esa-sceva/satcom-chunk-collection huggingface dataset:
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 5 Theory and the Application of Different Types of Graphs
### Agreement Graph and Bipartite Graph  
An _agreement graph_ is an undirect graph \(G=(V,E)\) where \(V\) is the set of jobs (nodes) and \(E\) is a set of edges connecting a pair of agreeing jobs. _Agreeing jobs_ are jobs that can be scheduled simultaneously on separate machines [117]. The agreement graph can be very useful when resources are non-sharable [117].  
Figure 10: Collaborative network for the dataset 10 by 6 [116].  
A _bipartite graph_, also known as a bigraph, is an undirected graph consisting of two disjoint independent sets of nodes, \(A\) and \(B\), where \(V=A\cup B\) Every edge connects a node in \(U\) to a node in \(V\). Agreement graphs often are bipartite graphs since only pairs of agreeing jobs are graphed. Figure 11 shows a simple example of an agreement graph.  
A tangible example of school exam planning can help the understanding of the concept of agreeing nodes (jobs) in the agreement graph of a shop scheduling problem [117,118]: suppose \(n\) exams need to be scheduled, equivalent to \(n\) jobs. Exams are scheduled to take place in \(m\) classrooms, equivalent to \(m\) machines, which can accommodate any exam. The objective is to minimize the examination period. The coinciding agreement graph for this situation would be an undirected graph, \(G=(V,E)\) such that \(V\) includes all of the jobs, \(J_{i}\) (exams), and \(E\) includes edges between jobs \(J_{i}\) and \(J_{j}\) if there are no students who take both exams (corresponding to those jobs) at the same time [117].
## Materials and Methods
### The Spiral STC Algorithm in an Emergency Environment  
In the _Spiral STC_ (STC) _on-line_ algorithm, a vehicle applies using coverage path algorithm relating to information in an emergency domain.  
**STC _on-line_ Algorithm**:  
**Sensors**: Vehicle position and direction sensors, detect using sensors.  
**Input**: Location of start mega-cell S and no information about the environment.  
**Recursive function**: STC (w, x), where x is the current node, w is the parent node in spanning tree.  
**Initialization**: STC (null, S).  
**Algorithm STC:**  
Spiral-STC(w,x):  
1. Mark the current a root cell x as \"visited\"  
2. While x still has an \"unvisited\" adjacent that is not completely occupied by an obstacle or vehicle.  
2.1. Search for new neighbours of x in an anti-clockwise direction. Call y the first neighbor of x.  
2.2. Build a connection edge between x and y.  
2.3. If y is completely free:  
From x move to a sub-cell of y along the output edge of x.  
2.4. If y is partially occupied by an obstacle or vehicle:  
2.4.1. Build C-Space space at y.  
2.4.2. From x just moving along the new contour of C-Space at y has just followed the access edge of y to return to x.  
2.5. Call STC (x, y)  
3. End the loop.  
4. If x differs from S, move back to x.  
5. Returns results (STC(w, x) stops).
## 1 Introduction  
Ultra-high-performance concrete (UHPC) has been shown to have far superior mechanical and durability properties relative to conventional concretes [1, 2]. Due to this performance, the research community has been increasingly interested in understanding its behavior to more efficiently use UHPC in conventional construction. The increased cost of proprietary UHPC mix designs has limited its use to applications requiring relatively small quantities of the material, such as joint material for bridge elements [3, 4] and overlays to existing bridge decks [5]. However, the development of non-proprietary mix designs with comparable performance to proprietary mix designs [6, 7] has made it more economical to use UHPC as a direct replacement for conventional concrete in structural applications.  
A requirement for using UHPC as a full structural element is accurately modeling its response in the structure for design purposes. Accurate analysis of structures requires failure criteria to predict when a material will fail in the analysis model. Numerous studies have been conducted to determine the failure criteria of UHPC in biaxial [8, 9] and triaxial compression stress conditions [10, 11]. [PERSON] and [PERSON] have also collected comprehensive failure data for UHPC in multiple stress states [12] and used that data to create a series of equations to better form the concrete's failure surface [13].  
