esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### The Effects of CSP and EH in Reserve Services
#### 5.2.1 Comparison Among Different Reserve Planning Decisions
In the reserve scheduling process, CSP reserve is considered the best replacement of the costly thermal unit reserve which effectively minifies the requirement and the reserve scheduling cost of thermal unit reserve. In the reserve deploying process, inevitable deviations occur between the stochastic renewables power output and the scheduled value, and the deviations require more rapid response reserve deploying services. The CSP reserve deployment is constrained by the \(PB\) ramp rate and range (higher than the thermal units) and thus the reserve deploying costs decrease gently with CSP reserve services. The installation of an EH is the choice for the drop of reserve deploying costs, because the EH could respond quickly to mitigate the power output deviations instead of the unforeseen deployment of thermal unit reserve. Therefore, with the scheduling cost reduction by the CSP reserve and the deploying cost reduction by the EH equipment, the reserve costs are relatively low in Strategy 3 and Strategy 6. In all, the chance-constrained reserve planning method with the consideration of reserve services from the TES/EH considered that the CSP station could achieve remarkable economic results.
# Representing and Validating Cultural Heritage Knowledge Graphs in CIDOC-CRM Ontology
## 3 Aligning COURAGE Entities to the CIDOC CRM Ontology
### Mapping of Properties to CIDOC-CRM
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Courage** & **CIDOC-CRM Entity** & **CIDOC-CRM Property** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{l} Official name \\ contentLanguage \\ dateOfFounding \\ placeOfFounding \\ placeOfFounding \\ collectionGeoScope \\ hasTopic \\ hasMasterpiece \\ CreateOntent \\ Features \\ Operators \\ Collectors \\ Owners \\ Stakeholders \\ Supporters \\ \end{tabular} } & E35 Title & P102 has title \\  & E56 Language & P2 has type \\  & E63 Beginning of Existence & P4 has time-span \\  & E63 Beginning of Existence & P7 took place at \\  & E55.5 Geographical Scope & P2 has type \\  & E55.6 Topic & P2 has type \\  & E79 Part Addition & P110 augmented \\  & E87a Content Creation & P147 curated \\  & Activity & P147 curated \\  & E63 Beginning of Existence & P92 brought into existence \\  & E87b Operator Activity & P147 curated \\  & E87c Collection Activity & P147 curated \\  & E8 Acquisition & P24 transferred title of \\  & E87d Stakeholder Activity & P147 curated \\  & E87e Support Activity & P147 curated \\ \hline \hline \end{tabular}
\end{table} 
Table 3: COURAGE collection entity representation in CIDOC-CRM. CreatorOf is a property in COURAGE that connects people and organizations with their own featured items. It is represented through the E12 production class (see Figure 5), which includes all activities that create one or more new items. The owner role is represented by the E8 Acquisition class in contrast to other roles. Ownership of an item in COURAGE can be transferred to multiple people and organizations during that time (see Figure 6). The founder role, which refers to the founder of a collection in COURAGE, was represented in CIDOC-CRM using the E66 formation. Then we f  
Figure 4: An example of a person first name representation in CIDOC-CRM. Figure 5: CreatorOf representation in CIDOC-CRM. Figure 6: An example of owner role representation in CIDOC-CRM. the E66 formation is to form a group of people and its properties are inappropriate to our needs. Therefore, the E63 beginning of existence class was used instead (see Figure 7) as it includes all necessary connections involved in a foundation event. Roles in COURAGE (founder, owner, operator, collector, stakeholder, and supporter) link persons/organizations with collections/featured items. For example, a national library can have an operator role connected to several collections, and/or can be the owner of them. Figure 8 is an example of stakeholder role representation in CIDOC-CRM. Several properties in COURAGE, which are mentioned below in the tables, were mapped to the E55 Type class and linked to the entity via the P2_has_type property in CIDOC-CRM. For instance, the created type E55.4 organizational type was used to represent the types of organizations via the P2_has_type property. For each type, an instance of E55.4 organizational type was created once and linked to several organizations (in Figure 9).
