esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Network Coordination in Omni-Channels
Thus, such mechanisms are sought that allow for the consistency of operations and continuity of processes, regardless of the factors affecting the flows of finished goods [65]. In research on network coordination, authors point to VMI, CPFR, and others. These are detailed tools in flow management; however, they do not exhaust the impact on the logistical problems that face the coordinator of distribution networks, supply networks, or supply chains. Intuitively, the flow mechanisms identified by network coordination researchers touch on key areas for flow management, including forecasting and resources management. In our proposed construct of logistics coordination in networks of cooperating organisations, we have adopted a broader spectrum, taking into account mechanisms analysed separately by different authors:  
* Network participants' resources management from the logistics operator level [66],
* Forecasting network flows [67],
* Organisation of transport and emergency transport [68],
* Logistical information management from the logistics operator level [69],
* Demand management [13; 52],
* Managing human resources and infrastructure in the network [70]. The implementation of such assumptions will extend the current theory of network coordination with logistic mechanisms allowing for the coordination of flows in complex distribution systems, which are omni-channel systems.
## 2 Preamble
### Wavelet Transform and Logarithmic Observation Scales
From an original signal, measured at a resolution of 1, it is then possible to obtain its decomposition \(\mathcal{N}(f)_{I}=\left(A_{2^{-j}}f,(D_{2^{j}}f)_{-J\leqslant j\leqslant-1}\right)\) up to an order \(J>0\). This set of discrete signal setsis called an orthogonal wavelet representation and it represents the signal at a coarse resolution \(A_{2^{-j}}\) as well as all associated detail signals at the intermediate resolutions \(2^{j}\) with \(-J\leqslant j\leqslant-1\). The advantage of this wavelet decomposition is that the change of scale is here logarithmic, whereas it was linear for the windowed Fourier transform. This affects the shape of the observation cells in the space \(x/\omega\). Assuming that the function \(\psi\) has zero mean, a standard deviation \(\sigma_{x}\) and its Fourier transform \(\hat{\psi}\) centered around \(\omega_{0}\) with a standard deviation of \(\sigma_{\omega}\), the cells described by different wavelets of the form \(\psi_{s}(x-x_{0})\) cover the area \([x_{0}-(\sigma_{x}/s);x_{0}+(\sigma_{x}/s)]\times[s\omega_{0}-s\sigma_{ \omega};s\omega_{0}+s\sigma_{\omega}]\), as illustrated in Figure 5b. It is clearly shown that when the observation scale \(s\) is small, the spatial resolution is coarse, while the frequency resolution is fine. When the observation scale \(s\) increases, the spatial resolution becomes finer but the frequency domain also increases in size. The spatial resolution is therefore not the same from one scale to another. It is perfectly suited to the study of non-stationary signals composed of several frequency components by making it possible to locate specific frequency components in the signal.
Most of the above studies focus on zoned scanning and changing the scanning offset angle to make the sample have better properties in a specific direction. Nevertheless, there are few studies on the change in the scanning strategy between the forming layers. In this paper, the combined method of LINE scanning, CHESS scanning, and STRIPE scanning strategies combined with a directional angle offset scanning strategy was studied to explore the surface quality, mechanical properties, and microstructure of L-PBF samples under different processing methods. The purpose of this experiment was to find the optimal process combination to directionally enhance the mechanical properties of the specimen while having better surface quality.
# A Control-Oriented Linear Parameter-Varying Model of a Commercial Vehicle Air Brake System
## 4 Linearization Through System Identification
Within the PRBS GUI, the identified model order and parameter \(q\) (number of Markov parameters) can be tuned to optimize the identified model accuracy and a closed-loop linear system model can be obtained in the state-space form in term of discrete-time system matrices \(A\), \(B\), \(C\), and \(D\). The parameters used for system identification are shown in the Table 4.  
