esa-sceva/satcom-chunk-collection huggingface dataset:
# No-Wait Job Shop Scheduling Using a Population-Based Iterated Greedy Algorithm
## 2 No-Wait Job Shop Scheduling Problem (NWJSP) with Makespan Minimization
### Problem Formulation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Notation.** & **Description** & **Notation** & **Description** \\ \hline \(m\) & number of machines & \(t_{i}\) & start time of \(j_{i}\) \\  & number of jobs & \(\pi=(\pi_{[1]},\ldots,\pi_{[n]})\) & job permutation \\ \(M=\{M_{1},M_{2},\ldots,M_{m}\}\) & set of \(m\) machines & \(t_{i}^{0}\) & start time of \(\pi_{[i]}\) \\ \(J=\{I_{1},I_{2},\ldots,I_{n}\}\) & set of \(n\) jobs & \(\sigma_{i}^{k}\) & operation of \(j_{i}\) on \(M_{k}\) \\ \(o_{(u)}\) & \(u\)-th operation of \(j_{i}\) & \(p_{i}^{k}\) & processing time of \(o_{i}^{k}\) \\ \(M_{i,u}\) & machine on which \(o_{i,u}\) is processed & \(S_{ij}\) & start time difference set \\ \(P_{i,u}\) & processing time of \(o_{i,u}\) & \(C_{\max}(\pi)\) & makespan of \(\pi\) \\ \(P_{i,u}=\sum_{u=1}^{n}p_{i,v}\) & cumulated processing time of \(j_{i}\) when \(\sigma_{u}\) is finished & \(ST_{\pi}=(t_{[1]},\ldots,t_{[n]},C_{\max}(\pi))\) & start time table of \(\pi\) \\ \(\xi_{ij}=\{(u,v)\}\) \(M_{i,u}=M_{j,v}\) & pairs of operations processed on the & \(p_{i}^{k}\) & cumulated processing time of \(j_{i}\) \\  & same machine & & when \(o_{i}^{k}\) is finished \\ \(L_{i}\) & total processing times of \(j_{i}\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations used throughout the paper. has \(n!\) solutions. The purpose of the timetabling sub-problem is to find a timetable, \(ST_{\pi}=(t_{[1]},\ldots,t_{[n]},C_{\max}(\pi))\), with a minimum \(C_{\max}(\pi)\) based on a given job sequence \(\pi=(\pi_{[1]},\ldots,\pi_{[n]})\).
# Situational Awareness and Problems of Its Formation in the Tasks of UAV Behavior Control
## 5 Forming Elements of Situational Awareness for UAVs
#### 5.2.1 A Formation Experiment for Visual Components of Situational Awareness  
As mentioned above, an essential part of UAV situational awareness is data about objects in the space around the UAV. These objects can be flying vehicles or other things, such as birds. In addition, some tasks performed by UAVs require information about objects on the Earth's surface. In particular, this information will be needed by the control system of the UAV when performing a landing. This information will be critical when the UAV is required to land on an unprepared site in an unfamiliar location. In this situation, computer vision allows the control system to select a place that meets the conditions for a safe landing.  
The main element of the task mentioned above of selecting a suitable landing site for the UAV is the semantic segmentation of the scene observed by video surveillance. The results of such a solution make it possible to identify areas of terrain suitable for landing and objects, including moving ones, which can interfere with the landing. The problem of tracking and predicting their motion, which was discussed in the previous section, needs to be solved for dynamic scene objects. The solution for this problem is necessary for the UAV behavior control system to have information to prevent potentially dangerous situations. We discuss this task further using the example of an urban scene to perform more accurate maneuvers and land UAVs in an urban environment.
## 2 Basic Principle of SAR Imaging Simulation
### SAR Echo Signal Simulation
#### 2.3.1 Generation of the Ocean Surface Backscattering Coefficient  
In this paper, a two-scale electromagnetic scattering model was used to calculate the ocean surface backscattering coefficient, and the SAR working mode was defined as vertical (VV) polarization. The backscattering coefficient under VV polarization can be expressed as follows [26]:  
\[\sigma^{0}_{VV}(\theta_{i})=\frac{1}{MN}\sigma^{0}_{BVV}(\theta^{\prime})\frac {[\sin\theta_{i}-\alpha(x,y)\cos\theta_{i}]^{4}[1+\alpha(x,y)\tan\theta_{i}]} {\Big{\{}\beta(x,y)^{2}+[\sin\theta_{i}-\alpha(x,y)\cos\theta_{i}]^{2}\Big{\}} ^{2}} \tag{7}\]  
where \(\alpha(x,y)\) and \(\beta(x,y)\) represent the slopes of the ocean surface at point \((x,y)\), which can be obtained by differentiating, and \(\sigma^{0}_{BVV}(\theta^{\prime})\) is the backscatter coefficient obtained by the perturbation method in the local coordinate system [27] as follows:  
\[\sigma^{0}_{BVV}(\theta^{\prime}_{i})=8k^{4}\cos^{4}\theta^{ \prime}_{i}\big{|}\big{|}\big{|}\big{.}\big{.}\big{.}\big{.}\big{.}\big{.} \big{.  
