{}^{K}{}_{K}{}^{T}, \tag{8}\]  
\[\hat{X}_{k|k}=\hat{X}_{k|k-1}+K_{k}e_{k}, \tag{9}\]  
\[Q_{k}=(1-d_{k})Q_{k-1}+d_{k}\Big{(}K_{k}e_{k}e_{k}{}^{T}K_{k}{}^{T}+P_{k|k}- \Phi_{k-1}P_{k-1|k-1}\Phi_{k-1}{}^{T}\Big{)}, \tag{10}\]  
\[d_{k}=(1-b)/\Big{(}1-b^{k}\Big{)}, \tag{11}\]  
where \(\Phi_{k}\) is the Jacobian of the state transition function \(f\), \(\Phi_{k}=\frac{\partial f(X_{k})}{\partial X}\Big{|}_{X_{k}=X_{k|k}}\), \(P_{k|k-1}\) represents the state prediction error covariance matrix, \(e_{k}\) is the innovation, \(H_{k}\) is the Jacobian matrix of the measurement function \(h\), \(H_{k}=\frac{\partial h(X_{k})}{\partial X}\Big{|}_{X_{k}=X_{k|k-1}}\), \(K_{k}\) is Kalman gain, \(P_{k|k}\) denotes the estimation error covariance matrix, and \(b\) is the forgetting factor in the range of 0.95 and 0.99.
## 2 System Description  
Figure 1 illustrates the layout of the Kirkuk gas power plant in Iraq, which serves as the case study for this research. The plant comprises three units of gas turbines: K1, K2, and K3. This study focuses on unit K1, a Siemens V94.3A gas turbine model, which typically has an electrical output capacity of approximately 150 megawatts (MW).  
Figure 2 displays the suggested hybrid system's outline involving the Brayton cycle (BC). It begins with the air compressor (AC) compressing air into the combustion chamber (CC), where it mixes with NG and burns. The resulting combustion gases drive the gas turbine (GT), producing energy in the generator (GEN). An integrated system combining RC and ORCs is added to maximize the benefit of the high-temperature exhaust gases discharged into the environment. Additionally, the system is enhanced with a PTC solar field to boost power output from the steam Rankine cycle by leveraging the substantial solar radiation potential in Kirkuk City. The working fluid selected for the ORCs is R123 due to its numerous advantages in combined power plant cycles, particularly in Organic  
Rankine Cycles (ORCs). R123's low boiling point allows for the efficient recovery of low-temperature waste heat, significantly enhancing overall system efficiency [33]. Additionally, it boasts excellent thermal efficiency, making it ideal for lower-temperature heat sources such as geothermal energy or waste heat from primary cycles. R123 is also non-flammable, chemically stable, and provides a safer option for operational use.
# Improving a Rapid Alignment Method of Tomography Projections by a Parallel Approach
## 3 Results and Discussion  
To measure the algorithm acceleration, we benchmarked each module against the corresponding block in the algorithm implementation [12] in Tomopy [7]. The system used for the testing is an HPC node, whose hardware and software configuration is summarised in Table 1.  
While initially Tomopy offered GPU reconstruction algorithms solely through interfaces [47; 50] to a single-GPU solution, in a very recent version, a native 3D multi-GPU reconstruction algorithm can be invoked during the alignment procedure. However, no other parts of the algorithm are currently parallelised. The recent scenario represents the baseline configuration we used in our manuscript.  
