esa-sceva/satcom-chunk-collection huggingface dataset:
# Parallel Agent-Based Framework for Analyzing Urban Agricultural Supply Chains
## 6 Experiments
### Analysis of Environmental Risk Factors  
In this section, we analyze the sensitivity of the environmental risk factors such as frosts, droughts, and heatwaves. Figure 12 presents a heatmap showing the percentage variation in the Mean Absolute Percentage Error (MAPE) under different stress test scenarios for environmental risk parameters. The rows represent the three risk dimensions considered in the model--heatwaves, frosts, and droughts (measured via the Standardized Precipitation Index, SPI)--while the columns correspond to scenario intensities: low 1 (lo1), low 2 (lo2), high 1 (hi1), and high 2 (hi2). Each scenario was evaluated while holding all other factors constant (ceteris paribus), modifying only the target variable's intensity across months. For instance, in the hi2 heatwave scenario, each month was increased by two intensity levels, whereas in the lo1 frost scenario, one level was subtracted per month.  
Regarding the heatwave, the MAPE increased substantially in the hi1 and lo2 scenarios, with the highest variation reaching over 2.5% in hi1. This indicates that the model is particularly sensitive to increases in heatwave intensity. Interestingly, reducing heatwave levels (lo1) also increased MAPE slightly, suggesting that both extreme increases and decreases affect the prediction accuracy. The hi2 scenario shows a smaller impact than hi1, possibly due to saturation or threshold effects in the model's sensitivity.  
By evaluating the frosts, the model shows moderate sensitivity to changes in frost intensity. The largest increase in MAPE occurs under the hi1 condition, but remains below 1.5%. The low-impact scenarios (lo1 and lo2) have a minimal effect, suggesting that parameter sensitivity is relatively stable against slight reductions in frost intensity.  
Finally, when evaluating the frosts, a notable variation is observed in lo2, with an MAPE increase close to 1.5%, indicating that the parameter is sensitive to reductions in precipitation (more drought-prone scenarios).  
Figure 11: Relative difference between the actual data and the values reported by the simulator of the quantity sold per product. (**a**) Metropolitan city of Santiago and (**b**) sixth region.
## 5 Fracture Analysis
### Effect of Aggregate Geometry  
The geometry of aggregates may affect the fracture behavior of asphalt concrete, which emphasizes the importance of aggregate shape in numerical modeling. In order to investigate the effect of aggregate geometry on the fracture behavior of asphalt concrete, two specimens with the same aggregate distribution but different aggregate shapes were generated, as shown in Figure 14. The SCB test was simulated with these specimens under a temperature of 0 \({}^{\circ}\)C and a loading rate of 5 mm/min.  
The total number of adhesive-damaged elements and cohesive-damaged elements was counted at the peak loads for the two specimens, and then the percent of damaged elements was calculated, as shown in Figure 15a. It can be seen that the percent of damaged elements for the specimen consisting of ellipsoidal aggregates was lower than that consisting of polyhedral aggregates. At the same time, for the specimen consisting of ellipsoidal aggregates, adhesive-damaged elements only accounted for 37.41% of the total damaged elements. While for the specimen consisting of polyhedral aggregates, this value increased to 43%. In other words, more angular aggregates tend to induce a higher percentage of damaged elements, especially adhesive-damaged elements. This can be further explained by Figure 15b, which shows the distribution of SDEG values for adhesive-damaged elements. As shown in Figure 15b, the maximum values of SDEG in adhesive-damaged elements were 0.96 and 0.99 for the specimen consisting of ellipsoidal aggregates and polyhedral aggregates, respectively. It should be noted that, in the simulation of this paper, any element was treated as failed and turned into a crack when its SDEG value reached 0.99. Therefore, almost no cracks formed at the aggregate-asphal interface of the specimen consisting of ellipsoidal aggregates, and the crack propagation path should be inside the asphalt mortar phase since the maximum SDEG value of the adhesive element was smaller than 0.99. While for the specimen consisting of polyhedral aggregates, some cracks formed at the aggregate-asphal interface. This is mainly because the polyhedral aggregates are more angular, which induces stress concentration at the aggregate-asphal interface.
## 2 Study Area and Data  
### Study Area  
This study focuses on the Heihe River basin [21] in the northwestern part of China. The region is geographically located between approximately 98\({}^{\circ}\) E to 105\({}^{\circ}\) E and 37\({}^{\circ}\) N to 42\({}^{\circ}\) N, and is a typical inland river basin, as shown in Figure 1.  
China's geographic environment is complex, characterized by significant elevation differences, diverse land cover types, and a wide range of climate types. The Heihe River region exhibits a similarly intricate geographic environment, including Deserts, grasslands, and Farmland. The climate is varied, ranging from arid to semi-arid, making it an ideal location for studying surface temperature variations. Based on the image, it can be observed that a significant portion of the Heihe River Basin consists of unused land, which refers to areas where more than 60% of the surface is devoid of vegetation, with vegetation cover below 10%. Due to the lack of vegetation's shielding and cooling effects, these barren lands exhibit higher absorption and reflection of radiation rates, leading to higher surface temperatures in the region. High spatial resolution LST holds immense value across various applications, and TIR LST products are currently the most widely used high-resolution LST. However, TIR LST is highly susceptible to cloud and fog interference, leading to significant data loss under cloudy conditions. For example, the MODIS LST product shows extensive data loss across China. Therefore, developing an all-weather LST generation method suited for China and obtaining spatially seamless, high-resolution TIR LST are of considerable practical significance.  