where \(\varepsilon\) represents the dielectric constant of seawater and can be obtained by the Debye Equation [28] as follows:  
\[\varepsilon=\varepsilon_{0}+\frac{\varepsilon_{s}-\varepsilon_{\infty}}{1+j \omega\tau}-j\frac{\sigma}{\omega\varepsilon_{0}} \tag{9}\]  
where \(\varepsilon_{\infty}\) represents the dielectric constant of seawater when the frequency is infinite; \(\varepsilon_{s}\) denotes the static dielectric constant; \(\tau\) represents the relaxation time; \(\sigma\) is the conductivity of seawater; \(\varepsilon_{0}=8.854\ast 10^{-12}\;F/m\) is the free space permittivity; and the above parameters are all related to the seawater temperature or salinity function [29].
## 7 Conclusions  
In this paper, an optimal solution for reducing the opening time of BF top bleeders in an existing BF is presented. The numerous configurations tested by CFD analysis made it possible to outline the final operational solution. This solution involves the construction of a pipeline (DN800) detaching from the dust catcher for partial bypassing of the cyclones, which is able to deliver the flow rate taken directly downstream of them. A servo-assisted valve (dirty relief valve DN800) is installed on this pipeline, calibrated to intervene at 850 mmWC and able to control low critical situations, avoiding the opening of the emergency bleeders at the BF top and keeping the network pressure within the tolerability limit (350 mmWC).  
As the critical level increases, the partial bypass of the cyclones is no longer sufficient to prevent the opening of the bleeder and keep the pressure excursions in the network within acceptable limits. To remedy this, an additional servo-assisted valve (clean relief valve DN800) was installed immediately after one of the three scrubbers, through which the previously cooled and cleaned BF gas is released into the atmosphere with almost no visual impact.  
It should be remembered that, in the chosen configuration, the safety levels are not affected because the BF mouth bleeders has not been modified or calibrated differently.  
During the commissioning phase [PERSON] and [PERSON] specialists made the fine tuning of the valve's intervention, optimizing the setpoints and control logics in order to limit as much as possible the intervention of emergency bleeders only to the most serious overpressure cases.  
After final commissioning of the new relief system, the opening of the emergency bleeder valves has been dramatically reduced with only some events in case of great overpressure phenomena, demonstrating the effectiveness of the adopted solution. The installation of the new equipment has reduced the total opening time of the BF top bleeders per year by more than 50% and after the tuning of the equipment in the field, by more than 96%.  
Figure 21: Production trend and total opening time of BF top bleeders in the same timeframe.  
**Author Contributions:** Conceptualization, M.B., E.P., L.P., N.P. and V.D.; Data curation, N.P., V.D. and S.N.; Formal analysis, L.P., V.D. and S.N.; Investigation, L.P., N.P. and S.N.; Methodology, N.P., V.D. and S.N.; Supervision M.B., E.P. and L.P.; Writing--original draft, M.B., E.P., L.P., N.P. and S.N.; Writing--review & editing, M.B., E.P. and L.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Lab-on-Chip Culturing System for Fungi--Towards Nanosatellite Missions
## 3 Results and Discussion  
### Fungi Cultivation in Simulated Microgravity  
At first, the studies on the influence of microgravity on the culture of fungi _F. culmorum_ were carried out. The fungi spores were placed in the lab-on-chip (Figure 9) according to the procedure described above. The lab-on-chip medium chamber was filled with sterile distilled water and the gas chamber was empty. The medium was changed manually with a pipette every 4 days. All inlets and outlets were covered with parafilm to limit evaporation of the medium while ensuring air supply to the gas chamber. The lab-on-chip was placed in the central part of the RVV which was rotating at a speed of 1 rotation per 4 s. The fungi cultivation lasted for 14 days. Simultaneously, a reference culture was provided on-chip.  
