esa-sceva/satcom-chunk-collection huggingface dataset:
# A Parallel-Phase Demodulation-Based Distance-Measurement Method Using Dual-Frequency Modulation
## 1 Introduction
Consequently, the previous methods require sequential integration of phase samples, which results in processing time being required for the integration of \(N\) phase samples per one-point distance measurement [12]. In most cases, four phase samples are used: 4-bucket algorithm [12; 13; 14; 15; 16]. Reducing the total processing time of the phase demodulation per pixel can improve the performance of distance measurement effectively with regard to motion blur suppression or accuracy improvement, especially in case of raster scanning type ToF sensors. With that knowledge, this paper contributes to decreasing the total processing time required to obtain a distance value using a novel parallel phase demodulation method. Compared to conventional ToF methods which calculate phase samples sequentially, in this paper, all phase samples are demodulated in parallel for one-point distance measurements. Although this method requires fast digitization of received light signal, this method needs only a single integration time for acquisition of samples. In addition to decreasing the processing time, the \(2\pi\) ambiguity problem, which is the main cause of limitation of maximum measurable range, is solved by adopting dual-frequency modulation. By combining parallel phase demodulation and dual-frequency modulation, not only is the maximum range increased, but the accuracy at all distances is improved as well. The remainder of this paper proceeds as follows: Section 2 explains the principles of the AMCW method. Section 3 mentions the \(2\pi\) ambiguity problem and previous solutions [22; 24; 25]. Section 4 deals with details of the proposed method. Sections 5 and 6 show the experimental setup, results, and discussion. In Section 7, conclusions are presented.
## 3 Overvoltage Simulation Model Including Variable Capacitance
### Arc Models  
The Mayr and Cassie arc models, based on the principle of energy conservation, are often employed for simulating arc process of power system.  
1. Cassie Model  
The Cassie arc model establishes the relationship between arc current and voltage. The equation for the Cassie arc model is  
\[\frac{1}{R_{\rm c}}\frac{dR_{\rm c}}{dt}=\frac{1}{\tau_{\rm c}}(1-\frac{u^{2} }{u_{\rm c}^{2}}) \tag{1}\]  
where \(R_{\rm c}\) is the dynamic resistance, \(\tau_{\rm c}\) is the arc time constant, \(u_{\rm c}\) is the arc voltage constant, and \(u\) is the arc voltage. The model indicates that the arc conductivity changes with the arc current. It is suitable for when there is a high current before current zero crossing.  
2. Mayr Model  
The Mayr arc model considers the thermal equilibrium of the arc conductor and gas dynamic characteristics. The equation for the Mayr arc model is  
\[\frac{1}{R_{\rm m}}\frac{dR_{\rm m}}{dt}=\frac{1}{\tau_{\rm m}}(1-\frac{Ei}{P _{\rm loss}}) \tag{2}\]  
where \(R_{\rm m}\) is the dynamic resistance, \(\tau_{\rm m}\) is the arc time constant, \(P_{\rm loss}\) is the dissipative power during arc burning, \(E\) is the electric field strength in the arc column, and \(i\) is the arc current. Equation (2) illustrates the relationship between arc conductivity and arc dissipation power.  
Figure 3: Arc reignition and extinguishing. (**a**) Reignition. (**b**) Extinguishing.  
According to Sections 2.1 and 2.2, the arc extinction and reignition processes, as well as the impact of circuit voltage and current on arc resistance, should be taken into account in the pantograph dropping simulation. The Mayr model is suitable for when there is a small current around the current crossing zero, consistent with the characteristics of the pantograph dropping arc. Additionally, the model can describe the relationship between the dissipated power and the arc resistance during arc burning. In summary, this paper chooses the Mayr arc combined with pantograph-catenary variable capacitance as the simulation model to research the operating overvoltage of a dropping pantograph.
# Plant Organic Acids as Natural Inhibitors of Foodborne Pathogens
###### Abstract  
Background: Foodborne infections affect approximately 600 million people annually. Simultaneously, many plants contain substances like organic acids, which have antimicrobial activity. The aim of this study was to examine the effects of 21 organic acids, naturally occurring in plants, on four foodborne bacteria (_Staphylococcus aureus_, _Listeria monocytogenes_, _Escherichia coli_, and _Salmonella enterica_ Typhimurium  
cattle and pig feces. Additionally, _Bacillus, Listeria_, and _Salmonella_ were also found in chicken feces. In cattle feces, _Bacillus, Campylobacter_, and _Vibrio_ bacteria were detected. Furthermore, in cattle, chicken, and pig feces, other genera potentially pathogenic for humans, such as _Corynebacterium, Streptococcus, Neisseria, Helicobacter, Enterobacter, Klebsiella, and Pseudomonas_, were also identified [3].  
