esa-sceva/satcom-chunk-collection huggingface dataset:
# Solution Merging in Matheuristics for Resource Constrained Job Scheduling
## 2 Resource Constrained Job Scheduling
### Model 2  
Let \(z_{jt}\) be a binary variable for all \(j\in J\) and \(t\in\mathcal{T}\), which takes value 1 if job \(j\) is completed at time \(t\)_or earlier_; that is, we effectively define \(z_{ji}:=\sum_{s\leq t}x_{js}\) or \(x_{jit}:=z_{jt}-z_{j,t-1}\). Substituting into the above model, we obtain our second formulation:  
min \[\sum_{j\in\mathcal{J}}\sum_{t\in\mathcal{T}}w_{ji}\cdot(z_{ji}-z_ {jt-1})\] (17) s.t. \[z_{jt_{\max}}=1 \forall\ j\in\mathcal{J}\] (18) \[z_{jt}-z_{jt-1}\geq 0 \forall\ j\in\mathcal{J},\ t\in\{1,\ldots,t_{\max}\}\] (19) \[z_{jt}=0 \forall\ t\in\{1,\ldots,r_{j}+p_{j}-1\},\quad\forall\ j\in \mathcal{J}\] (20) \[z_{bt}-z_{a,t-p_{b}}\leq 0 \forall\ (a,b)\in\mathcal{C},\ t\in\mathcal{T}\] (21) \[\sum_{j\in\mathcal{J}}z_{jt+p_{j}}-z_{jt}\leq 1 \forall\ i\in\mathcal{M},t\in\mathcal{T}\] (22) \[\sum_{j\in\mathcal{J}}g_{j}\cdot(z_{j,t+p_{j}}-z_{jt})\leq \mathcal{G} \forall\ t\in\mathcal{T}.\] (23)Constraints (18) ensure that all jobs are complete. Constraints (19) make sure that a jobs stays completed once it completes. Constraints (20) enforce the release times. Constraints (21) specify precedences between jobs \(a\) and \(b\) and Constraints (22) require that no more than one job is executed on a machine. Constraints (23) ensure that the resource constraints are satisfied. Previous work indicates that this type of formulation tends to perform better for branch and bound algorithms to solve scheduling problems [23; 34; 35].
# A New General Type-2 Fuzzy Predictive Scheme for PID Tuning
## 7 Temperature Control of CSTR Units  
### Control Performance  
By considering a CSTR (see Figure 4), and assuming that the physical properties of the reactor and its volume are constant and that its composition is complete, we can model the reactor according to Equations (19)-(21).  
\[\frac{dC_{A}}{dt}=\frac{q}{\
u}\big{(}C_{AF}-C_{A}\big{)} \tag{19}\] \[-k_{0}C_{A}exp\left(\frac{-F_{x}}{RT}\right)\varphi_{c}(t)\] \[\frac{dT}{dt}=\frac{q}{\
u}\big{(}T_{F}-T\big{)}\] \[-\frac{AH}{P\
u}k_{0}C_{A}exp\left(\frac{-F}{RT}\right)\varphi_{c}(t)\] (20) \[+\frac{\rho_{\text{C}}Cp_{\text{C}}}{P\
u P}\Big{(}1-\exp\left(- \frac{h_{A}}{4 cP\kappa_{P}}\varphi_{h}(t)\right)\Big{)}\Big{(}T_{cf}-T\Big{)}\] \[\frac{dT_{j}}{dt}=\frac{UA}{pc_{P}V}\big{(}T-T_{j}\big{)}+\frac{u }{V_{j}}\big{(}T_{jF}-T_{j}\big{)} \tag{21}\]  