## 2 Theoretical Models
### Surface Charging Theory Model
#### 2.1.2 Potential Solver  
The Debye length of the plume region being small relative to the characteristic length ensures that the plume region satisfies the quasi-neutral approximation condition. In this context, the neutralizer electrons are treated as isothermal fluid species. Their densities on the simulation mesh are determined by the local plasma potentials, following the Boltzmann relation:  
\[n_{e}=n_{\rm ref}e^{\frac{e(\phi_{p}-\phi_{\rm ref})}{\varepsilon_{e}}} \tag{4}\]  
where \(\phi_{p}\) is the plasma potential, \(\phi_{\rm ref}\) represents the plasma potential at the grid point, and \(e\) is the elementary charge. \(n_{\rm ref}\) represents the electron density at the grid point, where it is simultaneously a predetermined fixed value.  
According to the ground test and on-orbit data, the electron temperature of the thruster plasma plume is non-isothermal. Therefore, when calculating the local plasma potential for the non-isothermal fluid, it is necessary to refer to the non-isothermal model to obtain the corresponding values. Equation (5) represents the non-isothermal model, and Equation (6) represents the local plasma potential of the non-isothermal fluid [11]:  
\[T_{e}n_{e}^{1-\gamma}=C \tag{5}\]  
\[\phi_{p}=\frac{\gamma T_{\rm ref}}{e(\gamma-1)}\left[\left(\frac{n_{i}}{n_{ \rm ref}}\right)^{\gamma-1}-1\right]+\phi_{\rm ref} \tag{6}\]  
where \(C\) is a constant, and \(\gamma\) is a temperature-dependent coefficient; when \(\gamma=1\), it means an isothermal case, and when \(\gamma=5/3\), it means adiabatic plasma.
## 3 Proposed Methods
### Time Domain Phase Correction
The speech segment corresponding to the maximum value of \(C(i)\) is the candidate segment that most closely fits to the previous segment in the overlapped part. This similarity comparison can reduce the phase change caused by the overlap-add operation. Once all the input signal is segmented, the extended signal \(z(t)\) can be obtained as follows by the overlap-add operation:  
\[z(t)=\sum\limits_{m=1}^{M}S_{m}(t-\Delta z)\cdot w(t-\Delta z), \tag{15}\]  
where \(M\) is the number of the segments and \(w(\cdot)\) is the Hanning window function. \(z(t)\) can be expressed as follows in terms of amplitude spectrum and phase spectrum:  
\[Z_{k}(\omega)=A_{Z,k}(\omega)e^{i\angle\varphi_{Z,k}(\omega)},A_{Z,k}(\omega )=|Z_{k}(\omega)|, \tag{16}\]  
where \(A_{Z,k}\) and \(\varphi_{Z,k}(\omega)\) denote amplitude and phase at frequency \(\omega\) of the \(k\)th frame, respectively. This phase includes phase substitution of the lost frame. Considering all the  
Figure 5: Schematic diagram of searching speech segments in the WSOLA. frequencies, all correctly received frames, and the lost frame, we use matrix \(\mathbf{\varphi}_{Z}\) to represent the phase set, as follows:  
\[\mathbf{\varphi}_{Z}= \begin{bmatrix}\mathbf{\varphi}_{X,Y-T}&\cdots&\mathbf{\varphi}_{X,Y-1} &\mathbf{\varphi}_{Y,W}\end{bmatrix}  \tag{17}\]  
where \(\mathbf{\varphi}_{X,Y-t}\), \(t=1,\ldots,T\), represents the phases of \(T\) speech frames without packet loss, \(\mathbf{\varphi}_{Y,W}\) represents the phase substitution of the lost frame after using WSOLA. As shown in Figure 6, in the proposed time domain packet loss concealment method, once the log-power spectrum and phase spectrum of the lost speech frame are determined, by performing an inverse STFT, the concealed signal \(y(t)\) of the lost frame can be obtained in the time domain. It is worth noting that, in order to distinguish the proposed method from the WSOLA method, it is explained here that the proposed method is based on DNN to estimate the amplitude information of the packet loss segment. For the WSOLA method, the amplitude distortion will occur in the process of extending the signal in the time domain, which will affect the sense of hearing. The better estimation of the amplitude spectrum by DNN can solve the amplitude problem of WSOLA as well, but this is not the main purpose of this paper. For the phase information, the proposed method introduces the \"maximum similarity principle\" to solve the problem of inaccurate phase estimation with DNN in the packet loss segment.
