esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Kinetic Study of CFTX Degradation Reaction
The UV-Vis spectra of CFTX in 0.1 mol L\({}^{-1}\) Na\({}_{2}\)SO\({}_{4}\) solution recorded during electrolysis and the kinetic model that best fits the experimental data are shown in Figure 5.  
As can be seen from Figure 5a, the CFTX UV-Vis spectra present approximately similar characteristics to those obtained in the presence F\({}^{-}\) ions. The absorbance maximum decreases over time without altering the wavelength, and another decomposition compound isn't detected. As shown in Figure 5b, the absorbance shows a linear decrease with a mean square deviation very close to unity (R\({}^{2}\) = 0.99), according to the inserted equation, thus respecting a zero-order reaction kinetics. By derivation, the line slope is obtained, and consequently a  
Figure 4: UV-Vis spectra of NaI solution of various concentrations in the absence and presence of CFTX (**a**) and detailed UV-Vis spectra for 0.01 mol L\({}^{-1}\) and 0.001 mol L\({}^{-1}\) respectively, NaI solution (**b**). Figure 5: UV-Vis spectra of CFTX in 0.1 mol L\({}^{-1}\) Na\({}_{2}\)SO\({}_{4}\) solution obtained during electrolysis at constant current density of 50 mA cm\({}^{-2}\) (**a**) and kinetic model of CFTX degradation reaction in the presence of SO\({}_{4}\)\({}^{2-}\) ions (**b**). rate constant value of 0.0044 uA min\({}^{-1}\). The final degradation takes place after 204 min, when y = 0 and A = 0, respectively. Similarly, the best fitting of the experimental data, for the CFTX degradation reaction, was obtained according to the kinetics of the zero-order reactions, in the presence of F\({}^{-}\) and Cl\({}^{-}\) ions (Figure 6a,b). Smaller values for rate constant compared to that obtained in 0.1 mol L\({}^{-1}\) Na\({}_{2}\)SO\({}_{4}\) solution were computed as follows, 0.0096 uA min\({}^{-1}\), for CFTX/F\({}^{-}\) system and 0.1677 uA min\({}^{-1}\), for CFTX/Cl\({}^{-}\) system leading to a final degradation time of 84 min and 5.5 min, respectively. In the presence of Br\({}^{-}\) ions the CFTX degradation obeys the first-order reaction kinetics (Figure 6c,d). The rate constant high value of 1.361 uA min\({}^{-1}\) was calculated from both equations inserted in Figure 6c,d, but, according to these kinetics, no final degradation time can be determined, due to the exponential drop in absorbance showing that CFTX reminiscence does not degrade. As shown in Figure 6a, after 3 min a minimum absorbance threshold is reached followed by a plateau approximately identified with zero value, and CFTX degradation percentage is close to 100%. The data are centralized in \[-\frac{\mathrm{d}[\mathrm{CFTX}]}{\mathrm{dt}}=\mathrm{k}[\mathrm{CFTX}] \tag{15}\]0
## 2 Technical Architecture and Solution
### Architecture Overview  
The proposed technical solution is based on the Haystack framework [25] but can be applied straightforwardly with similar frameworks. Each architecture component is considered modular and can be directly modified with alternative options. Additionally, the data sources are selected as examples of e-government applications that could enhance the productivity of public sector employees in their daily tasks or be directly used by citizens based on their interests in various public sectors.
# An Automatic Embedded Device Registration Procedure Based on the OGC SensorThings API
## 3 Methodology  
This research proposes the SW-IoT plug-and-play (IoT-PNP) to automatically establish communication between the device layer and the service layer for both sensing and tasking capabilities of embedded devices. The IoT-PNP has three main components: (1) A data model and encoding describing devices' metadata, sensing and tasking capabilities, (2) a communication protocol and default connection settings for the gateway layer and the device layer, and (3) an automatic registration procedure for registering devices and their sensing and tasking capabilities on a SensorThings API service. The details of these components are explained in the following subsections.