Observation Variables  
To verify the working principle of our method, it is crucial to check for a reduction in the execution time of each step of the proposed algorithm; this is done trough _benchmarks_. As we mentioned, to generate an aligned dataset, the procedure requires the steps described in Section 2 (see Figure 2: the reconstruction, the motion estimation, and the frame warp. That is why this section is divided into (1) \"CT module benchmark\"; (2) \"Motion estimation benchmark\"; and finally, (3) \"Warp Module benchmark\". Due to the complexity of the inner working, as the CT module will be difficult to use by calling the RAW functions, an easy-to-import API has been written to ease the import into a custom code; a test code will be presented.  
\begin{table}
\begin{tabular}{c c} \hline \hline CPU &
\begin{tabular}{c} Intel(R) Xeon(R) CPU E5-2643 v4 @ 3.40 GHz \\ 24 hyper-threading core, 20 available (virtualisation) \\ \end{tabular} \\ \hline GPU & 2\(\times\) Nvidia Tesla k80, 4 available processors \\ \hline Virtualisation system & proxmox-ve: 6.1-2 (kernel: 5.3.13-1-pve) \\ \hline Virtual machine OS & Ubuntu 18.04 LTS (kernel 5.0.0-29-generic) \\ \hline Python & 3.9.5 Anaconda \\ \hline CUDA & 11.1 \\ \hline PyTorch [55] & 1.9 \\ \hline Scikit-Image [54] & 0.18.1 \\ \hline ASTRA Toolbox [42] & 1.9.9-dev1 \\ \hline TomoPy [7] & 1.10.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: System configuration for the algorithm testing.  
If no further processing is required (e.g., beads removal, deconvolution, etc.), eventually the reconstruction generated by the method (as a by-product of the alignment procedure) can be considered final.  
As we are devoted primarily to the speed of execution and considering that our implementation is working in a numerically accurate manner that follows the original algorithm [12], our results are mainly based on speed comparisons only.
# A New Affinely Adjustable Robust Model for Security Constrained Unit Commitment under Uncertainty
## 4 AARO Model for Security Constrained Unit Commitment (SCUC)
### Uncertainty Sets  
A brief description of the uncertainty sets used in our model is introduced in this section. The interested reader may consult [45; 59] for an in-depth explanation on this topic. As previously stated, in Section 4.1, an uncertainty set can be polyhedral or ellipsoidal. For instance, [PERSON] et al. [68] adopted an ellipsoidal uncertainty set within the AAROUC model to fit the spatial-temporal correlated wind power. Additionally, they proposed and tested a new criterion to select the value of the level of uncertainty. Conversely, we use uncertainty boxes (i.e., a polyhedral uncertainty set) \(U^{box}\) for each one of the sources of uncertainty considered in our AAROUC model;  
\[U^{box}_{w,t}=|\varepsilon_{w,t}|\leq\varepsilon^{max}_{w,t}\leftrightarrow -\varepsilon^{max}_{w,t}\leq\varepsilon_{w,t}\leq\varepsilon^{max}_{w,t} \qquad\forall w\in W,t\in T \tag{52}\]  
\[U^{box}_{p,t}=|\varepsilon_{p,t}|\leq\varepsilon^{max}_{p,t}\leftrightarrow -\varepsilon^{max}_{p,t}\leq\varepsilon_{p,t}\leq\varepsilon^{max}_{p,t} \qquad\forall p\in P,t\in T \tag{53}\]  
\[U^{box}_{s,t}=|\varepsilon_{s,t}|\leq\varepsilon^{max}_{s,t}\leftrightarrow -\varepsilon^{max}_{s,t}\leq\varepsilon_{s,t}\leq\varepsilon^{max}_{s,t} \qquad\forall s\in S,t\in T \tag{54}\]  
Equations (52)-(54) represent the intervals for each uncertainty variable: realized wind forecast error \(\varepsilon_{w,t}\in[-\varepsilon^{max}_{w,t},\varepsilon^{max}_{w,t}]\), materialized solar forecast error \(\varepsilon_{p,t}\in[-\varepsilon^{max}_{p,t},\varepsilon^{max}_{p,t}]\) and materialized demand forecast error \(\varepsilon_{s,t}\in[-\varepsilon^{max}_{s,t},\varepsilon^{max}_{s,t}]\). These intervals are symmetric and zero-centered, that is, realizations of uncertainty variables could be zero (i.e., \(\varepsilon_{w,t}=\varepsilon_{p,t}=\tilde{\varepsilon}_{s,t}=0\)). Likewise, \(\tilde{\varepsilon}^{max}_{w,t},\tilde{\varepsilon}^{max}_{p,t}\), and \(\tilde{\varepsilon}^{max}_{s,t}\) are the maximum uncertainty level forecasts that limit each interval. These quantities, also known as the budget value of the uncertainty set, are generally input data.