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 4 Case Study
### Data Preprocessing  
Data cleaning and handling were very important when building the model, so flight records were deleted if they contained unfillable missing values for necessary attributes such as scheduled departure time. For fillable data, such as weather variables, if the value was missing at hour \(t\), the value at its previous hour \(t-1\) was used in place of the missing value. In addition, the top and bottom 1% of delays were also eliminated as outliers. The cleaned data was randomly divided into two subsets: 80% of the data were used for training and the rest were used for testing. In this study, we predicted flight delays 2 h before the scheduled time, i.e., \(\tau=2\). A summary of the handling mode for explanatory variables is listed in Table 4. Some discrete variables, such as airline, were treated as 0-1 dummy variables, while other discrete variables, such as hour of the day and day of the week, were handled by sine-cosine functions in order to preserve periodicity. In addition, variables such as delay of previous flights were calculated. To improve the accuracy of the model and enhance convergence speed, the continuous data were normalized:  
\[x^{*}=\frac{x-\mu}{\sigma} \tag{9}\]  
where \(x^{*}\) is a normalized variable; \(x,\mu\), and \(\sigma\) are original variable, average value, and standard deviation, respectively. Figure 8: Cruise pressure of flights from PEK to HGH. Figure 7: Departure pressure of flights from PEK to HGH: (**a**) number of scheduled flights, (**b**) number of actual flights. In our study, the airline variable was handled by one-hot encoding. However, there were 155 airlines in total, and direct use of one-hot encoding would increase the dimensions of the dataset and affect the training speed. Therefore, we applied the Pareto principle to the airline data [30]. As shown in Figure 9, the top 10 airlines accounted for 82.13% of all flights in total. As shown in Table 5, using one-hot encoding an airline would be represented by one row and 155 columns of data, while using Pareto encoding, only one row and 11 columns of data were needed. In order to improve the efficiency of the calculation without affecting the results, the flight data for the routes with the five highest similarity coefficients were utilized to \begin{table}
\begin{tabular}{c c} \hline \hline
**Handling Mode** & **Explanatory Variables** \\ \hline One-hot encoding method & Airline \\
0-1 modeled & Base airport, holiday \\ Calculated & Delay of previous flight, arrival/departure pressure, cruise \\ pressure, aircraft capacity \\ \multirow{2}{*}{Sine–cosine functions} & Wind direction, hour of the day, day of the week, month of the \\  & year, season \\ No handling & Air temperature, atmospheric pressure, relative humidity, wind \\  & speed, horizontal visibility \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of handling modes of explanatory variables.
## 3 Materials and Methods  
This section presents the proposed security framework for systematically evaluating satellite network security. It outlines the satellite network architecture, analyzes vulnerabilities, and applies TV-HARM for threat modeling. Additionally, security metrics are introduced to quantify risks and assess the overall security posture.