# Error Source Analysis and Correction of GF-3 Polarimetric Data
###### Abstract  
The GaoFen-3 (GF-3) satellite is the first polarimetric synthetic aperture radar (PolSAR) satellite in China. With a designed in-orbit life of 8 years, it will provide large amounts of PolSAR data for ocean monitoring, disaster reduction, and many other applications. The polarimetric data quality is essential for all these applications, so the analysis and calibration of the polarimetric error sources are very important for GF-3. In this study, we established a full-link error model for GF-3 PolSAR system. Based on this model, we comprehensively analyzed the quantitative effects of the main error sources including the composition, figured out characteristics of the phase imbalance introduced by the antenna, and pointed out the error sources which have to be corrected. Furthermore, the polarimetric correction method for GF-3 PolSAR system is proposed. Finally, assisted by several external calibration experiments, polarimetric errors of GF-3 data are efficiently corrected during in-orbit-test phase.  
GF-3 satellite; PolSAR; Error source analysis; polarimetric correction 23 October 2018; 10.3390/rs10111685; doi:10.3390/rs10111685; doi:10.3390/rs10111685
## 2 Materials and Methods
### Sample Preparation
#### 2.2.3 USVA-DLLME and Derivatization Procedure  
The extraction procedure was performed as follows: the mixture of dispersive (1 mL of acetone) and extraction (250 \(\upmu\)L of CH\({}_{2}\)Cl\({}_{2}\)) solvent was injected above the sample level of the solution previously kept at room temperature at pH 3 with a few \(\upmu\)L of HCl [46]. The solution was subjected to vortex for 1 min and ultrasounds for 2 min: This occurrence was repeated three times, followed by centrifugation for 10 min at 4000 rpm at room temperature. The organic phase was withdrawn with a micro-syringe and placed in a vial with the addition of a few grains of anhydrous sodium sulphate. A total of 50 \(\upmu\)L of BSTFA + TMCS (99 + 1, v + v) were added [47] and the vial was closed and heated up to 50 \({}^{\circ}\)C for 30 min. Afterwards, the vial was cooled at room temperature and the organic phase was concentrated to a final volume of 20-50 \(\upmu\)L under a slight nitrogen flow and 1 \(\upmu\)L were analyzed in GC-IT-MS.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Compound*** & **\# CAS** & **Formula** & **MW** & **Target Ion*** & **Qualifier Ion*** & **II Ion*** \\ \hline ASA & 50-78-2 & C\({}_{4}\)H\({}_{6}\)O\({}_{4}\) & 180.16 & 252 [C\({}_{12}\)H\({}_{6}\)O\({}_{5}\)Si]\({}^{+}\) & 209 [C\({}_{16}\)H\({}_{3}\)O\({}_{5}\)Si]\({}^{+}\) & 149 [C\({}_{5}\)H\({}_{5}\)O\({}_{3}\)]\({}^{+}\) \\ IBP & 15687-27-1 & C\({}_{13}\)H\({}_{8}\)O\({}_{2}\) & 206.29 & 278 [C\({}_{15}\)H\({}_{2}\)O\({}_{5}\)Si]\({}^{+}\) & 160 [C\({}_{2}\)H\({}_{6}\)]\({}^{+}\) & 263 [C\({}_{5}\)H\({}_{8}\)O\({}_{5}\)Si]\({}^{+}\) \\ NAP & 22204-53-1 & C\({}_{14}\)H\({}_{4}\)O\({}_{3}\) & 230.26 & 302 [C\({}_{17}\)H\({}_{2}\)O\({}_{5}\)Si]\({}^{+}\) & 185 [C\({}_{3}\)H\({}_{3}\)O]\({}^{+}\) & 243 [C\({}_{4}\)H\({}_{2}\)O\({}_{3}\)]\({}^{+}\) \\ KPF & 22071-15-4 & C\({}_{16}\)H\({}_{4}\)O\({}_{3}\) & 254.28 & 325 [C\({}_{19}\)H\({}_{2}\)O\({}_{5}\)Si]\({}^{+}\) & 282 [C\({}_{15}\)H\({}_{4}\)O\({}_{5}\)Si]\({}^{+}\) & 295 [C\({}_{7}\)H\({}_{5}\)O\({}_{5}\)Si]\({}^{+}\) \\ \hline \end{tabular}  
* Abbreviations: acetylsalicylic acid (ASA), ibuprofen (IBP), naproxen (NAP), ketoprofen (KPP);
* target and qualifier ions of the trimethylsilyl derivates.  
\end{table}
Table 1: The non-steroidal anti-inflammatory drugs (NSAIDs) investigated in this paper, with their corresponding abbreviations, Chemical Abstracts Service (CAS) number, chemical structure, molecular weight (MW), target, and qualifier ions (selected ion monitoring (SIM), abundance 100%).
