esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experiments and Results
### Experimental Instances and Processing Time Variability  
To validate the effectiveness of the proposed method, the classic benchmark instances MK01-10 [38] are extended to a two-factory environment. The scale of MK01-10 is shown in Table 2. In this study, processing times are assumed to follow a normal distribution, where the theoretical processing time \(p_{ij}^{lk}\) provided in the benchmark instance is treated as the mean processing time \(\mu_{ij}^{lk}\) for each operation, and the variance is defined as a fraction \(\kappa\) of the mean. Different variances will lead to fluctuations of varying degrees. To maintain a manageable level of problem complexity while reflecting real-world characteristics, the \(\kappa\)-value corresponding to \"low variability\" defined in [21] is selected--specifically \(\kappa=0.1\)  
Figure 6: Illustration of the crossover for OS and MS.  
Figure 7: Illustration of the mutation for OS and MS.  
Although the variability of processing times remains consistent across all instances, the theoretical processing times differ between instances. Different theoretical processing times lead to varying fluctuation amplitudes because the variance is calculated as a proportion of the theoretical time. As a result, the actual fluctuation amplitude of processing times differs across instances, which directly affects the choice of scheduling strategy. For example, operations with larger fluctuations in processing times require more flexible scheduling strategies to accommodate the larger impact, while operations with smaller fluctuations can be handled with simpler scheduling approaches. The relevant scheduling strategies and details are discussed in Section 5.1 of the paper.  
Furthermore, to ensure fair comparisons among all algorithms, a unified termination criterion is adopted by setting the maximum number of function evaluations as \(600\times\sum\limits_{i=1}^{n}n_{i}\). The code and the data can be downloaded from: [[https://github.com/[PERSON]/code-and-data-for-DDFJSP-VPT](https://github.com/[PERSON]/code-and-data-for-DDFJSP-VPT)]([https://github.com/[PERSON]/code-and-data-for-DDFJSP-VPT](https://github.com/[PERSON]/code-and-data-for-DDFJSP-VPT)) (accessed on 3 February 2025).
## 2 Related Works
### Overview  
A method to provide users with NFoVs containing objects in the 360-degree video during the flight of a micro UAS equipped with an omnidirectional camera is shown in Figure 1. In this paper, the 'objects' are those that the user observes using the 360-degree video. A ground control station (GCS) transmits control signals to the omnidirectional camera-equipped micro UAS to make it fly autonomously.  
The 360-degree video captured and stored every set time during the \(i\)th flight of the micro UAS is defined as Video \(V_{i}\), with \(V_{i}\) = [\(v_{i,1}\), \(v_{i,2}\),..., \(v_{i,t}\),... ]. The user can watch the Video \(V_{i}\) recorded through the micro UAS's omnidirectional camera during in \(i\)th flight. The 360-degree video captured at time \(t\) is defined by Image \(v_{i,t}\). Image \(v_{i,t}\) comprises all surroundings of the micro UAS through a omnidirectional camera mounted on the micro UAS at time \(t\).  
The 360-degree video taken from the micro UAS's omnidirectional camera is delivered to the GCS, which calculates video control signals to control the 360-degree videos for the NFoVs with the objects using the received Video \(V_{i}\). Video Control Signal Set \(S_{i}\) = [\(s_{i,1}\), \(s_{i,2}\),..., \(s_{i,t}\),... ] is defined as a video control signal for calculating the NFoV of the objects using Video \(V_{i}\). The video control signal for controlling Image \(v_{i,t}\) at time \(t\) is defined as Video Control Signal \(s_{i,t}\).  
The data collection stage allows users to view the 360-degree video and collect video control signals that maneuver it in the direction of the objects. The 360-degree video control data are devised from the collected 360-degree videos and video control signal sets. Preprocessed 360-degree video control data are constructed to be used to train deep learning models, which is accomplished in the model training stage.  