Noticeably absent is data representing portions of the failure surface with two or more principal stresses in tension. In the case of conventional concrete, the absence of this data did not hinder design and analysis efforts since the tension strength is typically ignored due to its relatively low value when compared to its compressive strength [14]. However, UHPC has a much higher tensile strength, especially when incorporating steel fibers. Ignoring the tensile behavior of UHPC could be considered overly conservative, may not accurately depict behavior, and would ignore the contribution of the most expensive component of UHPC--the steel fibers. Therefore, a new test method is necessary to fill the gap in tensile data that would better inform failure models. With multiaxial tensile data, a complete failure surface could be developed to help improve the understanding of UHPC behavior as well as the analysis and design of UHPC structural elements. This study focuses on designing, fabricating, and evaluating a new test setup able to apply tension to materials (specifically concrete) in all three principal directions simultaneously to provide additional data for the material's failure surface.
# Single-Shot Full Characterization of the Spatial Wavefunction of Light Fields via Stokes Tomography
## Appendix C Spatial Stokes Tomography for Vector Modes  
The outputs of the spatial Stokes tomography mentioned in the main text, i.e., \(\langle\Psi(\mathbf{r})|\partial_{0\text{-}3}(\mathbf{r})|\Psi(\mathbf{r})\rangle\), are a set of mutually unbiased real-valued functions, or rather, spatial distributions of Stokes parameters, given by the following:  
\[\left\{\begin{array}{l}S_{0}(\mathbf{r})=\big{|}\sqrt{1-a}\psi_{R}\big{|}^{2}+ \big{|}\sqrt{a}\psi_{L}\big{|}^{2}\\ S_{1}(\mathbf{r})=\big{|}\psi_{H}\big{|}^{2}-\big{|}\psi_{V}\big{|}^{2}\\ S_{2}(\mathbf{r})=\big{|}\psi_{D}\big{|}^{2}-\big{|}\psi_{A}\big{|}^{2}\\ S_{3}(\mathbf{r})=\big{|}\sqrt{1-a}\psi_{R}\big{|}^{2}-\big{|}\sqrt{a}\psi_{L} \big{|}^{2}\end{array}\right. \tag{12}\]  
where \(\psi_{H,V,D,A,L,R}\) denote spatial probabilities or polarization-dependent spatial modes observed in the projections along the horizontal, vertical, diagonal, antidiagonal, left-, and right-circular polarizations, respectively. Notice that \(\psi_{H(D)}\) and \(\psi_{V(A)}\) are not usually orthogonal to each other, only in the case when \(a=0.5\). With the observations in Equation (A7), we can in situ reconstruct the polarization-resolved beam profile of the vector mode, expressed as follows:  
\[\mathbf{p}(\mathbf{r})=S_{0}(\mathbf{r})\otimes\hat{\rho}(\mathbf{r}).\] (A8)  
Here, \(S_{0}(\mathbf{r})\) describes the intensity profile of the beam, while \(\hat{\rho}(\mathbf{r})=\frac{1}{2}\sum_{0,3}S_{n}(\mathbf{r})\hat{\sigma}_{n}\) is a density matrix function that describes a spatially varying polarization distribution covered by the beam profile, as shown in Figure 1a. According to Equations (A7) and (A8), the tomography can be further simplified by using only H, V, D, and L polarization projections. On this basis, in order to implement the simplified tomography within a single-shot measurement, we adopt a nonpolarizing beam splitter (NPBS) with four output ports and an integrated polarizing filter (PF), as shown below:
## 2 Analysis of Unbalanced NPV
### Applicability under Different Modulation Degree  
Generally, the modulation degree M of the three-phase converter is the ratio of the modulating wave amplitude to the carrier wave amplitude. The value of M is also calculated to be equal to two times the ratio of the three-phase phase-voltage amplitude to the DC bus voltage value, as shown in the following equation.  
\[\text{M}=\frac{\text{V}_{m\_\text{amp}}}{\text{V}_{c\_\text{amp}}}=\frac{\text{ E}_{n\_\text{amp}}}{\text{V}_{\text{dc}}/2} \tag{10}\]  
where V\({}_{m\_\text{amp}}\) is the modulating wave amplitude, V\({}_{c\_\text{amp}}\) is the carrier wave amplitude, E\({}_{n\_\text{amp}}\) is the three-phase phase-voltage amplitude, and V\({}_{\text{dc}}\) is the amplitude of the DC bus voltage \(v_{\text{dc}}\).  