# Seismic Assessment and Strengthening of a Load-Bearing Masonry Structure Considering SSI Effects
## 2 Case Study
### Structural Description  
#### 2.4.1 Ground Floor  
The exterior and interior walls of the ground floor are made of load-bearing stone masonry, except for one wall constructed with clay bricks (optoplinth). The external load-bearing masonry is 65 cm thick, the internal is 50 cm, and the clay-brick wall 30 cm. The external masonry is built as three-leaf rubble construction with lime mortar applied on all wythes. Large dressed stones are positioned at the corners of the walls, while semi-dressed stones and clay bricks are used as infill between them, contributing to the stability and integrity of the masonry. The core is filled with rubble stone and mortar, forming a solid mass between the outer wythes [5]. The internal stone masonry is constructed as two-leaf rubble masonry (Figure 5a).  
The stone used in the masonry, of volcanic origin and gray in color, was sourced from quarries near the settlements of Thanos and Romanou in Lemnos. According to the geotechnical study of the Castle of Myrina [26] and after the identification of the building stones, it was found that they belong to the categories of lavas found in the local quarries (dacites or ignimbrites) with an average strength of fbc = 50 MPa.  
The mortar used in the building is cohesive and contains lime and large dark-colored coarse aggregates (brown and gray), collected from streambeds where volcanic rock erosion products accumulate. A small amount of ceramic material grains was also identified. The plaster applied to the ground-floor masonry is a lime-rich mortar of medium consistency, containing medium-grained and coarse-grained aggregates. The tensile strength of the interior masonry plaster was estimated at 78 kPa [27].  
No timber lacing was found in the ground floor masonry; however, iron ties and anchor bars--visible on the facades (Figure 5b)--were used at floor level to strengthen the walls. These ties--metal bars running along the exterior of the masonry--are not connected to the wooden elements of the floor or the timber framing present on the second floor. Timber ties were found embedded within the internal stone masonry, oriented transversely across the wall at door openings (Figure 5a).
# ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET
## 4 Conclusions  
In this paper, we have proposed a new channel-aware integrated time and frequency-based downlinks LTE scheduling (ITFDS) algorithm. The proposed scheduling algorithm distributes the  
Figure 10: Overhead versus different UEs.  
Figure 9: Spectral Efficiency versus different UEs.  
resources to all the users together using time-based and frequency-based scheduling. It works well for real-time flows and also maintains fairness among users. ITFDS outperforms other scheduling strategies in terms of system throughput, fairness and packet loss ratio. The simulation environment included both GBR and non-GBR bearers. The proposed scheduling algorithm achieved the QoS requirement of GBR bearers such as multimedia applications with increased system throughput and provided fairness to non-GBR bearers. The packet loss rate of the proposed scheduling algorithm has been reduced significantly compared to other scheduling strategies.  
Although it outperforms other algorithms, the ITFDS algorithm does not have good enough performance compared against other algorithms in terms of aggregated throughput. The proposed method is also deployed for 4G-LTE, but not for 5G technologies. The proposed LTE has some downside for implementing real-time applications in that LTE data rates minimize whenever the distance from the tower to the user equipment is very long. Sometimes, connectivity problems occur when voice is being converted into the packets, so that the transmission time may increase. Thus, our aims in future works are to investigate the extensions of the scheduling algorithm to other contexts and with 5G technology to ensure it is good enough to apply in Industry 4.0.  