The parameters used in Equations (19)-(21) are as given in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \(C_{A}\) & Concentration of the substance A inside the reactor \\ \(T\) & Temperature inside the reactor \\ Q & feed fluid flow, 0.2 m\({}^{3}\)/min \\ \(V\) & reactor volume, 2 m\({}^{3}\) \\ \(T_{F}\) & feed temperature, 30 \({}^{\circ}\)C \\ \(P\) & Medium density of material, 1000 kg/m\({}^{3}\) \\ \(k_{0}\) & constant reaction speed, \(3.5\times 10^{6}\) L/min \\ \(E_{a}\) & activation energy, 49.88 K/mol \\ \(UA\) & heat transfer coefficient, 252 kj/min\({}^{\circ}\)C \\ \(R\) & gases constant, \(8.134\times 10^{-3}\) kj/mol\({}^{\circ}\)C \\ \(V_{j}\) & jacket volume, 0.4 m\({}^{3}\) \\ \(H_{r}\) & heat of reaction, 500 kj/mol \\ \(T_{jF}\) & inlet fluid temperature to the jacket, 10 \({}^{\circ}\)C \\ \(\text{C}_{AF}\) & Concentration of substance A in feed, 100 mol/m\({}^{3}\) \\ \(\varphi_{c}(t)\) & Deactivation coefficient, 1 \\ \(\varphi_{h}(t)\) & Fouling coefficient, 1 \\ \(h_{A}\) & Heat transfer term, \(7\times 105\) cal/(min \(\cdot\) k) \\ \(T\) & Reactor temperature, 438.7763 K \\ \hline \end{tabular}
\end{table}
Table 1: System description.  
Figure 4: CSTR Unit performance process.
## 2 Efficient Network AOS Comprehensive Multiplexing Model
Due to the complexity of the AOS multiplexing and scheduling algorithm process, on the basis of theoretical analysis, the selection of buffer capacity can be further optimized through simulation and actual debugging. In the model, the virtual channel scheduling adopts a regularly spaced scheduling cycle, with a fixed number of \(K\) scheduling slots set within each scheduling cycle. The value of \(K\) should be greater than the number of virtual channels, with each time slot transmitting one channel transmission frame. Since the AOS frame uses a fixed transmission frame length, the total amount of data \(V\) transmitted in one transmission frame time slot and the occupied time \(\Delta t\) are both fixed values. Here, \(V\) is determined by the number of bytes in the transmission frame specified by the AOS protocol, and \(\Delta t\) is determined by \(V\) and the AOS processing speed. The virtual channel scheduling algorithm makes scheduling decisions at the beginning of each scheduling slot. Assuming the current scheduling cycle is \(r\), and the scheduling slot within the current cycle is \(k\) (where \(k\) takes values from 0, 1, \(\ldots\), \(K\)), then the current scheduling moment is denoted by \((r,k)_{sche}\).
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 7 Suppression Method of Photodarkening
### Changing the Pump Wavelength  
Some phenomena show that PD is related to the upconversion phenomenon, which can be suppressed by suppressing up conversion phenomenon. Taking Tm-doped fiber as an example, the phenomenon of PD was observed at the pump wavelength of 1.06-1.14 \(\upmu\)m [4; 10; 11; 14; 55; 56], but some studies show that in Tm-doped fiber, the phenomenon of PD has not been found at the pump wavelength of 1.25-1.6 \(\upmu\)m [11]. If we avoid the wavelength that may cause up conversion phenomenon, we may avoid the phenomenon of PD, but it is impossible to change the wavelength at will in the construction of light laser, so the method of changing the wavelength is very limited.