In general, the modulation degree is 0 \(<\) M \(<\) 1. Under special conditions, over-modulation (M \(>\) 1) will occur. If the modulation degree is too large or too small, the selection of switching method or voltage vector will be affected. In addition to the method for the control of the current and voltage of the whole circuit, the proposed NPV control strategy is an auxiliary control strategy and is specifically designed for the NPV control, and mainly implemented by adjusting the selection method of two zero vectors, \(\mathbf{V}_{00}[000]\) and \(\mathbf{V}_{07}[111]\), which is complementary to the whole control system of the circuit.  
For different situations of modulation degrees, the proposed NPV control strategy is not to directly control the circuit according to the zero-voltage vector selected by the control algorithm of the whole circuit control system, but to adjust the selection of the two zero-voltage vectors according to the NPV deviation. For example, in case of low modulation degree, the probability of occurrence of the zero-voltage vector \(\mathbf{V}_{00}[000]\) is much greater than that of \(\mathbf{V}_{07}[111]\); in the case of a high modulation degree, the probability of occurrence of the zero-voltage vector \(\mathbf{V}_{07}[111]\) is greater than that of \(\mathbf{V}_{00}[000]\). However, according to the principle of the NPV control strategy, regardless of the selection of the two zero-voltage vectors by the control system of the whole circuit, the allocation and adjustment of \(\mathbf{V}_{00}[000]\) and \(\mathbf{V}_{07}[111]\) will be targeted according to the NPV deviation, instead of controlling the circuit directly according to the allocation of the two zero-voltage vectors derived from the comparison of the modulating wave and the carrier wave. Therefore, the NPV control strategy proposed is not affected by the magnitude of the modulation degree.
## 5 Experiments
### Parameter Analysis  
In Equation (4), \(\beta\) is a crucial hyperparameter that controls the contribution of different contextual features to the final POI representation. These POI representations, in turn, influence the modeling of user preferences and ultimately impact the performance of the model in POI recommendation. To investigate the effect of \(\beta\) on model performance, we vary \(\beta\) in {0, 0.2, 0.4, 0.6, 0.8, 1.0} and observe the changes in model performance. The experiments are conducted on the NYC dataset with the length of the recommendation list fixed at 10. The experimental results are presented in Figures 2 and 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gowalla** & **Recall@5** & **Recall@10** & **Recall@15** \\ \hline STATRL & 0.285 & 0.284 & 0.324 \\ \hline Neu-PCM & 0.242 & 0.275 & 0.301 \\ \hline CAPRI & 0.341 & 0.397 & 0.452 \\ \hline SCR & 0.432 & 0.492 & 0.511 \\ \hline BSA-ST-Rec & 0.468 & 0.526 & 0.547 \\ \hline GNN-POI & 0.475 & 0.538 & 0.556 \\ \hline RELINE & 0.481 & 0.543 & 0.571 \\ \hline MCGRec & 0.489 & 0.558 & 0.583 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performances of all methods on Gowalla in terms of Recall.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gowalla** & **Precision@5** & **Precision@10** & **Precision@15** \\ \hline STATRL & 0.066 & 0.061 & 0.056 \\ \hline Neu-PCM & 0.044 & 0.039 & 0.037 \\ \hline CAPRI & 0.103 & 0.078 & 0.058 \\ \hline SCR & 0.142 & 0.121 & 0.074 \\ \hline BSA-ST-Rec & 0.163 & 0.139 & 0.111 \\ \hline GNN-POI & 0.174 & 0.153 & 0.129 \\ \hline RELINE & 0.189 & 0.172 & 0.141 \\ \hline MCGRec & 0.218 & 0.188 & 0.161 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performances of all methods on Gowalla in terms of Precision.  