In the model training stage, by inferring the 360-degree video input and video control signal onto it, the deep learning model shows improvement over the video control signal of the preprocessed 360-degree video control data.  
In the video control stage, the NFoVs are provided to the user using the trained deep learning model. In the GCS, Video \(V_{i}\) and Video Control Signal \(S_{i}\) are delivered to the user. The NFoVs calculated by 360-degree images and video control signals are defined as NFoV N\({}_{i}\), described by [\(n_{i,1}\), \(n_{i,2}\),..., \(n_{i,t}\),... ]. NFoV \(n_{i,t}\) is a view provided to the user as a result of controlling the 360-degree image depending on the video control signal; it is calculated using Video \(V_{i,t}\) and Video Control Signal \(s_{i,t}\). NFoV \(n_{i,t}\) contains objects for observation; users can use either a head-mounted display or a 360-degree video play application to identify NFoVs with objects.
# A Review on the Performance of Concrete Containing Non-Potable Water
## 5 Hardened Properties
### Water Absorption Capacity  
Water absorption (WA) represents the permeable porosity of concrete. According to different studies [13; 19; 23; 31; 32; 34] that used different incorporation ratios (25%, 50% and 100%) of treated water, there is no big difference between the values of tap water concrete and treated WW concrete (Figure 17). As treated WW has fewer impurities and organic matter, it may not be harmful to the imperviousness of concrete.  
Other studies [10; 16; 24] used the same incorporation ratios of untreated water and showed that the water absorption of concrete increased up to 24% (Figure 17). In addition, in some cases, the water absorption of concrete decreased with the use of some specific WW. This can be related to the fact that the chloride quantity in WW was lower than that in potable water [47]. In addition, other studies reported that domestic sewerage WW contains a large amount of organic WW that causes the formation of a large number of small pores leading to increased water absorption [10]. These differences between types of WW can be clearly seen when the water absorption and strength of WW concrete are compared (Figure 18). The results show that there is a poor relationship between the two properties because some of the chemical properties may not directly affect some of the hydration products (e.g., chloride content), while they may affect the porosity of concrete due to the gas generated out of them during the early reaction of cement compounds with them.
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 4 Analysis of ACF and PSD
### Modulation Parameter Analysis
#### 4.1.3 Influence of Subcarrier Number \(K\) on Navigation Characteristics  
The experimental conditions are as follows: the number of subcarriers \(K\) is 128, subcarrier interval is 1 MHz, useful time of one symbol of OFDM is \(T_{u}=1/\Delta f\), and time domain sampling interval is \(\Delta t=1/N\Delta f\). Figure 7 shows the ACF and PSD of the OFDM navigation signals in the zero-padding and cyclic prefix modes. It can be seen from Figure 7a that the main peaks of the ACF in the zero-padding and cyclic prefix modes are basically the same when other conditions are unchanged. However, there is a secondary peak generated in the ACF of cyclic prefix mode near the abscissa of 1.5 \(\upmu\)s. The zero-padding mode has no effect on the ACF. It can be found from Figure 7a that the PSD main lobe widths of the OFDM navigation signal in the cyclic prefix and zero-padding modes are basically the same, but the PSD in the cyclic prefix mode is slightly lower than that of the zero-padding mode (by about 1 dB).  
Figure 6: The PSD with different subcarrier intervals.  
Figure 7: Effect of guard interval type on the ACF and PSD. (**a**) The ACF of the OFDM navigation signals with different guard interval types; (**b**) The PSD of the OFDM navigation signals with different guard intervals.