## 3 Results and Discussion  
### Antioxidant Properties of GPP and PE  
At first, the antioxidant properties of GPP and PE were investigated by widely used assays, i.e., DPPH, FRAP, CUPRAC, and TPC assays. In the case of GPP, the experiments were carried out following the \"QUENCHER\" method, which allows one to measure the efficiency of electron transfer processes from a solid antioxidant [23; 24; 25; 26; 27]. As shown in Table 3, both samples exhibited remarkable antioxidant properties, which were significantly higher when compared with other agri-food by-products, such as spent coffee grounds, banana, pineapple, apple, and orange by-products [31; 32]. The high antioxidant activity observed is attributed to the presence of significant amounts of phenolic compounds in both by-products. In particular, pomegranate peels are rich in hydrolysable ellagitannins (mainly punicalagin and punicalin), endowed with very high antioxidant potency. On the other hand, grape pomace is rich in condensed tannins, together with small phenolic acids and anthocyanins [31]. Notably, PE exhibited stronger antioxidant properties and TPC values that were about 5-fold higher than those of the GPP sample. This difference in antioxidant activity and polyphenol content could be due to the different physical states of the two samples. In the case of GPP, the solid nature could decrease the solubility and dispersion of the sample in the assay medium, reducing the efficiency of electron or hydrogen transfer processes, leading to a decrease in the measured values.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **EC50 (mg/mL)** & **Trolox** & **Trolox** & **Gallic Acid** \\  & **(DPPH)** & **Equivalents** & **Equivalents** & **Equivalents** \\  & & **(FRAP)** & **(CURPAC)** & **(TPC)** \\ \hline GPP & 0.365 \(\pm\) 0.041 a & 0.024 \(\pm\) 0.002 b & 0.049 \(\pm\) 0.003 b & 0.045 \(\pm\) 0.001 b \\ PE & 0.031 \(\pm\) 0.001 b & 0.645 \(\pm\) 0.028 a & 0.316 \(\pm\) 0.003 a & 0.221 \(\pm\) 0.001 a \\ \hline \hline \end{tabular} Data are presented as mean \(\pm\) SD. Data in each column with different superscript letters are statistically different (\(p<0.05\)). GPP = grape pomace powder; PE = pomegranate extract.  
\end{table}
Table 3: Antioxidant properties of GPP and PE samples.
## 3 Results and Discussion
### Calculated Oxidizability Value of Oils  
A COX value is a beneficial element usually taken to evaluate the oil's tendency to undergo autoxidation. Based on the calculated COX values (Table 3), it could be concluded that the oils had different values of this parameter. The oxidative stabilities of the tested oils were in the following order: BO > FO > WCO > TO > PO > DO > CO. Among all the analysed oils, BO was the most susceptible to oxidation, where the COX value reached 13.56. It should be noted that CO had a COX value six times better than that of BO (2.35). Most of the analysed oils had high oxidizability values, higher than those investigated by [PERSON] et al. [49]: palm (1.62), peanut (4.63), and amelina oil (1.77). [PERSON] et al. [50] reported that the COX value of extra virgin olive oil equals 2.38. It follows that the tested oils should undergo rapid oxidation. It is noteworthy to mention that the rapidity of oxidation depends on the degree of unsaturation, the presence of antioxidants, and the prior storage conditions [51].
# Crowdsourcing Framework for QoE-Aware SD-WAN +
## 6 Performance Evaluation
### SD-WAN Rerouting Experiment Analysis  
The SD-WAN rerouting experiment was conducted in a controlled lab environment in which each participant watched a 3 min live streaming video and was asked to provide QoE feedback in the form of a 'dislike' click where quality is degraded on the basis of timely changed scenarios of event. Participants could provide feedback clicks at any time during the viewing session. In our experiment, due to the limited number of participants, we assumed that the response from a participant represented a summary of feedback from a group of users, i.e., each user essentially represented a summarization server. As such, a 10 s waiting time was enforced between acceptance of two consecutive clicks to better represent the summarization, as well as to ensure that the controller's rerouting was complete, thus avoiding a ping-pong routing problem.  
Figure 8 shows the timing of the vents for QoE-aware rerouting experiment for a sample participant, where red dots represent ping time (right axis), the blue line is the iperf loss percentage (left axis), green bars are VLC player errors, and the gray background indicates active minievents scenarios. It is observed that the streaming quality improved after every participant feedback click, where it was noted that the blue line indicated no further loss on the streaming link. It was noticed that, when applying a typical delay without loss, users did not detect quality degradation, and they typically did not click the feedback button, primarily due to the playback buffering capabilities of the VLC player. With gradually increasing packet loss percentage with minimum delay, the QoE was affected immediately, and the feedback for reroute was accordingly received. By applying degrading changes on two paths out of three, it was found that ONOS kept searching for the alternative routes with every click until the quality was acceptable for the user.  
