esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Profile and Overall Scheme
### Monitoring Objects and Requirements  
The monitoring objects of the railway infrastructure monitoring system mainly include bridges, tunnels, roadbeds, contact networks, and rails. Obviously, different infrastructures have different monitoring challenges and requirements. For instance, in the health monitoring of bridges, the system needs to use high-precision sensors to monitor bridge structural cracks, deformations, and other conditions. Due to the non-maintainable nature of the internal structure of bridges, this requires bridge monitoring sensors to have a longer lifespan, with maintenance intervals preferably on a monthly basis. On the other hand, due to the slow changes in bridge structures, monitoring data does not require real-time updates. Therefore, bridge monitoring sensors have lower real-time requirements. Similar to bridges, rails, roadbeds, and tunnels have similar monitoring requirements, but their maintenance is relatively easier. Therefore, the lifespan requirements for sensors for the latter three are not as strict as those for bridges. Monitoring the contact network requiresthe use of temperature or strain sensors. Since the contact network is a vital component of the railway electrification system and directly affects train safety, sensors of this kind need to upload monitoring data promptly and have higher real-time requirements.  
Based on practical project experience, this article categorizes the lifecycle requirements of monitoring equipment with maintenance cycles of one day, one week, and more than one month into three levels: low, medium, and high. Similarly, the real-time requirements of monitoring equipment with data sending frequencies higher than once every 10 min, between 10 min and 1 h, and more than 1 h are divided into three levels: high, medium, and low. Some monitoring objects and their corresponding characteristics are shown in Table 2. The real-time requirements and life cycle requirements of monitoring objects are different. We conduct a brief evaluation of their different functional requirements to provide support for the following work. The evaluation is mainly allocated based on the urgency of the data and the amount of data. For data types with high real-time requirements, we provide higher real-time evaluation to reduce delay; for periodic data with low real-time requirements, we base our calculations on the way the data are set up; the aggregation processing is designed to provide the network with a longer life cycle [26; 27; 28].
## 3 Nonlinear Dynamic Analysis of the RBRS
### Model Verification  
In order to verify the correctness of the non-linear dynamic model built in this paper, the accuracy of the model was verified by comparing it with the models built in the existing literature. In simulation, the parameters given in Table 1 were used in both models. As shown in Figure 3, the three-dimensional spectrum of the model in the Y direction of the rotor is given. Figure 3a is the three-dimensional spectrum of the model in reference [31]. Figure 3b is the calculation result of the model established in this paper. By comparison, it can be found that when the rotating speed is small, the frequency components of the two models are mainly one times frequency conversion\(f_{n1}\), and no rubbing occurs in the shafting. With the increase of rotational speed, the main components of Figure 3a are the frequency multiplication components\(f_{n1}\), \(2f_{n1}\), \(3f_{n1}\), and the frequency demultiplication component is not obvious. In addition, the amplitude of one times frequency conversion\(f_{n1}\) shows a fluctuating growth, while the amplitude of other frequency multiplication components increases first and then decreases. In Figure 3b, besides the frequency conversion and frequency multiplication components,  
Figure 2: Simulation condition schematic.  
there are also obvious frequency demultiplication components, such as \(f_{n1}/2\), \(3f_{n1}/2\), \(5f_{n1}/2\), \(7f_{n1}/2\), etc. The amplitude of each frequency component varies nonlinearly, and at a certain speed, there is an obvious continuous spectrum component in the three-dimensional spectrum. At this time, the system is in a chaotic state, but not in Figure 3a. When the speed reaches 71 rad/s, there are only obvious \(f_{n1}\) and \(2f_{n1}\) components in Figure 3a. Besides frequency multiplication components, Figure 3b has more obvious frequency demultiplication components.  
In order to compare the frequency components of the two models more clearly, Table 2 gives the comparison of frequency characteristics in three-dimensional spectrum. By comparing Figure 3 with Table 2, it can be seen that the model established in this paper takes into account the electromagnetic-rubbing coupling, non-linear electromagnetic force, hydraulic unbalance force, and other factors, which can better reflect the non-linear vibration characteristics of multi-physical field coupling.