The discrete-time state-space model is transferred into the continuous-time one and then into a continuous-time transfer function for the air brake system with 5.8 bar supply pressure and 2 bar reference pressure; see Equation (30) below. \[G_{CL}(s)=\frac{-1.3s+249.1}{s^{2}+6.75s+250} \tag{30}\]  
Using Equation (22), a second order system plant model can be generated below:  
\[G_{P}(s)=\frac{-1.6s+311.38}{s^{2}+8s+0.9} \tag{31}\]  
In Figure 8, the responses of the nonlinear model with proportional gain 0.8 and the second-order linear system model are compared. One can see that the linear system model is a non-minimal phase with one zero at 194.6 on the complex plan, leading to an overshoot in the system response for an over-damped system. This is mainly caused by the time delay in the nonlinear air brake system since the time delay is often approximated by a non-minimal phase transfer function through system identification. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference Pressure** & **2 Bar** & **3 Bar** & **4 Bar** \\
**Supply Pressure** & **5.8 Bar** & **5.8 Bar** & **5.8 Bar** \\ \hline Gain K & 0.8 & 0.35 & 0.4 \\ PRBS magnitude & 0.4 & 0.6 & 0.4 \\ Input sample rate (s) & 0.12 & 0.12 & 0.12 \\ Output sample rate (s) & 0.01 & 0.01 & 0.01 \\ Input/output sample ratio & 12 & 12 & 12 \\ Markov parameter & 20 & 25 & 23 \\ ID open-loop model order & 2 & 2 & 2 \\ \hline \end{tabular}
\end{table} 
Table 4: Parameters for Pseudo-Random Binary Signal (PRBS) system identification. Figure 8: Comparison between the nonlinear and linear models. where \(\theta=\left[\theta_{1},\theta_{2}\right]^{\text{T}}\) is a varying parameter vector. The LPV system model matrices are  
\[ \begin{split} A&=\left[\begin{array}{cc}0&0\\ 1&0\end{array}\right]-\left[\begin{array}{cc}1&0\\ 0&0\end{array}\right]\theta_{1}-\left[\begin{array}{cc}0&1\\ 0&0\end{array}\right]\theta_{2}\\ B&=\left[\begin{array}{cc}1&0\end{array}\right]^{\text{T}},\;\;C=\left[ \begin{array}{cc}-0.2&34.4\end{array}\right]\theta_{1}\end{split}  \tag{36}\]  
The scheduling parameters (\(\theta_{1}\), \(\theta_{2}\)) are a function of brake chamber pressure defined in Table 6. To validate the LPV model, the comparison results between LPV model and nonlinear model with proportional gain 0.8, 0.35, 0.4 under different reference pressure are shown in Figure 11. It is observed clearly that the LPV model is close to the nonlinear model.
### Binary System Azeotrope  
Figure 2 presents a plot of equilibrium data for a 'positive' azeotrope of the type used in the cleaning of paintings. In the plot, the horizontal axis represents the composition of the mixture. While commonly represented as mole fraction or % m/m, for consistency in discussing the azeotrope compositions as given in the conservation literature, the horizontal axis in this graph shows the concentration of Component A in the mixture as a volume percentage (% v/v). The vertical axis shows the temperature of the mixture. In Figure 2, the lower point X of the equilibrium data represents the composition and temperature at which the azeotrope exists. The value of % v/v\({}_{\mathrm{AZ}}\) is the volume percentage of Component A in the azeotrope. The value T\({}_{\mathrm{AZ}}\) represents the temperature at which the azeotrope boils at a given experimental pressure, which for conservation applications would be ambient pressure of 1 atm or 760 mmHg.  
The equilibrium data plot delineates how different mixtures' boiling or evaporation processes occur as shown in Figure 2. For example, if a solvent mixture is prepared at a % v/v lower than the % v/v\({}_{\mathrm{AZ}}\) (shown in point a), it reaches its liquid/gas equilibrium state at point 1 when this solution heats up. As the mixture boils (or evaporates), the gas phase composition will approximate the azeotrope at point X. In contrast, the liquid phase moves along the lower liquid/gas curve towards point 2. If no more boiling (or evaporation) is allowed, the mixture that remained in the liquid phase will now have a % v/v equal to that at point c, meaning that the liquid is now less rich in Component A. Thus, as the original mix had a composition a, the remaining liquid will have a lower composition c after evaporation. At the same time, the gas phase will have a composition equal to the azeotrope.  