# Multi-Field Models of Fiber Reinforced Concrete for Structural Applications
## 4 Electro-Mechanical Analyses with Applications to Self-Sensing
### Solution Scheme  
The electro-mechanical coupling is established using a staggered solution scheme. The base resistance of the numerical specimen is calculated for the case of no applied loading. Loading is then applied incrementally. For each load increment, the nodal positions are determined. The associated element strains are then used to calculate updated values of composite resistance. For the examples considered herein, planar arrays of nodes are placed along the negative and positive \(x\)-faces of the 3D domain (Figure 5). These nodes serve as electrode surfaces or grids for imposing a potential difference in the \(x\)-direction. The planar positioning of the nodes also facilitates the calculation of electrical current between the opposing faces.  
Fibers are placed uniformly at random within the cell. At this stage of model development, lower fiber contents are analyzed and therefore fiber-to-fiber contact is not a consideration. The aspect ratio of the fibers is \(l_{f}/d_{f}=50\), where \(l_{f}\) is \(0.5L\). The conductivity ratio is set at \(\sigma_{f}/\sigma_{m}=1000\). Whereas this ratio is much smaller than that between conductive fibers and concrete, which can be on the order of \(10^{8}\)[13], it is sufficient for examining basic operations of the model. For the discrete (volumetric) modeling of fibers, the fibers are discretized as indicated in Figure 2b. For both the discrete and semi-discrete models, random filling of the matrix volume with nodal points employed a minimum distance constraint of \(L/33\).  
The influence of conductive fibers on potential flow is illustrated in Figure 6, which is based on earlier simulations with lesser amounts of fibers. Flow initiating on the left side of the figure moves within and between the conductive fibers. The color coding of the streamlines is not a quantitative indicator but was done to distinguish the different flow paths.
# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
## 4 Experiment  
This section evaluates the performance of our proposed method through experiments and a results analysis. The first part describes the source of the data, the second part introduces the evaluation indicators, and the third part describes the training settings of the LSTM-CRF model. Finally, two sets of experiments show that the LSTM-CRF model with the integrity algorithm provides better results compared with the LSTM-CRF model and POS syntactic rules and our proposed method has good domain independence.
# Surface Subsidence Modelling Induced by Formation of Cavities in Underground Coal Gasification
## 4 Discussions
### Surface Subsidence Prediction Model  
To determine the subsidence at any point in the surface moving zone due to the UCG process, it is necessary to establish the subsidence equation of the surface subsidence. The established coordinate is shown in Figure 15.  
Figure 14: Maximum surface subsidence vs. cavity volume.  
It is noted that the surface coordinates' integral extends to the point on the ground where the coordinates are (\(x\), \(y\)). When the upper surface of the excavation space is an inclined plane, the upper-part surface equation is,  
\[z=ax+by+c \tag{8}\]  
where \(a\), \(b\), and \(c\) are plane parameters. The surface subsidence prediction model is  
\[W(x,y)=\iint\limits_{D}W_{e}(x,y)\sqrt{1+a^{2}+b^{2}}dxdy \tag{9}\]  
In order to validate the subsidence prediction accuracy of the proposed model, the numerical simulation results of four cavities were compared to that of the proposed model (Equation (9)). Figure 14 shows the comparison of the subsidence of the numerical simulation with that of the proposed model.  