# Preparation of Magnetic Polymers for the Elimination of 3-Isobutyl-2-methoxypyrazine from Wine
###### Abstract  
3-Isobutyl-2-methoxypyrazine (IBMP), the most prevalent grape-derived methoxyprazine, can contribute green bell pepper, vegetative and herbaceous aromas and flavours to wines. At elevated concentrations, this potent odorant may mask desirable fruity and floral aromas in wine and may be considered as a fault. A new remediation method for wines with elevated IBMP levels has been trialled using magnetic polymers, prepared in the same way as ordinary polymers but with the incorporation of iron oxide nanoparticles as magnetic substrates. Characterisation by Fourier transform-infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) showed no difference between thermally synthesised and microwave synthesised polymers. Magnetic polymers were found to have removed over 40% of the IBMP present in spiked model wine and white wine within ten minutes. The addition of magnetic nanoparticles and microwave-induced polymerisation did not affect the adsorption properties of the polymer in model wine and the polymer could be regenerated at least five times. Both Langmuir and Freundlich isotherms were found to fit the data for both types of polymer. However, attempts to produce imprinted polymers were not achieved, as they were found not to be differentiated from non-imprinted counterparts via adsorption tests.  
polymer synthesis; wine flavour; adsorption isotherm; adsorption kinetics; reusability +
Footnote †: journal: molecules
# Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration
## 3 PQ Mitigation Techniques  
### Traditional Methods  
#### 3.1.1 Harmonic Trap Filters (HTFs)  
A fundamental technique used for eliminating harmonics is the classical HTF. These filters reduce harmonics from the output waveform of the PMSGs. The HTF consists of a series RLC circuit tuned to the frequency which has an adverse effect on the operation of the WTGs.  
PMSGs consist of power electronic converters to convert the DC voltage to AC at a fixed frequency. These converters constitute six pulse rectifiers that inject high-intensity harmonics at low frequency into the grid. These harmonic injections cause further loss to the generator in the form of heat. This results in a decrease in the efficiency and lifespan of the generator. To mitigate this harmonic injection from the converters, passive filters, such as HPF are tuned to remove the characteristic harmonics introduced by the power electronic converters. The configuration of an HTF designed in [17] consists of the 5 th HTF, 7 th HTF, and damped filter. The paper demonstrated that HTF is tuned at an operating wind speed of7m/s (34.36 Hz) and 11m/s (52.8 Hz) to filter out the 5 th and 7 th dominant harmonic components. It is observed that the HTF can mitigate the harmonic components from the voltage and current waveform thereby reducing the harmonic losses in the WTG. One disadvantage of this passive filter is that it increases the stator losses, dissipates power from the generator, and affects its efficiency.
# Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence
## 3 Experiment and Comparison
### Parameter Description  
To evaluate the method designed in this paper and compare it with other works of literature, four general indexes [11], precision, recall, F1 number, and calculated frame frequency, were used to describe the detection results.  
Precision (\(P_{s}\)) indicates the ratio of the number of correctly predicted actual values to the number of all predicted actual values. For example, for remote sensing ship target detection, a higher recall ratio means that the ship target is better detected, which is defined as follows:  
\[Ps=\frac{TP}{TP+FP} \tag{13}\]  
The recall represents the ratio of correctly predicted truth values to the actual number of truth values. For remote sensing ship target detection, a higher recall rate represents the robustness of ship target detection for real targets, which is defined as follows:  
\[Rc=\frac{TP}{TP+FN} \tag{14}\]  
where _TP_ represents the number of predicted valid values and actual valid values; _FN_ represents the number of false values indicated and true values actually; _FP_ is the number of predicted valid values and actual false values.  
The \(F\)1 number is the complete result of the recall and recall index, which is defined as follows:  
\[F1=2\frac{PsRc}{Ps+Rc} \tag{15}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Type** & **Filter Number** & **Size/Stride** & **Output** \\ \hline \multirow{8}{*}{Backbone} & Input & & \(52\times 52\) & \\  & Convolutional-1 & 16 & \(3\times 3\) & \(52\times 52\times 16\) \\  & Maxpool-1 & - & \(2\times 2/2\) & \(26\times 26\times 16\) \\  & Convolutional-2 & 32 & \(3\times 3\) & \(13\times 13\times 32\) \\  & Maxpool-2 & - & \(2\times 2/1\) & \(13\times 13\times 32\) \\  & Convolutional-3 & 64 & \(3\times 3\) & \(13\times 13\times 64\) \\  & Maxpool-3 & - & \(2\times 2/1\) & \(13\times 13\times 64\) \\ \hline Prediction & Yolo-1 & & - & \\ \hline \hline \end{tabular}
\end{table}
Table 1: S-YOLO network designed in this paper for ship target slice detection and recognition.  
The calculated _FPS_ (Frames per Second) was used to represent the real-time performance of the algorithm. For example, in large-width remote sensing image detection, the image frame represented an image used for detection in practice.