Among fungi, there are many foodborne pathogens, including _Paecilomyces_ spp., _Xerochrysium_ spp., _Aspergillus_ spp., _Fusarium_ spp., _Penicillium_ spp., or _Alternaria_ spp. [4]. Most fungi cause poisoning through the production of mycotoxins. Infections are less common, such as invasive infections in immunocompromised individuals. These can be caused by, among others, _Absidia corymbifera, Aspergillus fumigatus, Blastoschiromyces capitatus, Candida catenulate, Fusarium moniliforme, Geotrichum candidum, Monascus ruber, Mucor circinelloides, M. indicius, Rhizous microspores, R. oryzae, and _Saccharomyces cerevisiae_, including _S. boulardii_[5; 6; 7; 8].  
To reduce the number of pathogens in food, various preventive methods are implemented, including maintaining hygiene and using food preservatives. In many types of food, especially fermented and dairy products, lactic acid bacteria are present [9; 10]. These bacteria produce bacteriocins and organic acids that inhibit the growth of other bacteria. Many products also incorporate plant parts, such as mint, sage, thyme, cardamom, and cinnamon. These plants contain essential oils that not only alter the flavor of dishes but also have antimicrobial properties [10]. Upon closer examination, many plants consumed as food contain substances with antimicrobial activity that inhibit pathogen growth. These substances include organic acids, phenols, phenolic acids, quinones, flavonoids, tannins, terpenoids, and alkaloids [11; 12]. Some of these compounds have the potential to be used as natural inhibitors of foodborne pathogens. We paid particular attention to organic acids, among other reasons, due to the use of some of them in medicine. Acetic acid, lactic acid, and benzoic acid are used in wound treatment, while citric acid is used in wound treatment and root canal antisepsis [13].  
The aim of this study was to examine the effects of 21 organic acids, naturally occurring in plants, on four foodborne bacteria (_Staphylococcus aureus_, _Listeria monocytogenes_, _Escherichia coli_, and _Salmonella enterica_ Typhimuriumiumium) and two foodborne fungi (_Gotrichum candidum_ and _Penicillium candidum_).
## 3 Results and Discussion
### ANG Management Tool Simulation Result
#### 3.2.2 For Field X  
The Decision Phase analysis (see Figure 13) indicated that the ANG to be flared (V\({}_{\rm F}\) = 17,247 MMscf) should be used rather than flared after comparing the ANG flare volume carbon emission value to the regulatory standards assumed for this ANG management tool. The allowable volume (A\({}_{\rm V}\)) and the carbon emission value for V\({}_{\rm F}\) and A\({}_{\rm V}\) are evaluated as 2734 MMscf, 945,136 tonnes and 149,812 tonnes, respectively. This step was then followed by the Reduce to Target Threshold phase. The Reduce to Target Threshold phase (see Figure 13) establishes the target threshold (zero-emission) to be attained and enhances the regulatory case for utilising the ANG volume (17,247 MMscf). To emphasise the necessity for analysing ANG utilisation choices, a basic economic analysis of benefit (amount gained from ANG sales (USD 44M)) and cost (arising from ANG flaring penalty  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Field Y** & & **Field X** & \\  & **Synthesis Gas** & **Methanol** & **Off-Gas** & **Synthesis Gas** & **Methanol** & **Off-Gas** \\ \hline Conditions & & & & & \\ Mass flow (kgmole/h) & 12,930 & 3053 & 873.5 & 8333 & 1952 & 329 \\ Pressure (kPa) & 2995 & 90 & 7400 & 2995 & 90 & 7400 \\ Temperature (\({}^{\circ}\)C) & 17 & 9 & 40 & 17 & 24 & 40 \\ \multicolumn{2}{c}{Mole Fraction} & & & & \\ Methane & - & - & - & - & - \\ Ethane & 0.006 & - & 0.292 & 0.012 & - & 0.292 \\ Propane & 0.001 & - & 0.050 & 0.001 & - & 0.049 \\ n-Butane & - & - & 0.037 & - & - & 0.035 \\ Carbon dioxide & 0.251 & 0.005 & 0.040 & 0.258 & 0.005 & 0.071 \\ Carbon Monoxide & 0.017 & - & 0.457 & 0.008 & - & 0.434 \\ Hydrogen & 0.720 & - & - & 0.718 & - & - \\ Water & - & - & - & - & - & - \\ Nitrogen & 0.003 & - & 0.118 & 0.003 & - & 0.112 \\ Methanol & - & 0.9950 & 0.006 & - & 0.9950 & 0.007 \\ \hline \end{tabular}
\end{table}
Table 9: Properties of the gases produced after the simulation for Fields Y and X.  