On the other hand, if a mixture is prepared with a % v/v higher than the % v/v\({}_{\mathrm{AZ}}\) (shown as point b in Figure 2), then as the solution heats up and boils (or evaporates)  
Figure 2: Temperature–composition equilibrium data plot at constant pressure of a homogeneous pressure-maximum azeotrope.  
at point 3, the gas phase will move toward the azeotrope point X while the liquid phase composition will move towards point 4. If the boiling is stopped, the new liquid phase composition will have a higher % v/v in Component A at point d. Thus, the liquid phase will increase in concentration of Component A as the mixture is boiled. As a useful generalization, a zeotropic composition (i.e., non-azeotrope) will shift its liquid composition in the direction opposite to the azeotropic point (x\({}_{\text{AZ}}\)) during evaporation.
## 3 Data and Empirical Results  
In Section 3.1, the data are described and the empirical findings are reported in Section 3.2, including the accuracy of the prediction models, the forecast under the Vine-COPAR, and the Granger causality based on the Vine-COPAR models.
## 3 Results
### Case Discussion  
Two women who visited our hospital were presented as case studies (Figure 4). Both patients were diagnosed with HLD L4/5 on opposite sides. Both patients had lower-extremity pain, which was rated on the VAS. Infrared thermal images were taken, objectively showing that the temperature change appeared along the dermatome of the diagnosed HLD-related level. The temperature difference was larger in the case of severe pain, showing that thermal imaging can visualize both the location of a lesion and severity of pain.  
Figure 4: Case comparison of the differences in the lower leg temperature distribution according to pain severity in patients with lumbosacral radiculopathy. **(A,B)**: MRI showing HLD L4/5, Rt. with upward migration. Sagittal and axial views. **(C)**: DITI image showing high temperature at L4/5 dermatome, right side. **(D,E)**: MRI showing HLD L4/5, Lt. Sagittal and axial views. **(F)**: DITI image showing high temperature at L4/5 dermatome, right side with high temperature difference between both sides.  
Case 1. A 60-year-old woman complained of decreased sensation in the right calf, and her pain at the time of the visit was a 5 on the VAS. The symptom duration was approximately 2 months. She had taken medications and physical therapy, but there was no change in the pain. MRI showed a ruptured lumbar disc at the L4/5 level on the right side with upward migration (Figure 4A,B). On infrared thermal images, color difference was checked in the right L4/5 dermatome region. The maximum temperature difference between the left and right lower limbs was \(-1.03\)\({}^{\circ}\)C. A microscopic disccetomy was performed for L4/5 intervertebral disc herniation, which was observed on examination, for the treatment of L5 nerve radiating pain.  
Case 2. A 51-year-old woman complained of muscle weakness in her left ankle, and her pain at the time of visit was a 9 on the VAS. The symptom duration was approximately 3 months. She had taken medications and physical therapy, but the pain increased in severity. MRI showed a ruptured lumbar disc at the L4/5 level on the left side (Figure 4D,E). On infrared thermal images, color difference was checked in the left L4/5 dermatome region. The maximum temperature difference between the left and right lower extremities was \(-2.93\)\({}^{\circ}\)F. A microscopic disccetomy was performed for lumbar 4/5 intervertebral disc herniation, observed on examination, for the treatment of lumbar five nerve radiating pain.  
After the disccetomy surgery, both patients' pain improved, and both were discharged without any complications. Unlike the rapid improvement of pain symptoms, numbness decreased over 3 and 6 months after discharge, and the thermography showed a decrease in the temperature difference between the lower extremities of both sides.
## 2 Literature Review
### Discussion of Existing Research  
In the problem encountered for TGD identification through portable-device-acquired CVD, existing research has mainly focused on signal analysis-based methods or ML-based methods to analyze the CVD. Although ML-based methods have demonstrated their potential for analyzing complex nonlinear relationships and the preliminary feasibility has been verified via simulation data, improving the identification accuracy is hindered in actual scenarios owing to certain obstacles. However, these ML models primarily perform architectural searches via the black-box trial method, which requires considerable time while offering a relatively low model performance. The TGD dataset represents a typical class-imbalanced dataset [40], resulting from a few instances of TGDs in a metro line. Therefore, a coping strategy is required to construct an ML model for TGD identification. In addition, existing TGD-identification models mainly leverage general evaluation metrics such as accuracy, precision, recall, and the area under the receiver's operating characteristic curve (AUC-ROC). However, for the direct application of these metrics, the actual management needs of metro companies are not taken into account.