Apart from the microscopic observation, growth of _F. culmorum_ was assessed quantitatively, as percentage of microscopic image coverage. For this purpose, on the top of photo, a grid resembling of [PERSON]'s counting chamber was superimposed. Coverage percentage was calculated as a sum of all the grid's elements where the mycelium was noticed.  
Correct fungi growth was observed for both the RVV and reference cultures (Table 2). The spore germination process started on the third day, which was more visible for the LOC placed in the RVV than for the control LOC. Over the following days, the hyphae grew until the mycelium formed. The mycelium growth in the LOC on the RVV was noticeably more branched, thus the simulated microgravity stimulated the development of the fungi in our experiment. It can be assumed that such cultivation in space would also bring a positive result.
# Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network
## 4 Simulations
### Performance Metrics  
For a simulation study, the following metrics are used in order to measure the performance of MICA and MCM:  
* _Average packet_: an average packet is defined as the average number of packets that each multi-receiver receives successfully during the simulation time.
* _Average delay_: an average delay is the average time taken for a packet to be transmitted across a network from source to destination.
* _Standard deviation_: the standard deviation is the variability or dispersion of packets received by all multi-receivers.
## 4 Case Studies
### An Experimental Case Study on Complex-Shaped Metallic Turbine Blades
#### 4.1.1 Reference MD Space Based on Healthy Samples  
First, the turbine blades are classified with the IMCS algorithm by considering a reference MD space that is constructed based on the healthy samples.  
Among the initial 15 features (resonance frequencies extracted from FRFs), only 6 out of 15 features are screened: \(f_{2},f_{4},f_{5},f_{8},f_{14},f_{15}\approx\) (5.9, 9.6, 10.7, 19.8, 25.9, 36.0) kHz.  
The classification results are shown in Figure 6, where the misclassifications are marked with black blocks. SMD (scaled Mahalanobis distance) in Figure 6 stands for the scaled Mahalanobis distance, which is obtained by considering a logarithmic normalization, as follows:  
\[\mathit{SMD\_training}=\frac{\mathit{log}\big{(}\mathit{MD}^{\mathit{training} }\big{)}-\mathit{min}\big{(}\mathit{log}\big{(}\mathit{MD}^{\mathit{training} }\big{)}\big{)}}{\mathit{max}\big{(}\mathit{log}\big{(}\mathit{MD}^{\mathit{training }}\big{)}\big{)}-\mathit{min}\big{(}\mathit{log}\big{(}\mathit{MD}^{\mathit{training }}\big{)}\big{)}} \tag{3}\]  
\[\mathit{SMD\_testing}=\frac{\mathit{log}\big{(}\mathit{MD}^{\mathit{testing} }\big{)}-\mathit{min}\big{(}\mathit{log}\big{(}\mathit{MD}^{\mathit{training} }\big{)}\big{)}}{\mathit{max}\big{(}\mathit{log}\big{(}\mathit{MD}^{\mathit{ training}}\big{)}\big{)}-\mathit{min}\big{(}\mathit{log}\big{(}\mathit{MD}^{\mathit{training }}\big{)}\big{)}} \tag{4}\]  
The procedure was run 20 times, independently, and the average accuracy and standard deviation of classification results are summarized in Table 1. IMCS achieved accuracies of 96.40% and 96.77% for the training pool dataset and testing dataset, respectively. Note that a small value of standard deviation and a relatively large standard deviation can be observed in training and testing accuracy,  
Figure 5: **Frequency response functions (FRFs)** of two healthy samples and one defective sample.  
respectively, reflecting the discrepancy between the populations of the training and testing samples due to the sparse dataset.
# A Two-Step High-Order Compact Corrected WENO Scheme
## 4 Error Analysis and Efficiency Comparison
### Efficiency Comparison  
Figure 8 presents a detailed comparison of the running times (in seconds) for five schemes in solving the two interacting blast waves problem: the original WENO scheme, WENO with characteristic-wise reconstruction, the MWCS, and the proposed CCWENO and CCWENO2 schemes. The computations were executed on the same personal computer equipped with an AMD Ryzen 7 5700U processor, ensuring reliable performance metrics.  
The analysis reveals that the CCWENO scheme incurs only a modest increase in computational effort compared to the standard WENO scheme without characteristic-wise reconstruction. However, it demonstrates significantly reduced running time when benchmarked against the MWCS and WENO with characteristic-wise reconstruction schemes, making it a more efficient choice overall. On the other hand, while the numerical results of CCWENO2 closely resemble those obtained by CCWENO in terms of accuracy and solution quality, CCWENO2 demands comparatively higher computational time.  