## 4 Results and Discussion
### Test of the Combined Ag/AgCl and Zn Pseudo-Membranes
#### 4.4.1 Preparation of the Ag/AgCl Electrodes  
A bath of 300 g NaCl and 250 mL concentrated HCl in 10 L demi water was used, and a current of 0.2 A (4.8 A/m\({}^{2}\)) was applied for 30 min. Maximum voltage was set to 0.39 V. Figure 12 shows the U-t curves and Table 11 summarizes the properties of the regression lines of the first part below 0.39 V. The slope of the left part is comparable with the slope in former experiments with larger current density. The applied charge was estimated to be 4718 C/m\({}^{2}\) or 0.049 mol/m\({}^{2}\), a factor of 20 lower than those in the former experiments. The expected resistance is also the same factor less; we estimated about 23.5 \(\Omega\cdot\)cm\({}^{2}\).  
Remarkable is the low start voltage (0.120 V), much lower than the Nernst voltage of the cell (about 0.221 V). We expected a higher start voltage, composed of different effects on Ag/AgCl and the Ti-MMO electrodes. These include Nernst potential, overvoltage, effects of concentration polarization, and ohmic voltage drop in the solution.
# Dynamical Effects of the Increase of the Axle Load on European Freight Railway Vehicles
## 2 Vehicle Model
### Model Architecture
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Body** & **Mass** & **Ixx** & **Iyy** & **Izz** \\  & **[kg]** & **[kg m\({}^{2}\)]** & **[kg m\({}^{2}\)]** & **[kg m\({}^{2}\)]** \\ \hline Axle-box (22.5 ton/axle) \({}^{1}\) & 170 & 1.48 & 6.58 & 5.23 \\ Axle-box (25 ton/axle) & 176 & 1.53 & 6.81 & 5.41 \\ Axle-box (27.5 ton/axle) & 182 & 1.59 & 7.05 & 5.61 \\ Axle-box (30 ton/axle) & 187 & 1.64 & 7.30 & 5.80 \\ Axle-box (32.5 ton/axle) & 192 & 1.70 & 7.56 & 6.01 \\ Axle-box (35 ton/axle) \({}^{1}\) & 197 & 1.76 & 7.83 & 6.22 \\ Bogie frame (22.5 ton/axle) \({}^{1}\) & 1450 & 1103 & 715 & 1783 \\ Bogie frame (25 ton/axle) & 1502 & 1143 & 741 & 1847 \\ Bogie frame (27.5 ton/axle) & 1550 & 1184 & 767 & 1913 \\ Bogie frame (30 ton/axle) & 1596 & 1226 & 795 & 1982 \\ Bogie frame (32.5 ton/axle) & 1639 & 1270 & 823 & 2053 \\ Bogie frame (35 ton/axle) \({}^{1}\) & 1680 & 1315 & 853 & 2126 \\ Wheelset (22.5 ton/axle) \({}^{1}\) & 1080 & 903 & 119 & 903 \\ Wheelset (25 ton/axle) & 1119 & 936 & 123 & 936 \\ Wheelset (27.5 ton/axle) & 1154 & 969 & 128 & 969 \\ Wheelset (30 ton/axle) & 1189 & 1004 & 132 & 1004 \\ Wheelset (32.5 ton/axle) & 1221 & 1041 & 137 & 1041 \\ Wheelset (35 ton/axle) & 1251 & 1078 & 142 & 1078 \\ \hline \hline \end{tabular} \({}^{1}\) Reference Vehicle.  
\end{table} 
Table 1: _Cont._  
Figure 1: Multibody model of the bogie assembled on the track.