Conceptualization, L.M.T. and L.H.S.; methodology, L.M.T., L.R.P. and K.R.S.; software, Y.H.R. and R.K.; validation, L.M.T.; formal analysis, L.H.S. and H.V.L.; investigation, R.K.; data curation, H.V.L. and L.R.P.; writing--original draft preparation, L.M.T. and Y.H.R.; writing--review and editing, L.H.S., H.V.L., and R.K.; visualization, K.R.S.; supervision, L.T.S. and H.V.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
## 3 Results
### Correlations between SIF and T at Hourly and Daily Timescales  
Heatmaps for hourly and daily correlations of SIF and T, ET are shown in Figure 3. We found that SIF corresponded more closely to changes in T and ET at daily (Pearson correlation coefficient = 0.63, \(p\) < 0.01) compared to hourly (Pearson correlation coefficient = 0.53, \(p\) < 0.01) scales (Figure 3). The correlation between SIF and T varied significantly among different months (Pearson correlation coefficient = 0.29-0.68, \(p\) < 0.01; Figure 3). Both hourly and daily correlations were highest in October with a slight decrease in September and November (Figure 3). Previous studies have shown that the ENF canopy GPP yields a hyperbolic linear relationship with SIF in the fall transition [28; 29]. In this study, as GPP and T correlated with carbon and water cycle coupling at multiple time scales, SIF and Twere fitted by linear and hyperbolic regression. The results showed that SIF and T in the canopy were correlated at hourly and daily scales (Figures 4 and 5). Compared to linear regression (hourly: \(p<0.001\), R\({}^{2}=0.37\), RMSE = 3%; daily: \(p<0.001\), R\({}^{2}=0.39\), RMSE = 2%), hyperbolic regression yielded a slightly lower R\({}^{2}\) and similar RMSE (\(p<0.05\), R\({}^{2}=0.35\), RMSE = 3%) at hourly scale and a similar R\({}^{2}\) and RMSE (\(p<0.05\), R\({}^{2}=0.37\), RMSE = 2%) at daily scale in the fall. SIF and T were significantly linearly correlated at hourly (R\({}^{2}=0.53\), \(p<0.001\)) and daily (R\({}^{2}=0.67\), \(p<0.001\)) timescales in the October.  
Figure 3: The Pearson correlation coefficients between SIF and ET, T at hourly and daily scales for different months in the fall season.  
Figure 2: Hourly averages of far-red SIF, ET, GPP, T, PAR, VPD, air temperature, soil water and soil temperature (**A–I**) in September (blue), October (purple) and November (black). Shaded regions represent SE of the hourly mean.
## 3 Results and Discussion  
### Effect of pH on Azo Dye Removal Efficiency by Potassium Ferrate (K\({}_{2}\)FeO\({}_{4}\))  
As the oxidation process is significantly affected by pH conditions, optimized pH conditions in K\({}_{2}\)FeO\({}_{4}\) treatment (oxidation-coagulation) were investigated using the remaining azo dyes as a function of time. Figure 1 shows the remaining dye concentration (mg/L) of OD and LGB after reacting with K\({}_{2}\)FeO\({}_{4}\) as a function of time under different pH values (3, 6, 9, and 12). Overall, the removal efficiencies of both dyes by K\({}_{2}\)FeO\({}_{4}\) were enhanced under acidic conditions. The OD and LGB removal efficiencies increased from 57% to 79% when the pH decreased from 12 to 3. However, an increase in contact time did not enhance the performance of K\({}_{2}\)FeO\({}_{4}\) at all pH conditions after 5 min of reaction. Superior performance under acidic conditions was primarily achieved due to the higher reactivity of the K\({}_{2}\)FeO\({}_{4}\). Under acidic conditions, HFFeO\({}_{4}\)\({}^{-}\) may be generated and reacted with dyes [24]. Moreover, K\({}_{2}\)FeO\({}_{4}\) is known to be more reactive when the pH is lower than 7.3. Therefore, the optimized pH condition for the removal of dye by K\({}_{2}\)FeO\({}_{4}\) was confirmed to be pH 3. This indicates that the initial solution pH had little impact on its oxidizing power. Meanwhile, K\({}_{2}\)FeO\({}_{4}\) had a much higher oxidation potential at acidic conditions (E\({}_{0}\) = 2.20 V) than at basic conditions (E\({}_{0}\) = 0.72 V). At pH < 7, although the oxidative ability of ferrite is high, the ferrate is highly unstable. This resulted in more reactive conditions.  
The effect of pH on azo dye removal efficiency was verified by ANOVA. Variation of the pH from 3 to 12 led to statistically significant differences in the removal of OD by K\({}_{2}\)FeO\({}_{4}\). For OD removal by K\({}_{2}\)FeO\({}_{4}\), the F value (46.526) was higher than F-critical (3.239) and the \(p\)-value was 3.98E-08 (<0.05). For LGB removal by K\({}_{2}\)FeO\({}_{4}\), the F value (55.94) was higher than F-critical (3.239) and the \(p\)-value was 1.06E-08 (<0.05). This indicates that controlling pH in the dye removal experiment led to statistically significant differences in removal.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 12 Joint Routing and Scheduling in WSNs with Multiple Sinks having Different Location Possibilities
### Initial Problem Formulation: Time Based Formulation  
The first formulation considered the time to be continuous and an independent variable based upon which all decision variables are dependent. It was named as _time-based formulation_ and was considered very hard to tackle. A brief description of this formulation is as follows:  
* Decision variables: \(T\) is a continuous variable that represents the network's lifetime, \(g_{ij}\left(t\right)\) is a continuous variable that represents the data rate on the link from node \(i\) to node \(j\) at a given time \(t\), \(g_{io}\left(t\right)\) is a continuous variable that represents the data rate from node \(i\) to one of the possible sinks' positions \(o\) at a given time \(t\), \(x_{s,o}\left(t\right)\) is a binary variable that is set only when sink \(s\) resides in position \(o\).