## 2 Related Method of XNAV for Single Spacecraft
### Pulse Phase Estimation Method
Applying (8) to every event \(\left\{\tau_{j}\right\}_{j=1}^{N}\), we have \(\left\{\phi\big{(}\tau_{j,SSB}\big{)}\right\}_{j=1}^{N}\). Thus, we have to estimate \(\phi_{0}^{i}\) and \(F_{i}^{orbit}\) from \(\left\{\phi\big{(}\tau_{j,SSB}\big{)}\right\}_{j=1}^{N}\). Here, \(F_{i}^{orbit}\) and \(\phi_{0}^{i}\) can be estimated using the epoch folding method. The observation period is assumed to consist of \(M\) pulsar periods in the epoch folding method. The period can be divided into \(M_{b}\) equal-length bins. The length of each bin is assumed as \(t_{b}\), and \(c_{l}^{m}\) is the number of detected photons in the \(l-th\) bin of the \(m-th\) period. Therefore, the empirical profile can be recovered. Here, we define the empirical profile function at \(\varphi_{l}\) as [19] the following:  
\[\lambda_{EF}\Big{(}\varphi_{l};F_{l}^{orbit}\Big{)}=\frac{1}{Mt_{b}}\sum_{m=1}^{M }c_{l}^{m}\Big{(}\varphi_{l};F_{l}^{orbit}\Big{)}, \tag{13}\]  
where \(\varphi_{l}\) is the pulse phase at the middle of \(l-th\) bin. We adopt the Chi-squared test to calculate the statistical characteristics of the empirical profile as follows [28]:  
\[\chi^{2}=\sum_{l=1}^{M_{b}}\frac{\big{(}C_{l}-\overline{C}\big{)}^{2}}{ \overline{C}}, \tag{14}\]  
where  
\[C_{l}=\sum_{m=1}^{M}c_{l}^{m}\Big{(}\varphi_{l};F_{l}^{orbit}\Big{)}, \tag{15}\]  
\[\overline{C}=\frac{\sum_{m=1}^{M}\sum_{l=1}^{M_{b}}c_{l}^{m}\Big{(}\varphi_{l };F_{l}^{orbit}\Big{)}}{M_{b}}, \tag{16}\]  
Using Equations (14)-(16), \(\overline{v}_{1}\) can be estimated with  
\[\hat{F}_{l}^{orbit}=arg\max_{F_{l}^{orbit}}\chi^{2}. \tag{17}\]  
Inaddition, \(\phi_{i}\) can be estimated by solving a nonlinear least squares estimation problem [19]:  
\[J(\phi_{i})=\sum_{l=0}^{M_{b}}\Big{[}\lambda_{EF}(\varphi_{l};\hat{F}_{l}^{ orbit})-\lambda_{Te}(\varphi_{l})\Big{]}^{2}, \tag{18}\]  
where \(\lambda_{Te}\) is the template of the pulsar. At the same time, \(\phi_{i}\) can be estimated with  
\[\hat{\phi}_{i}=\underset{\phi_{i}\in[0,1)}{\operatorname{argmin}}J(\phi_{i}). \tag{19}\]
## 3 Results
### Fatty Acids Oxidation Study  
It has been demonstrated that the fatty acid composition of oils is a strong indicative factor that could predict the oxidative state [14; 15; 16]. Owing to this, the determination of fatty acids was selected here to study their oxidation rate.  
Derivatization was the selected methodology for the fatty acid analysis by GC, since the use of ester derivatives is recommended. The methyl ester derivatives are much more volatile than the corresponding fatty acids. Moreover, they are much less polar, avoiding peak tailing and peak asymmetry on GC [33].  
Methyl palmitate, methyl linoleic, methyl oleate, and methyl stearate were the esters determined in this work, since palmitic acid, linoleic acid, oleic acid, and stearic acid are the most abundant fatty acids in hazelnuts and walnuts [34].  