# Performance Analysis of Undifferenced NRTK Considering Time-Varying Characteristics of Atmosphere
## 2 Positioning Model of BDS Undifferenced NRTK
### Generation of UD Error Correction Information for Single Reference Station  
When the coordinates of the reference station are known, omitting the epochs index, the observed minus computed (OMC) can be written as:  
\[E\left\{\widetilde{L}_{r,j}^{s}\right\}=T_{r}^{s}+dt_{r}+\lambda_{j}\delta_{r, j}-dt^{s}-\lambda_{j}\beta_{j}^{s}-\mu_{j}\cdot t_{r}^{s}+\lambda_{j}N_{r,j}^{s} \tag{3}\]  
\[E\left\{\widetilde{P}_{r,j}^{s}\right\}=T_{r}^{s}+dt_{r}+d_{r,j}-dt^{s}-d_{j} ^{s}+\mu_{j}\cdot t_{r}^{s} \tag{4}\]  
where \(\widetilde{L}_{r,j}^{s}\), \(\widetilde{P}_{r,j}^{s}\) represent the OMC of the pseudo-range and carrier phase observations, \(\widetilde{L}_{r,j}^{s}=L_{r,j}^{s}-\rho_{r}^{s}\), \(\widetilde{P}_{r,j}^{s}=P_{r,j}^{s}-\rho_{r}^{s}\), other symbols have the same meaning as in Equations (1) and (2); the OMC of the observations can be directly obtained through the observation equation, and it includes the clock errors of satellite and receiver, hardware delay errors, and atmospheric delay errors such as tropospheric error and ionospheric error. Unlike pseudo-range OMC, the carrier phase OMC also includes integer ambiguity parameters in addition to error correction value. At this time, the various errors contained in OMC can be directly used to generate OSR error correction information. The error correction information contained in OSR can be expressed as:  
\[OSR\left\{L_{r,j}^{s}\right\} =E\left\{\widetilde{L}_{r,j}^{s}\right\} \tag{5}\] \[=T_{r}^{s}+dt_{r}+\lambda_{j}\delta_{r,j}-dt^{s}-\lambda_{j} \beta_{j}^{s}-\mu_{j}\cdot t_{r}^{s}+\lambda_{j}\cdot N_{r,j}^{s}\] \[OSR\left\{P_{r,j}^{s}\right\} =E\left\{\widetilde{P}_{r,j}^{s}\right\}\] (6) \[=T_{r}^{s}+dt_{r}+d_{r,j}-dt^{s}-d_{j}^{s}+\mu_{j}\cdot t_{r}^{s}\]From Equations (5) and (6), UD pseudo-range OSR is directly expressed as OMC, and the carrier phase OSR includes the integer ambiguity, which only affects the integer ambiguity value of the user and does not affect the user positioning results. Unlike SSR error correction information, OSR error correction information does not separate various errors, but instead broadcasts them as a whole to directly eliminate the observation errors of users. The UD OSR error correction information includes atmospheric errors, clock errors, and hardware delay errors. The phase hardware delay errors affect the integer characteristics of ambiguity, while atmospheric errors affect the convergence speed of ambiguity.
## 1 Introduction
### Problem Definition and Contribution  
In Ecuador, the use of intelligent sensors at the agricultural level is very limited. Currently, there are sensors that are used for census, monitoring and control of multiple variables [3]. The use of these wireless sensors allows for efficient energy management. They are scalable. New sensors can be incorporated without affecting performance, achieving the implementation of dynamic network topologies [4].  
The contribution of this work is to reduce the technological gap in rural communities in Ecuador, which do not have many economic resources, and with this network, farmers will be able to control production and pests, based on the climatic changes present in the area.  
For the coverage area where the network is implemented, the applied system offers scalability, security, management and an affordable cost for the community, whose economic income depends mostly on agricultural activities. In this context, the system will serve as a basis for the implementation of techniques such as phytosanitary control, intelligent irrigation, and production forecasting among others.
## 4 Multi Objective Model of Optimal PMU Placement  
Considering the cost and accuracy of state estimation, a new multi-objective model of optimal PMU placement is presented. The objective functions are to minimize the number of PMUs and minimize the error of state estimation simultaneously, and the constraint is to ensure the distribution network is observable. On the basis of minimizing the number of PMUs required and the state estimation error, the solution with the largest measurement redundancy is obtained by modifying the objective function, so as to deal with the problem of data loss caused by communication problems.  
In addition, SCADA measurements are also applied to reduce the number of PMUs required for the full observability.