# A Heuristics-Guided Simplified Discrete Harmony Search Algorithm for Solving 0-1 Knapsack Problem
## 5 Comparative Experiments
### Experiment on the First Set of Large-Scale 0-1 KP Instances  
Using the first set of large-scale 0-1 KP instances, we compare the SDHS algorithm with three HS algorithms, the novel global harmony search (NGHS) algorithm [39], improved adaptive binary harmony search (ABHS) algorithm [41], and simplified binary harmony \begin{table}
\begin{tabular}{l l l l} \hline \hline
**0-1 KP Instances** & **Data Set Classification** & **Item Number** & **Algorithms** \\ \hline The first instance set & 16 uncorrelated instances & 500–6400 & NGHS [39], ABHS [41], \\  & 5 uncorrelated instances & 800–2000 & CGSHS [9], BMBO [27], \\ The second instance set & 5 weakly correlated instances & 800–2000 & CMBO [29], OMBO [28], \\  & 5 strongly correlated instances & 800–2000 & NM [48], LBSA [47] \\ \hline \multirow{5}{*}{The third instance set} & 5 weakly correlated instances & 200–1000 & CGMA [13], MBPSO [30], \\  & 5 strongly correlated instances & 200–1000 & DGHS [44], HSOSHS [21] \\ \cline{1-1}  & 5 multiple strongly correlated instances & 300–1200 & \\ \cline{1-1}  & 5 profit ceiling instances & 300–1000 & \\ \cline{1-1}  & 4 uncorrelated instances & 3000–10,000 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The three large-scale 0-1 KP examples selected for this experiment. search (SBHS) algorithm [45]. In [45], the maximum numbers of iterations for these instances are set to 15,000, 15,000, 20,000, 20,000, 30,000, 30,000, 40,000, 40,000, 50,000, 50,000, 60,000, 80,000, 100,000, 120,000, 150,000, and 200,000. In the SDHS algorithm, the number of iterations is set to the value used by [45] or 40,000 if the value used by [45] is bigger than 40,000. It means that the SDHS algorithm only constructs 20 percent of the number of solutions constructed by its competitors on the largest instance. As in [45], the SDHS algorithm is run 30 times for each instance. The statistical results of 30 independent runs are reported in Table 8. The best results are highlighted in bold. The data of [39; 41; 45] are taken from [45]. In terms of the worst value and mean value, the SDHS algorithm obtains better results than the other three algorithms on all sixteen instances. In terms of the best value, the SDHS algorithm finds a new best solution on nine instances, i.e., LKP07, LKP08, LKP09, LKP11, LKP12, LKP13, LKP14, LKP15, and LKP16. These results clearly show that the SDHS algorithm has best performance among the four HS algorithms. In addition, the comparison results between SDHS and the average values of the competitive algorithms based on the Friedman test and Holm post hoc test are shown in Table 9. As shown in Table 9, according to the Friedman test, SDHS obtains the best
## 3 Model and Method  
### Numerical Model Design for Thermal Simulations  
The relatively young oceanic plate material has been generally too buoyant to undergo significant subduction to great depths; instead, it can only be pushed horizontally beneath the overriding plate. For instance, beneath the Halmahera volcanic arc, located on the eastern flank of the Molucca Sea Plate, subduction has progressed to a depth of up to 450 km at an angle of approximately 45\({}^{\circ}\), characterized by continuous retreat since 10 Ma [6]. Taking Mindanao Island as a kinematic reference point, the Halmahera subduction zone along the eastern margin of the Molucca Sea Plate is currently retreating westward at a rate of approximately 7 cm/year, while simultaneously subducting eastward at around 2 cm/year [4]. Nevertheless, the geological dynamics of subduction and convergence around the Molucca Sea Plate are irregular, characterized by varying rates of subduction and convergence on either side [27].  
Assuming that the subduction rate is represented by \(V\), which has horizontal (\(Vx\)) and vertical (\(Vz\)) components, along with a movement rate \(U\) for the overriding plate (see Figure 3a), several conditions can be examined. When \(U-Vx=0\), the position of the subduction zone remains stable, and both the arc and the trench retain fixedness. Conversely, when \(U-Vx>0\), the subduction zone retreats, urging the arc to migrate backward along with the trench (Figure 3a).  