Figure 2: Performance of MCGRec with different \(\beta\) on NYC in terms of Precision.  
When \(\beta\) is set to 0 or 1, it implies that MCGRec discards either the geographical context or the temporal context, considering only a single type of contextual feature when modeling POI representations. The experimental results show that in both cases, the model's performance is suboptimal, indicating that modeling POI representations based on a single context is insufficiently accurate. Furthermore, the results demonstrate that as the value of \(\beta\) changes, the model's performance first increases and then decreases, with the optimal \(\beta\) value not being 0.5. This suggests that the impact of different contexts on the final POI representation is not uniform, indicating that one context may be more influential than the other in certain scenarios when modeling POI representations.
# An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN
## 2 Brief Review of Common Models
### Hybrid and Artificial Neural Network-Based Models  
The next generation of the propagation models relies on artificial neural networks (ANNs). These models are based on the training of an ANN with empirically collected data. Usually this involves training the ANN from scratch, where the ANN has to learn to derive the very basic mechanisms of radio propagation. For instance, the ANN even needs to learn the trivial fact that increasing the link distance has a negative impact on the signal strength. It is the ANN's responsibility to learn the FSPL from the distance and frequency of transmission or understand the attenuatingimpact of obstacles on the LoS. A review of the ANN-based models is provided in [19]. The drawbacks of this approach include:  
1. the time-consuming and exhaustive data collection process that is required for the training of ANNs. As an instance, in [20], authors collected 600,000 data samples in a relatively small indoor environment and trained the network for several hours. Not only is such data collection in a small environment tedious but it also defeats the purpose of estimation [19].
2. ANN requires considerable training time as it contains numerous neurons in each layer; furthermore, an overly complex ANN may lead to data overfitting and hence failing to reach a generalized solution [21; 22].  
To address these challenges, a hybrid model for a simple indoor environment was proposed [19]. The model comprised an optimized multiwall model (MWM), whose estimations were monitored by an ANN. Therefore, the ANN only had to learn and compensate the deficiencies of the MWM. This strategy drastically reduced the required training data samples and improved the accuracy of the estimation. A relatively similar strategy was employed in [23; 24] using the COST W.I. There are, however, two drawbacks of the latter implementations:  
1. Selecting an appropriate and flexible model for a complicated outdoor environment where many propagation mechanisms are involved is challenging. In fact, [PERSON] et al. [19] had to slightly adjust the MWM according to their requirement.
2. Conventional models are not tailored or flexible enough to represent the novel characteristics of communication devices. For instance, to the best of our knowledge, there is no propagation model that takes the spreading factor (s\({}_{\text{m}}\)) of a chirp spread spectrum modulation (CSS) [25] into consideration. Theoretically, a higher s\({}_{\text{m}}\) provides a higher processing gain and, therefore, improves the range. Including such technology-dependent parameters not only increases the modeling accuracy and makes the models more compatible with new devices, but it also helps modeling the signal propagation of the whole system rather than estimation of the path loss only.
# Improving Juvenile Age Estimation Based on Facial Landmark Points and Gravity Moment
## 4 The Proposed Method
#### Age Prediction  
Generally, age estimation aims to predict age based on the mapping from an input vector to an output age space. In this proposal, for an unlabeled facial image \(img\), we projected its landmark feature vector \(LMM_{(img)}\) into the age group space using the matrix \(wMtrx_{new}\). Equation (12) was utilized for the projection where the projected value represents the closeness of the image belonging to the class; the bigger the value, the higher the possibility of assigning the age to the age class.  
\[Project(img)=wMtrx_{new}\cdot LMM_{(img)} \tag{12}\]  
The following Algorithm 1 presents the LaGMO age estimation process.  