(USD 34M)) was performed. To examine the impact of a carbon tax, the carbon tax price was set at USD 20 per ton of CO\({}_{2}\) emissions (a carbon tax of USD 19M is incurred). This step was then followed by the Evaluate Options phase (see Figure 13). The Evaluate Options phase is where the optimum choice for gas utilisation is examined using techno-economic analysis (via the combination of MATLAB and Aspen HYSYS simulation software) to assure the most economical route for investment. Furthermore, the transport cost (both capital and operating transport costs), equipment cost (93M, 113M, and 173M for GTM, GTW, and LNG, respectively), and the economic indicators (see Table 10 for summary), amongst others, for the various ANG utilisation options are evaluated in this phase. Figure 13 shows the simulation results of the various phases obtained from the MATLAB simulation tool.  
Figure 12: Decision Phase, Reduce to Target Threshold phase, and Evaluate Options phase for Field Y.  
Figure 13: Decision Phase, Reduce to Target Threshold phase, and Evaluate Options phase for Field X.
# Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Parana State, Brazil
## 2 Material and Methods
### Data
#### 2.2.3 Performance Analyses  
The IMERG products were compared to ground-based gauge using statistical indices. For this, the IMERG data were sampled at the exact location of the gauge stations. The performance of the IMERG products assessed using the coefficient of determination (R\({}^{2}\)), the Kling-Gupta efficiency index (KGE), the error skewness (SK), the mean error (MBE), the mean absolute error (MAE), and the root of the mean square error (RMSE). The data qualitative assessment was performed using the categorical skills metrics: probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). Such metrics are used in several studies to assess the performance of satellite products [19; 39; 40; 41]. The equations for the metrics used are shown in Table 1. The rainfall threshold was considered as amounts higher than \(1\) mm day\({}^{-1}\), as used by [42]. R\({}^{2}\) measures how much of the variability of one variable can be explained by the other variable. KGE is an objective index which assesses error in terms of correlation, variability, and bias. MBE is a simple average of the error and informs if the estimate on average over- or underestimate the observed data. MAE and RMSE are error metrics in the same units as the observed variable and represent the average magnitude of the error. In this case, RMSE penalizes large errors by squaring the differences between those observed and those estimated. SK represents the distributions of errors. A positive [negative] SK value indicates a median error is smaller [larger] than the mean error, i.e., there is a higher frequency of errors smaller [larger] than the mean error. If the mean error (MBE) is centered close to 0, a positive [negative] SK value can indicate a higher frequency of underestimations [overestimations]. Errors were calculated by subtracting observed from predicted values. POD indicates the fraction of rain events detected correctly with the total number of events detected by satellite; FAR measures the fraction of occurrences of unreal events among the total number of events detected by satellite; CSI denotes the proportion of rain events correctly detected by satellite to the total number of observed events. Additionally, the IMERG's performance for estimating the precipitation was evaluated using a confusion matrix, for the daily and monthly products. The columns in this matrix shows the frequency distribution of IMERG precipitation amount within each ground
## 3 Model and Methodologies
### Preparation Phase
#### 3.1.2 Determination of Optimal Inventory Level  
To manage inventory under uncertain demand, two primary replenishment strategies are used: the continuous review model, which restocks a fixed amount upon reaching a specific reorder point, and the periodic review model, which evaluates and adjusts inventory to a target level at regular intervals. Both strategies aim to maintain a desired service level (\(SL\)), which is defined as the probability of fulfilling the demand with on-hand inventory. In the continuous review model, the reorder point \(R\) is determined by the inverse cumulative demand distribution over lead time \(R=F_{L}^{-1}(SL)\), where \(F_{L}^{-1}(\cdot)\) is the inverse cumulative demand distribution function during lead time, \(L\). For periodic review, the target inventory level \(M\) is adjusted for demand variability over the review period plus lead time \(M=F_{T+L}^{-1}(SL)\), where \(F_{T+L}^{-1}(\cdot)\) is the inverse cumulative demand distribution function during the review period and lead time \(T+L\)[34].  