* Objective function: is to maximize the lifetime of the network which is given as \(\underset{T,g_{ij}\left(t\right),S_{io}\left(t\right),x_{uo}\left(t\right)}{ max}\), \(T\).
* Constraint Sets: The constraints sets of the initial formulation are explained below: 1. Constraint set 1: is a linear constraint set in the binary variables \(x_{s,o}\left(t\right)\) that guarantees that a possible position \(o\) at \(t\) can get occupied by no more than one sink node, this is given as \(\sum_{s\in V_{s}}x_{s,o}\left(t\right)\leq 1,\ \ \forall o\in V_{o}\), where \(V_{s}\) and \(V_{o}\) are the sets of sink nodes and possible sink positions respectively. 2. Constraint set 2: is a linear constraint set in the binary variables \(x_{s,o}\left(t\right)\) that guarantees that sink \(s\) at time \(t\) can only reside in one location, this is given as \(\sum_{o\in V_{s}}x_{s,o}\left(t\right)\leq 1,\ \ \forall s\in V_{s}\).  
Figure 18: Mean and standard deviation of objective value for deterministic and robust solutions in minimum energy consumption problem for different uncertainty levels [6].  
Constraint set 3: linear conservation of flow equality constraints in the flow variables \(g_{i,j}\left(t\right)\) and \(g_{i,o}\left(t\right)\).
4. Constraint set 4: variable upper bounds on the flow variables \(g_{i,j}\left(t\right)\) from node to node links that represent the link capacity.
5. Constraint set 5: a mixed integer linear constraint in the variables \(g_{i,o}\left(t\right)\) and \(x_{s,o}\left(t\right)\) that impose link capacity on the flows from node \(i\) to the possible sink location \(o\) if any sink is assigned to that location, otherwise the flow is enforced to be zero. The constraint is \(g_{i,o}\left(t\right)\leq C_{i,o}.\sum_{s\in V}x_{s,o}\left(t\right)\forall l_ {i,o}\) where \(C_{i,o}\) is the capacity of the link \(l_{i,o}\).
6. Constraint set 6: An energy constraint for each sensor \(i\in S\) which is an integration of linear terms with respect to the time parameter \(t\) with the decision variable \(T\) in the upper limit of the integral.
7. Constraint set 7: non-negativity constraints on all the decision variables.
# Hybrid Crow Search Algorithm-LSTM System for Enhanced Stock Price Forecasting
## 2 Materials and Methods
### Data Generation
#### 2.1.2 Price-Fluctuation Limits  
Price-fluctuation limits are a regulatory mechanism employed by the Taiwan Stock Exchange to stabilize the market by suppressing excessive speculation and market manipulation and preventing steep fluctuations in a single day, thus lowering the overall risks. Specifically, the upper and lower limits of the stock market in Taiwan are limited to \(10\%\) of the closing price on the previous day.  
Based on this premise, we calculate the range the stock may reach. In other words, it is the previous day's price \(\pm 10\%\), which is used as our dataset.
## 4 The Proposed Model on DCGP  
The innovative model described below presents a novel approach to addressing uncertainty in supply chain optimization through chance-constrained goal Programming (CCGP).  