```
1\(LMM_{(img)}\) complete;
2 While tdic do;
3 Train;
4 Input:\(LMM_{(img)}\) Output:\(wMtrx\)
5 for\((LMM_{(img)}\in wMtrx),...,n\)do;
6 compute new term matrix by Equation (10);
7 endfor;
8 Save \(wMtrx_{new}\);
9 Test;
10 Input:\(img\) Output:\(age(img)\)
11 for\(LMM_{(img)}\in wMtrx_{new}),...,n\)do;
12 Estimate age by Equation (12);
13 Save \(age(img)\);
14 endfor;
15 Output:\(age(img)\)
16 endwhile;
```  
**Algorithm 1**The LaGMO age estimation process.
## 4 Results  
### Rock-Eval Pyrolysis, Vitrinite Reflectance Data, and Source Rock Quality  
In E\({}_{3}\)s\({}_{1}\) of the Huanghekou Depression, the TOC average value was 1.88%. In E\({}_{3}\)s\({}_{2}\) of the study area, the TOC average value was 1.03%. Further, in E\({}_{2}\)s\({}_{3}\) of the study area, the TOC average value was 1.87%, while in E\({}_{2}\)s\({}_{4}\), the TOC average value was 2.01% (Table 1).  
A comprehensive assessment of the source rocks in the study area showed that the predominant organic matter types could be classified as Type II\({}_{2}\) in E\({}_{3}\)s\({}_{1-2}\), indicating poor source rock and good source rock, while in E\({}_{2}\)s\({}_{3}\), the dominant organic matter types could be categorised as Type II\({}_{1}\)-II\({}_{2}\), with a minor presence of Type III, suggesting poor source rock and good source rock, and in E\({}_{2}\)s\({}_{4}\), the primary organic matter types consisted mainly of Type II\({}_{2}\) accompanied by a small amount of Type III, indicating medium source rock and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Well**} & \multirow{2}{*}{**Depth (m)**} & **Shahejie** & **TOC (\%)** & **Ro (\%)** & **T\({}_{\text{max}}\) (\({}^{\circ}\)C)** & **I\({}_{\text{H}}\) (mg HC/g\_TOC)** & **S\({}_{1}\) + S\({}_{2}\) (mg/g)** \\  & & **Formation** & **Average** & **Average** & **Average** & **Average** \\ \hline B36-1 & 2260 & E\({}_{2}\)s\({}_{3}\) & 2.5 & 0.46 & 434 & 513.2 & 13.25 \\ \hline \multirow{4}{*}{K2-1} & 3300–3380 & E\({}_{3}\)s\({}_{1}\) & 0.7 & 0.71 & 445 & 242.37 & 2.04 \\  & 3420–3500 & E\({}_{3}\)s\({}_{2}\) & 0.8 & 0.64 & 444.67 & 226.83 & 2.94 \\  & 3540–3700 & E\({}_{2}\)s\({}_{3}\) & 1.13 & 0.67 & 446 & 268.22 & 3.51 \\ \hline \multirow{4}{*}{K3-2} & 2800–2890 & E\({}_{3}\)s\({}_{1}\) & 0.66 & 0.6 & 422.71 & 273.94 & 1.93 \\  & 2900–2970 & E\({}_{3}\)s\({}_{2}\) & 0.67 & 0.65 & 372 & 297.42 & 2.12 \\  & 3000–3210 & E\({}_{2}\)s\({}_{3}\) & 0.9 & 0.71 & 393.36 & 314.89 & 3.03 \\ \hline \multirow{4}{*}{K3-4} & 3110–3200 & E\({}_{3}\)s\({}_{1}\) & 0.42 & 0.58 & 433.25 & 269.97 & 1.35 \\  & 3290–3390 & E\({}_{2}\)s\({}_{3}\) & 0.75 & 0.64 & 433.6 & 295.16 & 2.63 \\ \cline{1-1}  & 3420 & E\({}_{2}\)s\({}_{3}\) & 1.16 & 0.74 & 411 & 258.63 & 3.72 \\ \hline \hline \end{tabular} Note: “Depth” represents the buried depth of each sample with a deduction of water depth; TOC: total organic carbon; S\({}_{1}\) + S\({}_{2}\): free hydrocarbons + potential hydrocarbons; Ro: vitrinite reflectance; T\({}_{\text{max}}\): temperature of maximum hydrocarbon generation rate.  