These findings underscore the computational efficiency of the CCWENO scheme, particularly in scenarios where a balance between accuracy and runtime performance is critical. Meanwhile, the CCWENO2 scheme offers similar numerical reliability but  
Figure 6: Dissipation and dispersion error in discontinuous region.  
Figure 7: Dissipation and dispersion error in extreme discontinuous region.  
introduces a trade-off in terms of runtime, which may influence its applicability depending on specific use-case requirements.
## 3 Low-Complexity Optimization of the Algorithm
### Multiplex FIR Filter
The coefficient selection module (coe_set sel) and the channel selection module (channel sel) jointly process the sampled data of the signal, corresponding to the matching relationship between the channels and coefficients. Additionally, since the sampled data of multiple input channels share the same FIR filter sampling data, a time-division multiplexing mechanism is applied to transmit the single-channel input data. This approach increases the filter frequency while performing interleaved input processing of the input signals. The structure is shown in Figure 7.  
Figure 6: Multi-channel multi-coefficient FIR filter module diagram. In this structure, \(L_{l}(t_{1})\) and \(L_{Q}(t_{1})\) represent the values of the single-channel \(I/Q\) branches at a certain time, while \(L_{l}(t_{2})\) and \(L_{Q}(t_{2})\) represent the values at the next time. By increasing the oversampling factor \(N_{s}\), the single-channel data are interleaved and transformed into multi-channel data. Through using the structure of multiplexed FIR filters, the parallelism of computations in a single matching branch is improved, thereby reducing the computational time and resource consumption of each branch and supporting higher throughput.
# Optimization Design of the Lower Rocker Arm of a Vertical Roller Mill Based on ANSYS Workbench
## 3 Optimum Design of Response Surface Analysis Method  
### Mathematical Model of Optimal Design  
(1) Input variables  
The structural size of the internal cavity can be completely defined by selecting seven dimensional parameters: \(x_{1}\), \(x_{2}\), \(x_{3}\), \(x_{4}\), \(x_{5}\), \(x_{6}\), and \(x_{7}\), as shown in Figure 6. Additionally, since the input variables \(x_{1}\), \(x_{2}\), \(x_{3}\), and \(x_{4}\) all represent the thickness of the cavity wall and feature the same size values, it is possible to set \(x_{2}\), \(x_{3}\), and \(x_{4}=x_{1}\).  
The expression of the input variable can be expressed as a vector.  
\[x=(x_{1},x_{5},x_{6},x_{7})^{T} \tag{6}\]  
Taking into account the assembly relationships and process constraints, the range of constraints for each input variable is as follows:  
\(127.5\) mm \(\leq x_{1}\leq 155.8\) mm  
\(705.0\) mm \(\leq x_{5}\leq 849.4\) mm  
\(94.5\) mm \(\leq x_{6}\leq 107.0\) mm  
\(163.8\) mm \(\leq x_{7}\leq 200.2\) mm  
Figure 4: Results of the static analysis of the lower rocker arm: (**a**) equivalent stress distribution; (**b**) displacement distribution.  
Figure 5: Equivalent stress distribution at several points in the arm body of the lower rocker arm.  
(2) Objective function  
Under the requirement of deformation and strength, the optimal design goal of the lower rocker arm is to make its mass as small as possible. Therefore, the objective function is the mass of the lower rocker arm, which can be expressed as follows.  
\[f(x)=M(x)=M(x_{1},x_{5},x_{6},x_{7}) \tag{7}\]  
(3) Constraints  
The material of the lower rocker arm is ZG270-500 and the safety factor is 1.4 [12]. Therefore, the allowable stress of the lower rocker arm is 192.8 MPa and the strength constraint of the lower rocker arm is:  
\[g_{1}(x)=\sigma_{\max}-[\sigma_{s}]\leq 0 \tag{8}\]  
In order to reserve optimum space for the model and to minimize the effect of deformation, the allowable displacement of the lower arm is set to no more than 1.5 mm:  
\[g_{2}(x)=\delta_{\max}-[\delta]\leq 0 \tag{9}\]  
To sum up, the mathematical model of this optimization design is:  
\[\left\{\begin{array}{l}x=(x_{1},x_{5},x_{6},x_{7})^{T}\\ f(x)=M(x)=M(x_{1},x_{5},x_{6},x_{7})\\ g_{1}(x)=\sigma_{\max}-[\sigma_{s}]\leq 0\\ g_{2}(x)=\delta_{\max}-[\delta]\leq 0\end{array}\right. \tag{10}\]  
The analysis interface of the rocker arm in this paper is shown in Figure 7.  