## 3 Parallel ARIA Implementation
### Parallel ARIA Implementation on ARMv8
As the number of blocks to be implemented in parallel increases, alignment becomes more complex than 4-PT parallel implementation. Conversely, the round function implementation is simplified because each byte is loaded into a different register. Due to the various load instructions in ARMv8 architecture, aligning is possible at the same time as load without the aligning process. The 16-PT loads the plaintext block with the LD4.B instruction. In 4-PT, an arrangement specifier is used as S. In 16-PT, it is used as B. When one block is loaded, 4 LD4.B instructions are used, and 16 \(\times\) 4 (64) times are used to load all plaintext blocks. The implementation code is the same as Algorithm 1. If the index of the implemented macro is operated 16 times from 0-15, the aligned plaintext block is loaded into the v0-v15 vector register. If the macro is operated once with PT-load 0, the first plaintext block is divided into bytes and loaded at the 0-th index of v0-v15. In the implementation of the substitution layer, there is no difficulty in implementation because values using the same Sbox are combined in one register. In the implementation of the diffusion layer, 8-bit based implementation method is used. Since each register is divided in units of bytes, parallel operation is possible if registers required for each operation are used. ```
1:macro PT-load index
2:LD4.B v0-v3[\(\backslash\)index], [x0], #4
3:LD4.B v4-v7[\(\backslash\)index], [x0], #4
4:LD4.B v8-v11[\(\backslash\)index], [x0], #4
5:LD4.B v12-v15[\(\backslash\)index], [x0], #4.endm
```  
**Algorithm 1** Plaintext load macro in 16-PT parallel implementation (x0: plaintext address).
# Synthesis of Shape-Memory Polyurethanes: Combined Experimental and Simulation Studies
## 2 Materials and Methods
### Syntheses  
#### 2.2.1 Bis(methylcarbonate)decamethylene  
Bis(methylcarbonate)decamethylene Based on 1,10-Decanediol with an 8 Molar Excess of DMC (E\(\_\)BMC\(\_\)8)  
The alkylene bis(methylcarbonate)s (BMCs) were synthesized according to previously published procedures [20]. Briefly, in a 2000 cm\({}^{3}\) reactor equipped with a magnetic stirrer, Vigreux column, distillation condenser, and thermometer, 250.0 g (1.43 mol) of 1,10-decanediol and 1000.0 g (11.1 mol) of DMC were placed in the presence of potassium carbonate (3 mol.% referred to 1,10-decanediol), used as a catalyst. The mixture was heated under reflux for 1.5 h at 80 \({}^{\circ}\)C. Thereafter, while using the distillation column, the azeotrope of DMC/methanol was removed. The reactions were carried out at the boiling point of the mixture (approximately 90 \({}^{\circ}\)C). The progress of the reaction was followed by measurement of refractive index of the distillate until no more methanol was detected. In addition, the region from 3.85 ppm to 3.55 ppm in the \({}^{1}\)H NMR spectra was analyzed to observe the progress of conversion for E\(\_\)BMC\(\_\)10 with 20 min sampling intervals (Figure S1, Supplementary Materials). The excess of DMC and residuals of methanol were removed under reduced pressure. Yield was 99%.  
E\(\_\)BMC\(\_\)8: \({}^{1}\)H NMR (CDCl\({}_{3}\), 400 MHz): \(\delta\) (ppm) = 4.11 (t, 4H, C(O)OCH\({}_{2}\)), 3.76 (s, 6H, CH\({}_{3}\)O), 1.63 (m, 4H, OCH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)), 1.33 (m, 4H, OCH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)), 1.26 (m, 8H, OCH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)); Figure S1, Supplementary Materials.  
BMC Based on 1,10-Decanediol with a 10 Molar Excess of DMC (E_BMC_10)  
E_BMC_10 was obtained analogically to E_BMC_8, using 200.0 g (1.15 mol) of 1,10-decanediol and 1000.0 g (11.1 mol) of DMC. Yield 98%.  
E_BMC_10: \({}^{1}\)H NMR (CDCL\({}_{3}\), 400 MHz): \(\delta\) (ppm) = 4.12 (t, 4H, C(O)OCH\({}_{2}\)), 3.77 (s, 6H, CH\({}_{3}\)O), 1.65 (m, 4H, OCH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)), 1.34 (m, 4H, OCH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)), 1.28 (m, 8H, OCH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)); Figure S2, Supplementary Materials.  
BMC Based on 1,10-Decanediol with an 18 Molar Excess of DMC (E_BMC_18)  
E_BMC_18 was obtained analogically to E_BMC_8 and E_BMC_10, using 105.0 g (0.6 mol) of 1,10-decanediol and 1000.0 g (11.1 mol) of DMC. Yield 98%.  