The calculated maximum subsidence value was 10.2 mm, which was a small difference from the numerical value. The maximum difference between numerical and calculated deformations was 4.6%, which is acceptable for engineering analysis. Additionally, the numerical surface subsidence was greater than the calculated value, plotted in Figure 16. The reason was that the interaction between cavities was not considered in the proposed model. Actually, the interaction between cavities would affect the surrounding ground deformation. Hence, it is indicated that the proposed method can be used to analyze the ground deformation induced by UCG.  
In order to illustrate the proposed model for actual mine gasification, an example was given with three gasification cavities, taken from [PERSON] and [PERSON] (1982) [1]. Data for this example were normalized. Figure 17 shows the comparison of the proposed model and field data. The curve tendency of the proposed model was the same as that of the field data. It is noted that the two profiles were closest over the first cavity, whereas large discrepancies were observed over the other cavities. The whole error between the two curves was less than 17.8%. Hence, the proposed model can be used to calculate subsidence profiles for multiple cavities.  
Figure 16: Subsidence comparison between numerical simulation and proposed model.
## 5 Performance Evaluation
### Performance Evaluation
#### 5.2.4 Influence of Data Popularity on Transmission Performance  
The influence of data popularity Zipf(\(\alpha\)) on transmission performance is shown in Figure 10. The skewness of data requests is in the range of 0.6 to 1.2 following Zipf's distribution. With the increase in \(\alpha\), more popular content will be cached in more intermediate network nodes; the cache hit rate will also increase, while the download time will be reduced. The two network coding schemes achieve a higher cache hit rate than others. Both of them can support content requests from multiple content providers and aggregate the encoded blocks of the same content to reduce the amount of data transmitted by the network. Compared with NDN-Multicast, they can reduce the download time by approximately 41.6%. At the same time, IDEANE's value is roughly 10.8% lower than that of NetCodCCN.  
Figure 10: Influence of data popularity on transmission delay.  
Figure 9: Influence of bottleneck network bandwidth on transmission delay.
## 2 The Proposed Approach
### Concepts and Rules for Multi-Attribute Three-Way Decisions
#### 2.2.5 Rules for Multi-Attribute Three-Way Decisions  
To provide semantic interpretations of the thresholds and three regions in probabilistic rough sets, [PERSON] [63] created decision theoretic rough sets (DTRS) with the use of Bayesian theory. Table 5 lists the two states and three actions that make up a DTRS model.  
For \(\forall x\in U\), we denote \(R(a_{i}|[x])\) as the expected loss when taking the action \(a_{i}(i=P,\,\,B,\,N)\), and according to Table 5 it can be calculated as [62]:  
\[R(a_{P}|[x])=\lambda_{PP}Pr(C[x])+\lambda_{PN}Pr(\
eg C[[x]) \tag{3}\]  
\[R(a_{B}|[x])=\lambda_{BP}Pr(C[[x])+\lambda_{BN}Pr(\
eg C[[x]) \tag{4}\]  
\begin{table}
\begin{tabular}{c c c} \hline \(A_{i}\) & \(C_{j}(P)\) & \(\
eg C_{j}(N)\) \\ \hline \(a_{P}\) & \(\lambda_{PP}\) & \(\lambda_{PN}\) \\ \(a_{B}\) & \(\lambda_{BP}\) & \(\lambda_{BN}\) \\ \(a_{N}\) & \(\lambda_{NP}\) & \(\lambda_{NN}\) \\ \hline \end{tabular}
\end{table}
Table 5: The loss function.  
\[R(a_{N}|[x])=\lambda_{NP}Pr(C[[x])+\lambda_{NN}Pr(\
eg C[[x]) \tag{5}\]  
The conditional probability of an object \(x\) belonging to \(C\), given that the object is described by its equivalence class \([x]\), can be expressed as \(Pr(C[[x])\)[64]. Note that \(Pr(\
eg C[[x])=1-Pr(C[[x])\).  