Table 1 shows the amount of these fatty acids expressed as ng/g hazelnuts and as ng/g walnuts in the bags made with laminate A (made with adhesive alone) and laminate B (made with adhesive + 10% of nanoSe). Fatty acid values were around 50% higher in the bags made with laminate B, which contained nanoSe at both day 21 and day 42, respectively. The highest differences were found for walnuts. An improvement on the amount of fatty acids from 50 to 66% at day 42 was found in the active bag. It can be seen that 11.3 ng/g of methyl oleate were found in the bag made with laminate B and 3.88 ng/g were found in the bag made with laminate A. In the case of hazelnuts, the differences on the fatty acid amount between the active bag and the blank ranged from 24 to 53% at day 42. Therefore, these results demonstrate the antioxidant effect on food and the consequent shelf life extension of the packaged nuts, since pristine fatty acids remain for much more time in nuts.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Hazelnuts**} & \multicolumn{2}{c}{**Walnuts**} \\ \hline  & **Laminate A** & **Laminate B** & **Laminate A** & **Laminate B** \\ \hline
**Methyl Palmitate** & & & & \\ \hline Day 0 & 28.6 \(\pm\) 3.4 & 28.6 \(\pm\) 3.3 & 38.89 \(\pm\) 3.0 & 38.89 \(\pm\) 4.1 \\ Day 21 & 8.04 \(\pm\) 0.90 & 16.4 \(\pm\) 1.51 & 12.1 \(\pm\) 1.6 & 21
## 2 Numerical Methods
### Dispersion Model for Leaked Liquid Hydrogen
#### 2.2.3 Hybrid Model and Atomospheric Stability  
We further compared the predictive performance of the pseudo-source model and the pool model for liquid hydrogen leaks by varying the fraction parameter \(\chi\). \(\chi\) represents the ratio of utilizing the pseudo-source model to the liquid pool model based on the leaked mass flow rate. Specifically, \(\chi=1.0\) indicates that the pseudo-source model is applied to the entire mass flow rate of leaked liquid hydrogen, while \(\chi=0.0\) implies the use of the liquid pool model for the entire mass flow rate. Likewise, \(\chi=0.6\) signifies that the pseudo-source model was applied to only 60% of the mass flow rate of leaked liquid hydrogen. When applying the pure pseudo-source model (\(\chi=1.0\)) for liquid hydrogen leakage, the temperature of released hydrogen was set to 20.5 K slightly above the boiling point of hydrogen.  
In this study, we opted for 60% utilization of the pseudo-source model (\(\chi=0.6\)), considering previous research [20; 21] that achieved reasonable results using the pseudo-source model to simulate the dispersion resulting from liquid hydrogen leaks. The equations and numerical procedures for these models can be found in the referenced publications [19; 25].  
Gas dispersion into the atmosphere is substantially affected by turbulence. Pasquill identified atmospheric stabilities ranging from Class A (very unstable) to Class F (stable); stability is affected by wind speed, cloud cover, and solar radiation [26]. This classification is now standard when assessing material dispersions [27]. Table 2 lists the Pasquill stability classes according to weather conditions. Both the computational and experimental wind speeds for cases 1 and 2 were 3.2 and 5.8 m/s, respectively, as shown in Table 1. As a result, we applied the Pasquill Class C and D criteria (Day, moderate sun), respectively, to represent the stability conditions in the FLACS computations, taking into account the weather conditions on the experimental days.
## 2 System Modeling
### Control Algorithm  
Considering that the magnitude of the clutch transmitted torque \(\overline{T_{c}}(t)\) can be physically controlled, it is defined as the control input, as shown in Figure 2. Advanced controllers for clutch engagement comprise feed-forward and feedback blocks. Most of the feedback blocks have a PID-like form, and the gains are calculated by the advanced controllers [27; 28]. Without loss of generality, a control algorithm consisting of a feed-forward block and a PI feedback loop was modeled in this study. The engine output torque \(T_{e}\), which can be obtained from the engine control unit, is regarded as the feed-forward part of the control input. The deviation from the reference slipping speed of the clutch is considered as the feedback signal. The control algorithm is mathematically expressed as  
\[\overline{T_{c}}(t)=T_{e}(t)+k_{p}e(t)+k_{i}\!\!\int e(t) \tag{13}\]  
where  
\[e(t)=\Delta\dot{\theta}_{c}(t)-\Delta\dot{\theta}_{c}^{*}(t) \tag{14}\]