With all participants, it was found that ONOS managed to reroute traffic on the basis of user feedback, and the quality of the stream was enhanced after the controller's enhancement action. Among all participants, the ONOS reaction and response for the rerouting decision was consistent and timely. It took ONOS 10-15 ms to construct a new route (intent) as a new streaming path and 15-20 ms to reroute the traffic.  
These results demonstrate that users' interactive feedback can be taken into consideration during streaming sessions, and this feedback can be communicated in a timely manner to the SDN controller to alert of an existing issue and that a corrective action is required. It was shown that the rerouting decision can be made on the spot, and quality can be enhanced on the basis of external user feedback.
# On-the-Fly Repairing of Atomicity Violations in ARINC 653 Software
## 7 Conclusions  
It is essential for the aircraft health management system in ARINC 653-based concurrent programs to repair atomicity violations on the fly to ensure the program's normal  
Figure 11: Repairing time of synthetic programs with 1,280,000 iterations (note that the treatment time for RAV is described above the bar) [16].  
execution. In previous works, atomicity violations have been diagnosed by comparing the access history generated and maintained whenever an access operation is executed with the access information during execution. There is time and space overhead for repairing whenever an access operation executes. This paper proposes RAV (Repairing Atomicity Violation). It diagnoses atomicity violations by using correct interleaving collected in the test phase.  
RAV showed a time overhead of 1.6\(\times\) 1.20\(\times\) regardless of the number of accesses to shared variables. It also showed the space overhead of 219 KB. The aircraft is a hard real-time system, so the program must have a constant time and space overhead during operation. If the design considers the time and space overhead required for repairs, RAV is well suited for use in airborne software.  
Conceptualization, T.-h.K. and Y.-K.J.; methodology, E.-t.C.; software, E.-t.C. and T.-h.K.; validation, E.-t.C., and M.H.; investigation, E.-t.C. and T.-h.K.; resources, E.-t.C.; writing--original draft preparation, E.-t.C. and T.-h.K.; writing--review and editing, S.L.; visualization, E.-t.C.; supervision, Y.-K.J. and S.L.; project administration, Y.-K.J.; funding acquisition, Y.-K.J. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research study was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF2018R1D1A3B07041838) and was supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program, 20005378, Open Avionics System Architecture and Software Development for Small to Medium Aircraft Class) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea). It was also supported by the Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government (21 ZS1300; Research on High Performance Computing Technology to overcome limitations of AI processing).  
Not applicable.  
The authors declare no conflict of interest.
## 3 The Optimization and Reconfiguration of UDNs Incorporating VPPs
### Constraints
#### 3.2.8 VPP Participation in Demand Response Constraints  
The VPP participation in demand response constraint is as shown in (36)-(38).  
\[P_{\text{VPP}}^{t}=P_{\text{buy}}^{0,t}-P_{\text{buy}}^{t} \tag{36}\]  
\[P_{\text{peak}}^{\text{DR},t}=\begin{cases}\text{max}\big{(}P_{\text{VPP}}^{t },0\big{)}&t\in t_{\text{peak}}\\ 0&t\
otin t_{\text{peak}}\end{cases} \tag{37}\]  
\[P_{\text{valley}}^{\text{DR},t}=\begin{cases}\text{max}\big{(}-P_{\text{VPP}}^{t },0\big{)}&t\in t_{\text{valley}}\\ 0&t\
otin t_{\text{valley}}\end{cases} \tag{38}\]where \(P_{\rm buy}^{0,t}\) is the power purchased from the main grid by the UDN operator at time \(t\) without invoking the VPP; \(t_{\rm peak}\) and \(t_{\rm valley}\) are the sets of time periods for peak shaving and valley filling demand response, respectively.