In the periodic review model, inventory is assessed daily and adjusted to meet the demand based on daily patterns. The service level, which is critical in preventing stockouts or excess inventory, is the probability \(P(\Phi\leq I)\) that balances the cost of unsatisfied demand (\(c_{u}\)) with the cost of surplus inventory (\(c_{o}\)), where \(\Phi\) and \(I\) denote the demand and current inventory level, respectively. The optimal inventory level \(w=Q^{*}\) minimizes the expected mismatch cost \(G(w)\), where \(w\) represents the inventory level, defined in Equation (1), as follows :  
\[\min\quad G(w)= c_{u}\int_{\Phi}\max\{\Phi-w,0\}dF(\Phi)\] \[+c_{o}\int_{\Phi}\max\{w-\Phi,0\}dF(\Phi) \tag{1}\]  
The optimal inventory level \(Q^{*}\) indicates a unique optimal point, owing to \(G(w)\)'s convexity [35]. This framework establishes that the optimal service level aligns with the cost ratio \(\frac{c_{u}}{c_{u}+c_{o}}\) in Equation (2), where \(Q^{*}\) ensures cost-effective inventory management without explicit ordering costs.  
\[F(Q^{*})=\frac{c_{u}}{c_{u}+c_{o}} \tag{2}\]  
To implement this model effectively, accurate demand forecasting is crucial. The inverse cumulative demand distribution function \(F_{T+L}^{-1}(\cdot)\) for future periods is estimatedusing the DeepAR model as detailed in Section 3.1.1. This estimated distribution is then utilized to determine the target inventory level through Equation (2), ensuring that the inventory management system adapts to forecasted demand patterns while maintaining the desired service level.
## 4 A DFN Approach to Analyze Scale Effects in RQD Measurement  
### Conceptual DFN Model  
Using the parameters listed in Table 3, a conceptual rock mass was generated. The model represents a homogeneous rock mass (i.e., a single geotechnical domain) with no faults or shear zones. An example of the DFN model is shown in Figure 5.  
The model used for the simulation is cubic, with height, length, and width equal to 30 m. Three orthogonal boreholes passing through the center of boxes were defined to condition the fractures in the models to a given P10 value. The same boreholes were then used to measure the RQD for variable core run lengths, and another four wells along the diagonal line were defined to provide alternative measurements of RQD in each model (Figure 6).  
Figure 4: The transition from intact to heavily jointed rock mass with increasing sample size (modified from [PERSON], 2007 [31]).  
Figure 3: Variation in the simulated pillar strength as a function of pillar width (modified from [22]).  
This work generated eight models based on four classes of fracture density and two types of wells. Using a method initially proposed by [PERSON] [2], the RQD in each well of different measuring lengths (ranging from 3 to 30 m) was calculated. The maximum and minimum RQD values were recorded for the study of the scale effect and estimation of REL.  
Four off-center parallel wells were defined (Figure 7) to verify the representativeness of the centered wells used for RQD calculation.  
\begin{table}
\begin{tabular}{c c} \hline
**Fracture Data** & **DFN Input Data** \\ \hline Fracture Orientation & Three near orthogonal fracture sets: dip/dip direction of 00\({}^{\circ}\)/000\({}^{\circ}\) (blue), 00\({}^{\circ}\)090\({}^{\circ}\) (grey), and 90\({}^{\circ}\)/000\({}^{\circ}\) (green), respectively. Fisher dispersion value of 80 for all sets. \\ \hline Fracture Length & Log-normal distribution, mean: 3 m, standard deviation: 3 m. No truncation. \\ \hline Fracture Terminations & 0\% \\ \hline Fracture Intensity & Linear fracture intensity P\({}_{10}\) intensity of 3, 5, 7, and 10 m\({}^{-1}\) (for each of the three sets) \\ \hline \end{tabular}
\end{table}
Table 3: Parameters used to generate the conceptual rock mass.  
Figure 5: (**a**) Example of a DFN realization generated using the parameters listed in Table 3; (**b**) the stereonet with the fracture sets (each dot represents a fracture).  
Figure 6: Location and orientation of the boreholes used for the measurements of the RQD for variable core run lengths: (**a**) orthogonal boreholes; (**b**) inclined boreholes.  
According to Table 4, the difference between the RQD calculated using the center well and the average of the surrounding four wells is negligible (RQDs lie within a section of the same RQD Condition). Therefore, representativeness is guaranteed.
# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management
## 3 Methodology
### Improved YOLOv5s
#### 3.2.4 Label Smoothing Algorithm  
Label smoothing is a widely employed regularization technique in deep neural networks (DNNs) that effectively mitigates the issue of overfitting during training. Its implementation has been observed to enhance the classification accuracy of Deep Neural Networks [43]. Overfitting is a prevalent issue arising during deep learning models' movement. It refers to a scenario where the model exhibits good performance on the training set, but its performance on the test set is unsatisfactory. This poor generalization ability renders the model ineffective in predicting unknown data. It is widely accepted that including mislabeled labels is a crucial measure for reducing the potential for excessive confidence in identifying data used for training.  