Figure 6: Definition of input variables: (**a**) position of the internal cavity; (**b**) internal cavity size parameters.
### Energy Scalable Pulse Cleaning Techniques
#### 1.3.3 Former Experimental Results concerning the Use of NFF  
As the first step toward improving the temporal contrast of UV systems, the nonlinear Fourier-filter was integrated into our short-pulse hybrid _KrF_ excimer laser system after the first and second passes of amplification [43]. This part of the experimental arrangement is illustrated in Figure 3. The input pulse at 248 nm was generated by frequency doubling of subpicosecond dye laser pulses [12], where the frequency converter uses the so-called active spatial filtering phenomena [17], providing temporally and spatially clean UV pulses of several times of 10 uJ energy for the first amplification pass in _KrF_. This is also part of the low NA pre-imaging, which images the input annular beam block to the plane of a secondary beam block, which acts as an input for the following NFF. Here, the second amplification pass is part of the image system of NFF, whose output is amplified in a subsequent four passes to \(\sim\)100 mJ (not indicated in Figure 3). The final power amplification stage is used in a two-beam interferometric multiplexing arrangement [44; 45], extracting a higher portion of the energy stored in the amplifier. With this arrangement, more than 2 orders of magnitude higher intensity contrast (\(>\)10\({}^{11}\)) is achieved for the same output energy as in former cases [43]. It is important to note that the ASE at the output of the system is measured to be generated exclusively by the amplifier chain following the temporal filter [43].  
Due to the direct amplification scheme in excimer amplifiers, the use of amplifier(s) following the pulse cleaning element seems possible, since deterioration of the temporal contrast in the following amplifier(s) is only originated by the development of ASE connected to the gain-length product of these amplifiers. From the point of view of contrast, this additional amplification is only tolerable as long as the amplification for the main pulse is comparable to that of the ASE. This, however, is not fulfilled in the above mentioned arrangement, because a rapid increase of the ASE compared to the short signal-pulse was experimentally observed in the relatively \"long\" amplifier chain following the temporal filter [43]. This necessitates the use of a temporal filter of high contrast improvement and high throughput as close to the end of the amplifier chain as possible. On the other hand, the origin of the moderate amplification of short pulses (compared to that of ASE) in excimer s needs further investigations.
## 4 Discussion
The example in the USA shows the difficulties of greening along water-channels or the greening after small-scale precipitation events, which leads to higher NDVI-values and influences the irrigation mapping method. In contrast to the results in the USA and Sudan, the mapping results in China are very similar at all spatial resolutions. Large-scaled fields of similar sizes and a small share of fallow fields characterize the two large agricultural areas of the scene. They indicate that the farming system follows a central management scheme resulting in a low complexity of the shape of the fields. The regular pattern of the fields, the absence of fallow fields and the large size of the fields in combination result in constant NDVI values across the different spatial resolutions and, hence, scale-independent mapping results. Besides the different behavior regarding the mapping result at a coarser spatial resolution in China compared to Sudan and the USA, the relation of the negative changes of mapped irrigated area and the LSI behaves in all three regions constantly. This means the negative areal change of irrigated area with resolution is explained by the LSI and shows that landscape metrics can also be used outside of the analysis of natural ecosystems in man-made patterns. The relations between negative areal change and LSI can be used as information about the considered region regarding an expected loss of mapped irrigated area at a coarser resolution derived by wide-swath medium-resolution satellites. A transferability is possible, since the study showed that the downscaled Sentinel-2 and original PROBA-V NDVI time series of the same spatial resolution and the same time period were practically identical despite the differences in sensor characteristics, measurement and sun angle. This demonstrates the stability of the overall approach and allows to link Sentinel-2- and PROBA-V-derived irrigation maps. The scaling relation builds a bridge between the medium-resolution sensors such as PROBA-V or the new Sentinel-3 mission and high-resolution sensors such as Sentinel-2. The presented results identify three main driving forces on the extent of the irrigated area: (a) the spatial resolution, (b) the spatial distribution of the irrigated fields in the analyzed area and (c) the complexity of the shape of the connected irrigated fields. Changes in the spatial resolution influence the mapping results differently depending on the spatial distribution and the complexity of the shape of the irrigated fields in the analyzed area. Thus, the influence of the spatial resolution on the mapping results differs from landscape to landscape. The trend towards spatially and temporally high-resolution satellite data and high-performance computing offers opportunities to rethink existing methods of irrigation mapping considering local conditions such as the spatial distribution of fields and combine crop growth model results with derived information about the development of biomass and plant conditions.