## 3 Results
### In Vivo MRI of Tumor-Bearing Mice through Intravenous Injections  
To study the clinical potential of the prepared nanoparticles, the contrast feasibility of MNPs or LHRH-MNPs under systematic circulation inside the mice were investigated through intravenous injection in tumor-bearing mice. Figure 9 shows T2-weighted MR images after injection of MNPs or LHRH-MNPs through the tail vein at a concentration of 20 mg of Fe\({}_{3}\)O\({}_{4}\) per kg of mouse body weight. Significant signal attenuation was observed in the tumor region in the LHRH-MNP treated group at 2 h post injection, and the contrast became enhanced after 24 h, which facilitated the differentiation between tumors and surrounding tissues (Figure 9B). However, such contrast changes were not obvious in the mice injected with MNPs (Figure 9A).  
Figure 8: T2-weighted MR images of nude mice bearing xenograft breast tumors 4-weeks post tumor induction before and after intratumoral injection of (**A**) MNPs or (**B**) LHRH-MNPs. Tumor sites in mice are identified by yellow dotted circles, and the darkened areas within the tumors are highlighted with yellow arrows. The same animal was scanned before, immediately after, 2 h post, 24 h post and 2 weeks post nanoparticle injection. (**C**) CNR of tumors treated with MNPs or LHRH-MNPs. (**D**) Relative contrast of tumors treated with MNPs or LHRH-MNPs.  
The tumor-to-nanoparticle contrast varied over time and the maximum relative contrast were found to be \(25\pm 1\%\) and \(66\pm 4\%\) at 24 h post injection for MNPs and LHRH-MNPs (\(p<0.0001\)), respectively (Figure 9C). No statistical significance in attenuation was detected for MNPs between 2 h (\(15\pm 4\%\)) and 24 h (\(25\pm 1\%\)) (\(p=0.2\)), whereas the negative enhancement in LHRH-MNP treated group was significant (\(25\pm 1\%\) at 2 h and \(66\pm 4\%\) at 24 h, \(p<0.0001\)). The index suggested that LHRH-MNPs exhibited much higher signal changes for breast tumor imaging and detection limit compared to MNPs as the T2 based MRI contrast agents. The current results also highlight the potential for future applications of LHRH-MNPs as contrast agents in the early diagnosis of cancer.
# Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data
## 2 Materials and Methods
### Methods  
#### 2.3.1 Selecting the Optimal Spectral Variables of Dry Soil  
One of the most important steps in the development of the soil hyperspectral estimation method of CLQ was the determination of the optimal spectral variables. In this study, the feature importance (\(\hat{I}_{j}\)) of spectral variables obtained using the gradient boosting  
Figure 4: The result of component decomposition based on FCLS: (**a**) soil abundance; (**b**) soil spectral reflectance at 900 nm of the HJ-1A HSI data.  
Figure 3: The spectral variables curves: (**a**) original soil spectral curves; (**b**) dry soil spectral curves; (**c**) the first derivative spectral curves; (**d**) the second derivative spectral curves.  
decision tree (GBDT), combined with the variance inflation factor (VIF), was used to select the spectral variables of dry soil. The formula of (\(\hat{I}_{j}\)) is written as [24; 25]:  
\[\hat{I}_{j}=\frac{1}{M}\sum_{m=1}^{M}I_{j}(T_{m}) \tag{2}\]  
where \(I_{j}(T_{m})\) is the feature importance of the spectral variables in a single tree, \(M\) is the number of trees, and \(T\) denotes the spectral variables. \(I_{j}(T_{m})\) is calculated by loss reduction after splitting, expressed as:  
\[I_{j}(T)=\sum_{i=1}^{L-1}i_{t}(v_{t}=j) \tag{3}\]  
where \(L\) is the number of leaf nodes, \(L-1\) is the number of non-leaf nodes, \(v_{t}\) is the feature associated with node \(t\), and \(i_{t}\) is the square of the loss value after node splitting. In this study, we selected the spectral variables with \(\hat{I}_{j}>0.003\).  
In order to reduce the collinearity, the variance inflation factor (VIF) was used for further selection of optimal spectral variables, and the spectral variables with a VIF within ten will be retained [26].