## Materials and Methods  
### Materials and Reagents  
Fresh porcine livers were provided by a local meat market (Carnicas M. Boo, San Cibrao das Vinas, Ourense). The 2,2\({}^{\prime}\)-Azobis (2-methylpropionamidine) dihydrochloride (AAPH) 98% and (\(\pm\))-6-hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid 97% (Trolox) were acquired from Acros organics (New Jersey, USA). L-(+)-ascorbic acid 98+%, 2,4,6-Tri(2-pyridyl)-1,3,5-triazine (TPTZ) 98%, and 2,2\({}^{\prime}\)-azino-bis (3-ethylbenzothazoline-6-sulphonic acid) diammonium salt (ABTS) 98% were purchased from Alfa Aesar (Kandel, Germany). Gallic acid monohydrate extra pure, di-potassium hydrogen phosphate anhydrous extra pure, potassium dihydrogen phosphate reagent grade, sodium hydroxide 32%, and hydrochloric acid 37% extra pure were supplied by Scharlau S.L. (Barcelona, Spain). Potassium peroxodisulphate and sodium acetate were dispensed by VWR Chemicals (Leuven, Belgium), iron (III) chloride hexahydrate and iron (II) sulphate heptahydrate were from VWR Chemicals (Germany), and Folin-Ciocalteu reagent was from VWR chemicals (France). The 2,2-diphenyl-1-picrylhydrazyl (DPPH) and nisin from _Lactococcus lactis_ were procured from Sigma Aldrich (Germany), methanol was from Chem-Lab NV (Zedelgem, Belgium), gentamicin sulfate was from Biowest (France), and fluorescein sodium was from Fisher (UK). Papain 6000 USP, Bromelain 2000 U/g, and bioprotease LA 660 (alcalase) were supplied by Biocon (Spain). _Brochothrix thermosphacta_ (CECT 847) was distributed by CECT (Spain). _Listeria monocytogenes_ (MKTD 11994/WDCM 00019), _Staphylococcus aureus_ (MKTD 799/WDCM 00032), _Salmonella enterica_ (MKTD 17058), _Pseudomonas aeruginosa_ (MKTD 1128/WDCM 00026), and _Escherichia coli_ (DSMZ 1103/WDCM 00013) were from Microkit (Spain). Amicon stirred cells (400 mL, membrane diameter 76 mm) were purchased from Millipore (Germany), and 5, 10, and 30 kDa molecular weight cut-off (MWCO) (76 mm) membranes were from Millipore (Jaffrey, NH, USA). Cellulose acetate filters with pore size of 0.22 um were supplied by Filter Lab (Spain).
## 4 Policy Optimization  
Whenever an ASC finishes its current job, it refers to the crane dispatching policy to select its next job. In preparation for this selection, a set of candidate jobs that should (or can) be done within a fixed-length horizon are identified, as explained in detail in Section 4.1. If the candidate job is one of the retrieval jobs, the job is already well-defined as its pickup location (where the container is currently stacked), and its drop-off location is one of the HPs where the requesting vehicle is waiting. If not, the job must be defined by determining the stacking position for the corresponding container. In this paper, we used the stacking policy developed by [5] to determine the stacking position of a container, whether it is the one newly coming into the block or the one relocated within the block for rehandling, repositioning, or remarshaling. Similar to our crane dispatching policy, the stacking policy also uses a multi-criteria scoring function to evaluate candidate stacking positions and selects the best one. It is after the stacking positions are all determined that the crane dispatching policy evaluates each candidate job and assigns the best job to the waiting ASC. After each dispatching of ASC, the horizon and the corresponding set of candidate jobs are updated, and the stacking policy followed by the dispatching policy is referenced again. Figure 4 provides the pseudocode for the overall procedure of ASC operations at a block. Below, we first describe how we uses a multi-criteria scoring function to create the crane dispatching policy. In the subsections that follow, we explain how the policy can be optimized by using our noisy optimization technique.  
**procedure** ASC Operation at a Block  
**persistent**: \(h\), a fixed-length horizon for crane scheduling  
**repeat**  
Wait until an ASC finishes its current job  
\(asc\leftarrow\) ASC that just finished its job  
Update \(h\)  
\(J\leftarrow\) set of candidate jobs for \(asc\) within \(h\)  
**for each**\(j\) in \(J\) except the retrieval jobs **do**  
\(c\leftarrow\) container pertaining to job \(j\)  
\(L\leftarrow\) set of candidate stacking positions for container \(c\)  
\(j\_stacking\_position\leftarrow\pi_{s}(c,\,L)\) /* apply stacking policy \(\pi_{s}\)*/  
\(asc\_job\leftarrow\pi_{d}(J)\) /* apply dispatching policy \(\pi_{d}\)*/  
**until** there remains no job  
Figure 4: Procedure of ASC operations at a yard block using the stacking and dispatching policies.
## 2 System Architecture
### Information Management System
#### 2.2.1 Filter and Queue Module  
The Filter and Queue module accomplishes action prioritization and scheduling. The core of this module is built by a rules-based strategy, which is responsible for the manipulation of the flow of the messages. Application request(s) (messages, notifications, etc. to the driver) are filtered dynamically due to their base priority. The filtering is based on a truth table that describes a strategy, defined according to safety relevance criteria, of prioritization and scheduling of application requests to be forwarded to the driver on the basis of current DVE state [37]. It is precisely the dependency by actual DVE state that makes the filtering a dynamic process. This module includes the rules to enable or postpone the presentation of the incoming actions: negative outcome of the IMS means that the action will be queued; in contrast, with a positive outcome, we proceed to the Modality and Channel module.