According to Bayesian theory, the best decision is the one with minimum loss, so rules for multi-attribute three-way decisions can be indicated as follows:  
\[\text{If }min(R(a_{P}|[x]),\ R(a_{B}|[x]),\ R(a_{N}|[x]))=R(a_{P}|[x]), \text{ decide }x\in POS(C)\] \[\text{If }min(R(a_{P}|[x]),\ R(a_{B}|[x]),\ R(a_{N}|[x]))=R(a_{B}|[x]),\text{ decide }x\in BND(C)\] \[\text{If }min(R(a_{P}|[x]),\ R(a_{B}|[x]),\ R(a_{N}|[x]))=R(a_{N}|[x ]),\text{ decide }x\in NEG(C)\]
## 2 Dynamic Modeling  
As shown in Figure 2, a typical SFF system includes a rotational actuator, a flexible joint, a hub, a slender link, and a tip payload. \(J_{\mathrm{m}}\), \(\theta_{\mathrm{m}}\), and \(\tau\) are used to denote the moment of inertia of the motor, the angular displacement of the actuator, and the torque applied to the rotational actuator. The flexible joint is considered as a purely torsional spring with constant stiffness \(k\). The hub is assumed as a cylinder with a diameter \(R\) and the inertia \(J_{\mathrm{h}}\). The flexible link with uniform cross-sections is described with bending stiffness \(EI\) and a length \(L\) having density per unit length \(\rho\). The payload is attached to the flexible link with the mass \(m_{\mathrm{p}}\) and the moment of inertia \(J_{\mathrm{p}}\). The motion of the slender link is limited in the horizontal plane regardless of gravity. \(O-xy\) frame is denoted as the inertial frame of the SFF. The relative coordinate system \(O-x^{\prime}y^{\prime}\) is referred to as the TCS with the axis \(x^{\prime}\) along the undeformed link. \(\theta\) represents the angular displacement of the slender link in the inertial frame in Figure 4.  
The location of curvilinear points on the flexible link consists of a rigid location of the undeformed link and a transversal deflection due to the flexibility. The rigid location vector of a point is expressed as \(r_{\mathrm{rl}}\). The transversal deflection vector is denoted as \(r_{\mathrm{t}}=[u,v]^{\mathrm{T}}\), which is related to the location of the curvilinear point \(s\) along the link and time. \(r_{\mathrm{h}}\) is given as the location vector pointing to the proximal end of the link. Therefore, the location of the point on the link is obtained as  
\[r_{\mathrm{fl}}=r_{\mathrm{h}}+r_{\mathrm{rl}}+r_{\mathrm{t}} \tag{1}\]  
The original location of the point with respect to the inertial frame is denoted as \((x,0)\). The rotational matrix from the relative coordinate frame to the inertial global frame is denoted as  
\[\mathbf{T}= \begin{bmatrix}\cos\theta&-\sin\theta\\ \sin\theta&\cos\theta\end{bmatrix} . \tag{2}\]  
Figure 4: Kinematic modeling of an SFF system. By combining Equations (1) and (2), the location vector is transferred as  
\[r_{\text{fl}}= \begin{bmatrix}\cos\theta&-\sin\theta\\ \sin\theta&\cos\theta\end{bmatrix}\begin{pmatrix}\begin{bmatrix}x\\ 0\end{bmatrix}+\begin{bmatrix}u\\ v\end{bmatrix}\end{pmatrix} , \tag{3}\]  
Based on the assumption of the inextensible flexible link, the following constraint equation is obtained as  
\[\left(1+\frac{\partial u}{\partial s}\right)^{2}+\left(\frac{\partial v}{ \partial s}\right)^{2}=1. \tag{4}\]  
To incorporate the stiffening effect in an SFF system, the potential energy of the system is composed of the potential energy of the flexible link \(V_{\text{L}}\) due to bending, the energy \(V_{\text{A}}\) caused by the axial shortening and the inertial force, and the potential energy of the torsional spring.