E_BMC_18: \({}^{1}\)H NMR (CDCl\({}_{3}\), 400 MHz): \(\delta\) (ppm) = 4.12 (t, 4H, C(O)OCH\({}_{2}\)), 3.77 (s, 6H, CH\({}_{3}\)O), 1.65 (m, 4H, OCH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)), 1.34 (m, 4H, OCH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)), 1.27 (m, 8H, OCH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)); Figure S3, Supplementary Materials.  
FTIR (ATR): 2920, 2850, 1750, 1440, 1280, 1260, 950, 930, 790, 710 cm\({}^{-1}\); Figures S4-S6 in the Supplementary Materials.
## 4 Primary Market Design
### Naive DA Mechanism  
As the naive DA mechanism, we adopt standard allocation and pricing policies, which are commonly used in many studies on DA, in order to delineate idiosyncratic problems in traders' behavior in perishable goods markets.  
A common goal of the allocation policy in DA markets is to compute trades that maximize social surplus, which is the sum of the difference between bid and ask prices for all matched bids, with the assumption that traders bid truthfully. Thus, the standard allocation policy arranges the asks in ascending order of the seller's price and the bids in descending order of the buyer's price, and matches the asks and bids in sequence. We refer to this allocation rule as a _greedy allocation policy_.  
The greedy allocation policy is monotonic and is efficient in durable goods markets, in which agents do not lose utility when they fail to trade. However, sellers of perishable goods lose the value of perished goods when they fail to sell them during the trading period. Consequently, in addition to increasing social surpluses from trades, increasing the number of successful trades is important in the perishable goods markets for maximizing social utility. In our previous research [19], we advocated a criticality-based allocation policy that gives higher priorities to the expiring bids. Since the criticality-based allocation policy is not monotonic, traders have an incentive to misreport their valuation and temporal information.  
The pricing policy is important to secure truthfulness and increase market efficiency. Nevertheless, since obtaining truthfulness in online DA markets for perishable goods is impossible, we impose both budget balance and individual rationality, and we promote reasonable efficiency and adequate truthfulness. We adopt the _k-DA pricing policy_[23], in which a clearing price for both sellers and buyers is determined as \((1-k)\hat{v}_{j}^{t}+k\hat{v}_{i}^{t}\), thereby making the naive DA market budget balanced (i.e., \(s_{i,j}^{t}=b_{i,j}^{t}\)). We set the value of \(k\) as 0.5 for experiments in this paper.
## 1 Introduction
To this end, the RADP-based SS is developed to obtain robust sliding mode for mismatched uncertainties, and an adaptive sliding-mode FTC is designed to guarantee the reachability of above sliding mode in the presence of faults. The detailed contributions are summarized as follows. 1. The RADP-based SS is designed, which is robust to the mismatched uncertainties. An adaptive fault compensation is used to deal with the actuator faults. Compared with the traditional sliding-mode FTC [21], the novel scheme in the present paper achieves a certain sense of optimized performance by introducing the RADP-based SS. To the best knowledge of the authors, the ADP-based SS does not appear in previous reports, and the more important factor of a novel SMC tackling mismatched uncertainties based on this new SS is not reported either. 2. The idea of the ADP-based SS design method may be open a door that we can obtain many kinds of sliding modes with various properties inherited from ADP methods, such as finite-horizon optimal SS [22] or safety SS [23]. These provide more methods or possibilities for solving FTC problems. The rest of this article is organized as follows. Section 2 gives the problem formulation. In Section 3, an adaptive sliding-mode FTC with RADP-based SS for the uncertain cascade systems is developed. The simulation examples are provided in Section 4 followed by the conclusions in Section 5.