The technique of label smoothing involves the integration of a uniform distribution, wherein the conventional label vector \(y_{i}\) is substituted with the modified label vector \(\hat{y}_{i}\). Equations (14) and (15) represent label smoothing.  
\[\hat{y}_{i}=y_{i}(1-\delta)+\frac{\delta}{K} \tag{14}\]  
The total count of \(K\) multicategory groups and the small hyperparameter \(\delta\) (typically set to 0.1) are the focal points of assessment.  
\[\hat{y}_{i}=\begin{cases}1-\delta,&i=\text{target}\\ \frac{\delta}{K},&i\
eq\text{target}\end{cases} \tag{15}\]  
In our study, the effectiveness of the label smoothing method was analyzed with a default value of 0.1, which has yielded favorable results in various scenarios [44].
## 2 Experiments  
### Synthesis of SMPUs  
In order to simplify the comparison, the chemicals and molar concentrations of the soft and the hard segments were the same as previously reported [20], except that the high molecular weight polyol, PEG-6000, was used as the soft segment in this work (hereafter denoted as PEG-SMPU series).  
Through the process optimization, it was found that the optimal concentrations of the soft segment (polyols) and hard segment are 58 and 42 wt%, respectively. The hard segment consists of 4,4'-diphenylmethane diisocyanate (MDI), isophorone diisocyanate (IPDI), PEG-200 and 1, 4-butanediol (BDO). The concentrations for the PEG-200 and BDO are 10 and 4.2 wt%, respectively (Diisocyanate:PEG-200:BDO = 27.8:10.0:4.2%). The molar ratio, \(\gamma\), of MDI to IPDI [defined as \(\gamma\) = MDI/(MDI + IPDI)] was varied from 1 to 0.325 as summarized in Table 1, to synthesize different samples to clarify the effect of diisocyanates on the properties of SMPUs.  
The synthesis process established is as follows: The set-up consisted of a 500 mL round bottom three-neck flask, a hot water bath with a temperature controller and an over-head stirrer. The flask was purged with a continuous flow of dry nitrogen and the temperature of the mixture was monitored by a thermometer. A measured amount of PEG-6000 and dimethylformamide (DMF) were first added into the flask, followed by IPDI and two drops of dibutyltin dilaurate catalyst. DMF is a solvent to dissolve the chemicals for reaction, and will vaporize during polymerization. After two hours of reaction at 90 \({}^{\circ}\)C, the PEG-200 (Note: PEG-200 is part of the hard segment, not the polyol soft segment) was added into the flask, and was followed by the addition of MDI. The mixture was reacted for a further hour with continuous stirring. The process was completed by adding BDO, drop wise, as a chain  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline
**Sample** & **MDI** & **IPDI** & **\(\boldsymbol{\gamma}\)** \\ \hline PEG-H1, PCL-S1 & 19.95 & 0 & 1.0 \\ \hline PEG-H2, PCL-S2 & 16.5 & 3.37 & 0.83 \\ \hline PEG-H3, PCL-S3 & 13.36 & 6.65 & 0.668 \\ \hline PEG-H4, PCL-S4 & 10.0 & 10.0 & 0.50 \\ \hline PEG-H5, PCL-S5 & 6.6 & 13.7 & 0.325 \\ \hline \end{tabular} \([\gamma\) = MDI/(MDI+IPDI)].  
\end{table}
Table 1: Molar ratio of diisocyanates used for synthesis of shape memory polyurethane.  
extender for the chemical reaction at 60 \({}^{\circ}\)C for a further hour. The SMPU films were cast by pouring the polymer resin on a PTFE moulding plate. Then the films were baked at 60 \({}^{\circ}\)C for 12 h, 80 \({}^{\circ}\)C for 24 h and 100 \({}^{\circ}\)C for 8 h, respectively, in a vacuum. The same method of synthesis for PCL-SMPUs was reported in the previous work [20].
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 3 Framework of the Interference Analysis  
In this section, we present a framework for evaluating the harmful interference that NGSO satellite systems may cause to RAS telescopes as shown in Figure 3. The framework includes steps for generating the cumulative distribution functions (CDFs) of out-of-band emissions from the NGSO satellites based on the distribution of users and interference avoidance strategies, determining the beam activation probability, and using an EPFD calculation engine to assess the EPFD for each cell of the RAS telescope. By considering the EPFD distributions from multiple NGSO satellite systems, we can calculate the aggregate EPFD distribution for the RAS telescope and assess the overall level of interference. These simulation results can be used to determine how to mitigate or manage the interference caused by the NGSO satellite systems to the RAS telescopes.