\end{table}
Table 1: _Cont._good source rock (Figure 2a,b). The Ro analysis of the source rock indicated that E\({}_{3}\)s\({}_{1}\) of the Huanghekou Depression was between the immature and mature stages, E\({}_{3}\)s\({}_{2}\) was in the low-maturity-to-mature stage, and E\({}_{2}\)s\({}_{3}\) was also between the low-maturity and mature stages, whereas E\({}_{2}\)s\({}_{4}\) reached the mature stage (Figure 2c).
## 4 Simulation and Analysis  
Extensive simulations are conducted to evaluate the performance of the proposed content placement and user association algorithm. In the simulation, 7 MBSs are uniformly distributed in the considered area, while SBSs and UEs randomly drops, the maximum number of BSs that a UE can be associated is 3 [25]. Major parameter settings are listed in Table 1.
## 2 Methods
### Theory Analysis of Combiner Branch
#### 2.1.1 Impedance Transformation Analysis  
In a combiner, conventional 50 \(\Omega\) RF networks employ 70.7 \(\Omega\) quarter-wavelength lines for the 50 \(\Omega\) to 100 \(\Omega\) impedance transformation. This function is realized through a capacitor-loaded parallel microstrip structure, such as that shown in Figure 1, which simultaneously widens the lines and shortens their lengths.  
To achieve an impedance transformation of 50 \(\Omega\) to 100 \(\Omega\) at the operating frequency, a multi-section microstrip structure with lumped capacitive compensation was proposed, as shown in Figure 1. The design comprised two microstrip lines (electrical length \(\beta\)1\({}_{1}\)), one line (\(\beta\)1\({}_{2}\)), and a shunt capacitor C, all sharing the characteristic impedance Z\({}_{0}\). Through the optimization of \(\beta\)1\({}_{1}\), \(\beta\)1\({}_{2}\), Z\({}_{0}\), and C, impedance matching between the source (Z\({}_{\rm s}\) = 50 \(\Omega\)) and load (Z\({}_{1}\) = 100 \(\Omega\)) was realized. The analytical process is detailed below:  
(1) After passing through the transmission line \(\beta\)1\({}_{1}\), the load Z\({}_{\rm I}\) satisfies the following [13; 14]:  
\[Z_{in2}=Z_{0}\frac{Z_{I}+Z_{0}*\tan(\beta\)1\({}_{1})}{Z_{0}+Z_{I}*\tan(\beta\)1 \({}_{1})} \tag{1}\]  
(2) As one terminal of the capacitor C is connected to point B, the following relationship holds:  
\[Z_{c}=\frac{1}{j\omega C} \tag{2}\]  
(3) By equivalently transforming the capacitor to point B, the total impedance at point B can be expressed as follows:  
\[\frac{1}{Z_{B}}=\frac{1}{Z_{in2}}+j\omega C=>Z_{B}=\left(\frac{1}{Z_{B}}+j \omega C\right)^{-1} \tag{3}\]  
Figure 1: High-power microstrip line hybrid structure with lumped capacitive loading.  
(4) Consequently, the input impedance of the transmission line \(\beta\)l\({}_{2}\) is given by the following:  
\[Z_{in1}=Z_{0}\frac{Z_{B}+Z_{0}*\tan(\beta l_{2})}{Z_{0}+Z_{B}*\tan(\beta l_{2})} \tag{4}\]  
(5) The final expression for the source impedance Zs is as follows:  
\[Z_{\rm s}=Z_{0}\frac{Z_{in1}+Z_{0}*\tan(\beta l_{1})}{Z_{0}+Z_{in1}*\tan(\beta l _{1})} \tag{5}\]  
Substituting Equations (1)-(4) into (5) yields the complete Zs-Z1 relationship. Setting the right Zs and Zl, the target impedance transformation can be achieved by optimizing \(\beta\)l\({}_{1}\), \(\beta\)l\({}_{2}\), \(Z_{0}\), and C. Of course, multiple parameter sets satisfy Equation (5), enabling selection based on criteria such as the characteristic impedance, bandwidth, power capability, and capacitance value.