## 2 Theoretical Background
### Cumulative Fatigue Damage Theory  
Cumulative fatigue damage is a material mechanical property deterioration process caused by repeated cyclic stress. Fatigue failure occurs when damage accumulates to a critical value. The accurate description of the material fatigue cumulative damage development process under cyclic loading is the basis of material fatigue life estimations and rational structure fatigue design. The default S-N curve data are measured under the same stress amplitude when using the S-N curve for fatigue life evaluation, as shown in Figure 2. The stress applied to a structure is complex and changeable in practical engineering problems, so the fatigue life cannot be obtained directly using the S-N curve. [PERSON]'s linear damage accumulation theory solves this problem well and plays a role as a bridge. [PERSON]'s linear fatigue cumulative damage theory considers fatigue failure an irreversible deterioration process of materials under cyclic stress. Each internal change in the structure itself is related to the external loading mode in this state.  
\[\frac{n}{N}=\left\{\begin{array}{ll}2\times 10^{6}\left(\frac{\Delta\sigma_{z}}{ \Delta\sigma_{z}}\times\frac{1}{\tau_{TF}\times\gamma_{mf}}\right)^{m_{1}}& \left(10^{5}\leq N\leq 5\times 10^{6}\right)\\ 5\times 10^{6}\left(\frac{\Delta\sigma_{z}}{\Delta\sigma_{z}}\times\frac{1}{ \tau_{TF}\times\gamma_{mf}}\right)^{m_{2}}\left(\frac{2}{5}\right)^{\frac{m_{2 }}{m_{1}}}&\left(5\times 10^{6}\leq N\leq 10^{8}\right)\end{array}\right. \tag{14}\]  
The life prediction algorithm in the EN1999 standard is based on Palmgren-Miner cumulative damage theory, which calculates the damage generated by each load case under fatigue load conditions and accumulates the damage generated by all load cases to obtain the total damage. The calculation formula is shown in Equations (15) and (16):  
\[\frac{n_{i}}{N_{i}}=\left(\frac{\sigma}{\sigma_{-1}}\right)^{m} \tag{15}\]  
\[D=\sum\left(\frac{n_{i}}{N_{i}}\right)=\frac{n_{1}}{N_{1}}+\frac{n_{2}}{N_{2} }+\cdots+\frac{n_{n}}{N_{n}} \tag{16}\]
# CFD Simulation of the Safety of Unmanned Ship Berthing under the Influence of Various Factors
## 3 Multi-Factor Berthing Safety Study on an Actual Unmanned Ship
### Analysis of the Berthing Characteristics of Actual Unmanned Ship
#### 3.2.1 Flow Characteristic Analysis  
The initial waterline of the unmanned ship is 0.53 m below deck, and the speed distribution and air-water volume fraction at different speeds are obtained by simulation, as shown in Figure 8.  
As can be seen from Figure 8, color and length represent the flow speed here, the more the color is biased to red, the longer the length indicates the greater the speed, the direction of the vector arrow represents the flow direction of the flow field media here. The water flows smoothly in the front of the unmanned ship, and waves are generated at the stern. The water level near the stern is low. Far from the stern, the water level first rises and then drops, and finally stabilizes. This is because the process of unmanned ships sailing on the water will increase the pressure of the unmanned ship's bottom water. The high-pressure water will flow to the low-pressure area when passing through the surface of the unmanned ship. Therefore, the wake part of the unmanned ship will be a part of the water flow that is raised. At the same time, while moving, the unmanned ship will create a certain degree  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Number of Grids** & **Resistance/N** \\ \hline Scheme 1 & 552,273 & 3666 \\ Scheme 2 & 456,914 & 3796 \\ Scheme 3 & 588,330 & 3828 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Resistance under different grid schemes.  