## 1 Introduction
### Novelty of the Integrated Approach  
The study presents a novel, integrated geophysical approach for diagnosing and mitigating highway pavement subsidence by combining InSAR, GPR, and ERT methods. Unlike previous research, which typically applies these techniques in isolation or in limited combinations, this work demonstrates their synergistic potential in a real-world infrastructure setting along Road 431, Israel. The study uniquely correlates surface subsidence trends (InSAR) with detailed subsurface anomalies (GPR and ERT), providing a high-resolution, multi-depth diagnostic framework. This research bridges the critical gap between surface monitoring and subsurface characterization by identifying and validating karstic voids, disintegrated fill layers, and groundwater fluctuations through borehole drilling. Additionally, incorporating PSI-based InSAR enables precise subsidence rate measurements at the millimeter scale, a significant advancement in the geotechnical monitoring of transportation. The findings offer a first-of-its-kind roadmap for integrating satellite, ground-based geophysics, and direct validation techniques to enhance geotechnical risk assessment and infrastructure resilience. This approach not only refines subsidence detection accuracy but also provides a transferable framework for monitoring and mitigating geohazards in transportation networks worldwide.
## 4 Hyperspectral Image Destriping Method
### Discussion
#### 5.4.4 Adaptability of Algorithms  
In this part, we introduce the adaptability of this method to other real remote sensing images. We selected three real images in the experiment, including MODIS satellite images, Hyperion images and CHRIS images.  
The first data is MODIS, and we select three bands (bands 8, 10 and 14) to do the single-band stripe removal experiment. The second data is Hyperion, and we select some bands (bands 8, 57 and 79) of \(256\times 256\) for the single-band destriping experiment and extract a sub-image (bands 93-102) of \(256\times 256\times 10\) for the hyperspectral destriping experiment. The last data is CHRIS, which can get images from five different angles. In our study, we choose an image obtained using mode-1, which has \(748\times 766\) pixels with 18 bands.  
This image (bands 1, 2 and 3) is adopted for the single-band destriping experiment, and a sub-image of \(500\times 500\times 18\) of this image is used for the hyperspectral destriping experiment.  
Figures 28-30 show the single-band destriping results of the three real datasets. Figures 31-34 show the multi-band destriping results of the two real datasets. We can see that the method proposed in this paper can completely remove the stripe noise of the three data, and has a good ability to maintain image details.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Single-Band Method(s)** & **WFAF** & **SLD** & **LRSID** & **GSTV** & **Ours** \\ \hline Band 1 & 2.51 & 2.57 & 79.65 & 38.41 & 32.12 \\ Band 15 & 4.61 & 7.64 & 76.32 & 53.99 & 29.72 \\ Band 30 & 3.91 & 4.56 & 51.33 & 43.76 & 28.25 \\ \hline
**Hyperspectral Method(s)** & **ASSTV** & **LRMID** & **LRTD** & **Ours** & \\ \hline  & 557 & 1070 & 3314 & 778 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of Running Time.  
Figure 28: MODIS raw data and corrected data. (**a**) Original image of band 8; (**b**) original image of band 10; (**c**) original image of band 14; (**d**) corrected image of band 8; (**e**) corrected image of band 10; (**f**) corrected image of band 14.  
Figure 30: CHRIS raw data and corrected data. (**a**) Original image of band 1; (**b**) original image of band 2; (**c**) original image of band 3; (**d**) corrected image of band 1; (**e**) corrected image of band 2; (**f**) corrected image of band 3.  
Figure 31: Hyperion raw data. (**a**) Original image of band 93; (**b**) original image of band 95; (**c**) original image of band 97; (**d**) original image of band 99; (**e**) original image of band 101.  
Figure 29: Hyperion raw data and corrected data. (**a**) Original image of band 8; (**b**) original image of band 57; (**c**) original image of band 79; (**d**) corrected image of band 8; (**e**) corrected image of band 57; (**f**) corrected image of band 79.