\[\begin{split}\text{Lexmin}&\{a_{1}^{-},a_{2}^{-}\}\\ \text{s.t.}&\quad\text{s.t.}\\ &\quad\text{M}(F_{1}-F_{1}(X,Y,\xi)\leq d_{1}^{-})\geq\alpha_{1} \\ &\quad\text{M}(F_{2}-F_{2}(X,Y,\xi)\leq d_{2}^{-})\geq\alpha_{2} \\ &\quad\text{M}\left(\sum_{m\in K}\text{QSP}_{(m,n,p)}\leq\text{ SCap}_{(m,n,t)}\right)\geq\beta_{n,p,t},\quad\forall s\in S,p\in P,t\in T\\ &\quad\text{M}\left(\sum_{m\in K}V_{rm}\cdot\text{IPKM}_{(m,p,t) }+\sum_{s\in S}\sum_{m\in M}V_{rm}\cdot\text{QSP}_{(m,n,p,t)}\leq\text{PCap}_{ (p,t)}^{1}\right)\geq\gamma_{p,t},\quad\forall p\in P,t\in T\\ &\quad\text{M}\left(\sum_{k\in K}V_{k}\cdot\text{IPK}_{(k,p,t)} \leq\text{PCap}_{(p,t)}^{2}\right)\geq\alpha_{p,t},\quad\forall p\in P,t\in T \\ &\quad\text{M}\left(\sum_{k\in K}V_{k}\cdot\text{QPD}_{(k,p,t) }+\sum_{k\in K}V_{k}\cdot\text{IDK}_{(k,d,t)}\leq\text{DKCap}_{(d,t)}\right) \geq\epsilon_{d,t},\quad\forall d\in D,t\in T\\ &\quad\text{M}\left(\sum_{d\in D}\text{QDC}_{(k,d,t)}\leq\text{ DC}_{(k,d,t)}\right)\geq\theta_{k,t},\quad\forall k\in K,c\in C,t\in T\\ &\quad\text{M}\left(\sum_{d\in D}(1-\lambda_{(k,d,t)})\cdot\text{ QDC}_{(k,d,t)}\leq(1-\text{Tacc}_{(k,c)})\cdot\text{DC}_{(k,t)}\right)\geq\zeta_{k,c,t}, \quad\forall k\in K,c\in C,t\in T\\ &\quad\text{M}\left(\sum_{k\in K}\text{pt}_{(k,p)}\cdot\text{ QPK}_{(k,p,t)}+\sum_{k\in K}\text{st}_{(k,p)}\cdot X_{(k,p,t)}\leq\text{TPTP}_{(p,t)} \right)\geq\eta_{p,t},\quad\forall p\in P,t\in T\\ &\quad\text{M}\left(\text{BQKC}_{(k,d,t)}=\text{BQKC}_{(k,d,t-1)}+ \text{DC}_{(k,c,t)}-\sum_{d\in D}\text{QDC}_{(k,d,t)}\right)\geq\theta_{k,c,t },\quad\forall k\in K,c\in C,t\in T\\ &\quad\text{M}\left(\text{BQKC}_{(k,d,t)}\leq\beta_{(k,c)}\cdot \text{DC}_{(k,c,t)}\right)\geq\iota_{k,d},\quad\forall k\in K,d\in D,c\in C,t \in T\\ &\quad\text{M}\left(\text{IPRM}_{(m,p,t)}=\text{IPRM}_{(m,p,t-1)}+ \sum_{k\in K}\alpha_{(m,k)}\cdot\text{QPK}_{(k,p,t)}\right)\geq\kappa_{rm,p,t},\quad\forall rm\in RM,p\in P,t\in T\\ &\quad\text{M}\left(\text{IPK}_{(k,p,t)}=\text{IPK}_{(k,p,t-1)}+ \text{QPK}_{(k,p,t)}-\sum_{d\in D}\text{QPD}_{(k,p,t)}\right)\geq\lambda_{k,p,t},\quad\forall k\in K,p\in P,t\in T\\ &\quad\text{M}\left(\text{IDK}_{(k,d,t)}=\text{IDK}_{(k,d,t-1)}+ \sum_{p\in P}\text{QPD}_{(k,p,t,t)}-\sum_{c\in C}\text{QDC}_{(k,d,c,t)}\right) \geq\mu_{k,d},\quad\forall k\in K,d\in D,t\in T\\ &\quad\text{M}\left(\sum_{d\in D}\text{QDC}_{(k,d,c,t)}-\text{BQKC} _{(k,d,t-1)}=\text{DC}_{(k,c,t)}\right)\geq\upsilon_{k,t},\quad\forall k\in K,c\in C\\ &\quad\text{QSP}_{(m,n,p,t)},\text{QPK}_{(k,p,t)},\text{IPRM}_{(m,p,t )},\text{IPK}_{(k,p,t)},\text{IDK}_{(k,d,t)}\geq 0\\ &\quad\text{QPD}_{(k,p,t,t)},\text{QDC}_{(k,d,c,t)},\text{BQKC}_{(k, d,t)}\geq 0\\ &\quad X_{(k,p,t)}\in\{0,1\}\end{split}\]