Figure 7: Computational domain and grids.  
of vacuum at the tail, and the pressure value will be relatively small in a small area close to the tail. Since the viscosity of water is much greater than that of air, the tail part of the unmanned ship will be occupied by air first, and the wake of the unmanned ship will be convex. When the speed is less than or equal to 12 m/s, the speed of the first part of the ship increases as the air bypasses the surface of the circular fence, and the air flow rate on the deck becomes smaller and a vortex is formed due to the influence of structures such as the console on the deck. When the speed is too high, there will be fluctuations, causing changes in pressure and viscosity. When the speed is greater than 16 m/s, a vortex is formed on the surface of the tail of an unmanned ship, where the pressure is small and the speed is greater, such as at 20 m/s, the maximum speed at the tail vortex is 59.3 m/s. This will seriously affect the berthing safety of unmanned ship.
# Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography
## 4 Results
### Trend and Correlation Analysis of LULC and Ambient Temperature
the strongest correlation with ambient temperature, reinforcing the well-documented urban heat island effect, where artificial surfaces retain and emit heat more intensely than natural landscapes. Urbanization and infrastructural development played a crucial role in modifying the local temperature patterns. In contrast, water bodies showed a weaker correlation with ambient temperature, which aligns with their inherent ability to moderate temperature fluctuations through evaporative cooling and thermal inertia. Furthermore, grasslands and bare soil exhibited moderate correlations, indicating their intermediary role in surface heat absorption and emission dynamics. Building upon these findings, a follow-up analysis examined the correlation between the mean temperature of LULC classes and ambient temperature, revealing a strong positive correlation coefficient of approximately 0.827. The results confirm that as ambient temperature increases, the overall mean temperature across different LULC classes also tends to significantly increase.
# The Use of Biological Sensors and Instrumental Analysis to Discriminate COVID-19 Odor Signatures
## 2 Materials and Methods
### HS-SPME-GC-MS Methods
#### 2.3.2 HS-SPME-GC-MS Analysis Procedure  
Following sample irradiation, T-shirt samples were prepared for reagent-free sampling by removing a 4 \(\times\) 4 inch piece of fabric from the underarm section using sterilized scissors. This piece was then placed inside a 40 mL headspace vial, which was pre-cleaned according to the methods published by [PERSON] et al. (2022) [40]. Samples were stored at room temperature (20 \({}^{\circ}\)C) between sample collection and analysis.  
Samples were placed in a digital heating bath set at 50 \({}^{\circ}\)C immediately prior to sample extraction. A clean 50/30 \(\upmu\)m divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) SPME fiber was exposed to the headspace of the T-shirt samples at a 1-inch fiber exposure setting. No previous sample equilibration was performed. After 24 h, SPME fibers were unexposed and removed from the sample headspace.  
Analytes on the SPME fibers were thermally desorbed at 270 \({}^{\circ}\)C for 4 min (3-inch fiber height) into the heated inlet of the GC (Agilent 8890; Agilent Technologies, Santa Clara, CA, USA). A splitless injection method with a 1 mL/min column flow was implemented on a HP5-MS UI capillary column (15 m \(\times\) 0.25 mm I.D. \(\times\) 0.25 \(\upmu\)m phase thickness; Agilent Technologies). Helium was used as the carrier gas. Over temperature parameters started at 40 \({}^{\circ}\)C (1.25 min hold), increased to 165 \({}^{\circ}\)C (5 \({}^{\circ}\)C/min rate), and concluded at 270 \({}^{\circ}\)C (30 \({}^{\circ}\)C/min rate). The total method runtime was 29.75 min. A mass spectrometer (Agilent 5977B MSD; Agilent Technologies) with an electron impact ionization (EI) source and quadrupole mass analyzer was used with the following parameters: MS source was maintained at 230 \({}^{\circ}\)C, MS Quad at 150 \({}^{\circ}\)C, transfer line at 280 \({}^{\circ}\)C, EI source at 70 eV, and scan range at m/z 50-550.
## 2 Communication System Model and Theory  
In this section, we provide an explanation and theoretical derivation of the communication system composed of the previously proposed transmitter and receiver, as well as the Doppler estimation process involved in signal processing.