Figure 3: (**a**) The subduction retreat kinematics model of Halmahera volcanic arc; (**b**) two-dimensional initial geometric model of subduction zone, where \(V\) is the subduction rate, \(U\) is the overriding plate movement rate, and \(\Phi\) is the initial subduction angle.  
With reference to earlier subduction models conducted in this region [28; 29; 30], we set up four distinct subduction models, depicted schematically in Figure 3b. Each model spanned 300 km in length and extended to a depth of 200 km. The weak zone width was set at 5 km, while the sticky-air layer thickness was 10 km across all models. Here, \(V\) represents the subduction rate, \(U\) denotes the movement rate of the overriding plate, and \(\Phi\) signifies the initial subduction angle. The varying geometric parameters for each specific model are presented in Table 1. The models were discretized with a minimum grid resolution of 2 \(\times\) 2 km.  
It should be noted that the variation of the subduction angle was an important factor leading to the generation of the volcanism. However, due to the relatively younger subduction zone beneath the Halmahera arc, there is no historical record of the changes in subduction angle or the associated volcanic activity during transient subduction. As a result, the effect of variations in the subduction angle is not taken into account in our simulation models.
## 3 Proposed Credibility Evaluation Methodology
### Scenario-Based Reliability Evaluation  
We employed the scenario-based reliability assessment method proposed by [PERSON] [31], the primary objective of which is to assess whether XILS tests can effectively replace real-vehicle tests in a given scenario. For each scenario, we conducted \(N\) trials of both the XILS and real-vehicle tests to calculate three types of scenario consistency indices: intra-XILS consistency (\(S_{k}(v)\)), intra-real-vehicle consistency (\(S_{k}(r)\)), and XILS-real-vehicle consistency (\(S_{k}(v,r)\)). Based on these indices, the scenario correlation index \(C_{k}\) and the applicability index \(A_{k}\) are calculated as shown in Equations (12) and (13):  
\[C_{k}=\frac{S_{k}(v,r)}{S_{k}(r)}\times 100\,(\%), \tag{12}\]  
\[A_{k}=\frac{S_{k}(v)}{S_{k}(r)}\times 100\,(\%). \tag{13}\]  
A scenario correlation index \(C_{k}\) close to 100% indicates a very high similarity between the XILS and real-vehicle test results in that scenario, while an applicability index \(A_{k}\) exceeding 100% indicates that the repeatability and reproducibility of the XILS tests are better than those of the real-vehicle tests. The XILS platform is deemed to attain scenario-based reliability \(R_{k}\) when all the criteria in Equation (14) are met:  
\[R_{k}=(C_{k}>\alpha_{S})\cap(A_{k}>\beta_{S}),\,k\in\{1,2,\ldots,K\}, \tag{14}\]  
where \(\alpha_{S}\) and \(\beta_{S}\) denote the correlation evaluation criterion and the applicability evaluation criterion, which are defined in Equations (15) and (16), respectively:  
\[\alpha_{S}=C_{S}-\sigma_{S}, \tag{15}\]  
\[\beta_{S}=Q-\sigma_{S},\,(Q=1), \tag{16}\]  
where \(C_{S}\) denotes the minimum value among the maximum scenario consistency indices for the real-vehicle tests, and \(\sigma_{S}\) is the average deviation between the maximum and minimum consistency indices across these tests. These criteria imply that the normalized consistency of the XILS trials must exceed that of the real-vehicle trials to guarantee superior repeatability and reproducibility.  
The scenario evaluation-based thresholds \(\alpha_{S}\) and \(\beta_{S}\) follow the same statistical framework as parameter-based criteria but are applied at the scenario evaluation. Following the methodology detailed in [PERSON] ([31], Equations (34)-(39)), these thresholds are derived from the distribution of consistency indices across all test scenarios, ensuring that evaluation criteria reflect realistic performance expectations while maintaining statistical validity for scenario-specific assessments.  
This method allows for pre-assessing whether XILS can effectively replace real-vehicle testing in a given scenario, thereby enhancing the efficiency of simulation-based validation. The complete derivation of the scenario-based evaluation equation is excluded from this paper as it has been provided by [PERSON] [31].