# An Improved Differential Quadrature Time Element Method
## 2 Basic Rules and Formulations
### DQ Rod and Beam Elements
#### 2.4.2 C\({}^{1}\) DQ Euler Beam Element  
Assume the deflection of a uniform Euler beam element is  
\[w(x)=\sum\limits_{i=1}^{m}l_{i}(x)w_{i} \tag{25}\]  
where \(w_{i}=w(x_{i})\) is the deflections at the Guass-Lobatto quadrature nodes, similar to \(u_{i}\). The total potential energy and kinetic energy coefficient functions are  
\[\begin{array}{rl}\prod&=\frac{1}{2}\int_{0}^{L}EI\left(\frac{\partial^{2}w}{ \partial x}\right)^{2}\mathrm{d}x-\int_{0}^{L}fw\mathrm{d}x\\ &=\frac{EI}{2}\mathbf{w}^{\mathrm{T}}\mathbf{B}_{S}^{*}\mathbf{C}_{S}^{*}\mathbf{B}_{S}\mathbf{ \overline{w}}-\mathbf{\overline{w}}^{\mathrm{T}}\mathbf{C}_{S}^{*}\mathbf{f}\end{array} \tag{26}\]  
\[T_{0}=\frac{1}{2}\int_{0}^{L}\rho S\overline{w}^{2}\mathrm{d}x=\frac{1}{2} \overline{w}^{\mathrm{T}}(\rho S\mathbf{C}^{*})\overline{w} \tag{27}\]  
where \(\mathbf{B}_{S}=(\mathbf{A}_{s})^{2}\), \(I\) is the moment of inertia, and  
\[\overline{w}^{\mathrm{T}}=\left[w_{1}\;w_{2}\;w_{3}\;\cdots\;w_{m-1}\;w_{m}\right] \tag{28}\]In order to satisfy C\({}^{1}\) inter-element continuity requirement of Euler beam element, the displacement vector is modified to  
\[\mathbf{w}^{\mathrm{T}}=[w_{1}\;\mathrm{d}w_{1}/\mathrm{d}x\;w_{3}\;\cdots\;w_{m-2} \;w_{m}\;\mathrm{d}w_{m}/\mathrm{d}x] \tag{29}\]  
Based on DQ rule, the relation between \(\overline{\mathbf{w}}\) and \(\mathbf{w}\) is readily given by  
\[\mathbf{w}=\overline{\mathbf{Tw}} \tag{30}\]  
where  
\[\mathbf{T}=\begin{bmatrix}1&0&0&\cdots&0&0&0\\ A_{1,1}^{(1)}&A_{1,2}^{(1)}&A_{1,3}^{(1)}&\cdots&A_{1,m-2}^{(1)}&A_{1,m-1}^{( 1)}&A_{1,m}^{(1)}\\ 0&0&1&\cdots&0&0&0\\ \vdots&\vdots&\vdots&&\vdots&\vdots&\vdots\\ 0&0&0&\cdots&1&0&0\\ 0&0&0&\cdots&0&0&1\\ A_{m,1}^{(1)}&A_{m,2}^{(1)}&A_{m,3}^{(1)}&\cdots&A_{m,m-2}^{(1)}&A_{m,m-1}^{( 1)}&A_{m,m}^{(1)}\end{bmatrix} \tag{31}\]  
Substitute Equation (30) into Equations (26) and (27), the mass matrix \(\mathbf{M}\), stiffness matrix \(\mathbf{K}\) and generalized load vector \(\mathbf{F}\) are obtained as  
\[\mathbf{K}=EIT^{-\mathrm{T}}(\mathbf{B}_{s}\mathbf{C}_{s}^{*}\mathbf{B}_{s})\mathbf{T}^{-1},\mathbf{M}= \mathbf{T}^{-\mathrm{T}}(\rho SC_{s}^{*})\mathbf{T}^{-1},\mathbf{F}=\mathbf{T}^{-\mathrm{T}}\bm {C}_{s}^{*}\mathbf{f} \tag{32}\]  
In fact, the mass matrix, stiffness matrix and generalized load vector of other structures with arbitrary geometry, such as plane, Kirchhoff plate and shells, etc., can be obtained by the same way [37]. Note that DQ rule cannot be used directly for irregular geometric domain; it should be transformed from the Cartesian \(x\)-\(y\) coordinate system to the natural \(\xi\)-\(\eta\) coordinate system, so the DQ rule should be reformulated, and [PERSON] and [PERSON] [46] have done this work first, and [PERSON] and [PERSON] [36] gave a very detailed description about the transformation.