Figure 1: Heihe District Division Map and location distribution of sites.
## 3 Results and Discussion
### Characterization of CNCs-Coated Plastics
#### 3.2.2 FTIR of Corona-Treated and Untreated CNCs-Coated PET Film  
The corona treatment was aimed at offsetting the lack of affinity and reinforcing the adhesion between CNCs and adhesive. As outlined in Figure 7a-c, the peaks formed during the corona-treatment can be observed. The ozone treatment usually oxidizes functionally chemical groups of the cellulose nanocrystals surface. However, that oxidation was less pronounced in the already-oxidized cellulose nanocrystals (CNC\({}_{\text{COOH}}\)), while it was much more marked on esterified CNCs (CNC\({}_{\text{COOR}}\)) and CNC\({}_{\text{SO3H}}\) surface (Figure 7a-c), with the formation of carbonyl groups (carboxyl and ester) between 1645 cm\({}^{-1}\) and 1750 cm\({}^{-1}\). These polar groups would contribute to improve the adhesion between CNCs coatings and the polyurethane adhesive.  
Figure 5: Z potential values versus pH of uncoated and coated PET film with the three types of cellulose CNCs (\(n=3\)).  
Figure 6: Water contact angle values versus time of PET film coated with the three types of cellulose nanocrystals.
## 5 Conclusions  
In conclusion, in this study, we provide a description and construction of a 16-leg low-pass PFT \({}^{13}\)C birdcage RF coil for hyperpolarized \({}^{13}\)C MRS/MRSI. We verified the usefulness and applicability of the proposed PFT \({}^{13}\)C birdcage RF coil by assessing its performance using \({}^{1}\)H FDTD EM simulations, \({}^{1}\)H MR experiments for ACR and corn-oil phantoms, \({}^{13}\)C 2D CSI for corn-oil phantom, in vivo hyperpolarized \({}^{13}\)C slab dynamic MRS using 3D FIDCSI, and in vivo \({}^{13}\)C 3D MRSI. The proposed PFT \({}^{13}\)C birdcage RF coil was observed to significantly aid \({}^{13}\)C animal MRS/MRSI analysis because it acquires high-resolution \({}^{1}\)H and \({}^{13}\)C images without any physical movements of in vivo models and hardware structures. Further research should be performed to assess the applicability of the proposed 16-leg low-pass PFT \({}^{13}\)C birdcage RF coil with CPSCs in various x-nuclei, such as \({}^{23}\)Na, \({}^{31}\)P, and \({}^{19}\)F, as well as in ultra-high-field (UHF) MRI systems with state-of-the-art MR pulse sequences.  
Conceptualization, J.-S.Y. and C.-H.O.; methodology, J.-S.Y., Y.-J.J., H.-J.C. and C.-H.O.; validation, J.-S.Y., I.P. and C.-H.O.; formal analysis, J.-S.Y.; investigation, J.-S.Y., J.-M.K., H.-J.C., G.-W.J., I.P., G.-W.K. and C.-H.O.; data curation, J.-S.Y. and I.P.; writing--original draft preparation, J.-S.Y.; writing--review and editing, J.-S.Y., J.-M.K. and C.-H.O.; supervision, C.-H.O. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Technology Innovation Program (#10076675) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) and the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 1711138003, KMDP-RnD KMDP_PR_20200901_0041-2021-02).  
The animal study was approved by the Institutional Animal Care and Use Committee (IACUC) of Chonnam National University Medical School, Gwangju, Korea (protocol code: CNU-IACUC-H-2016-28 and date of approval: 2016-08-31). All experiments were performed based on relevant guidelines and regulations.  
This study does not involve any human studies.  
This study also includes the study of the human studies.  
**Acknowledgments:** The authors thank ZMT for providing a free license of Sim4 Life used in this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion  
In the literature, many studies analyzed the 6 h and 3 h updated GNSS satellite orbit and its application, and few orbit products contain the full constellation of Galileo, BDS and QZSS NONE GEOs. Disadvantages about accuracy, latency and stability have still been recognized in recent research works using the predicted half of IGS ultrarapid ephemerides (6 h update interval and 3 h latency) for real-time applications. A strategy of real-time multi-GNSS orbit determination via hourly update is proposed in this contribution. With the station-clustered and parallel processing method, POD of multi-GNSS can be conducted within one hour, and thus, hourly updated orbit is achievable using cloud servers. By comparing with GFZ final orbits, orbit boundary discontinuity investigation and SLR validation, we analyzed the performance of our orbit.  
Comparison with GFZ final multi-GNSS orbits show improvement of our GPS orbit with respect to the predicted half of IGS ultrarapid GPS satellite ephemerides with a nominal accuracy of 5 cm (1-D mean RMS). It should be noted that the accuracy of the Galileo constellation is almost equal to that of GPS not only in external orbit comparison but also in orbit boundary discontinuity investigation. For BDS, BDS IGSOs have larger  
Figure 21: SLR validation results of BDS-2 (**a**) C08 (**13**), (**b**) C10 (**l**5**), (**c**) C13 (**l**6**) and (**d**) C11(M3) orbits.  
Figure 20: SLR validation results of BDS-3 (**a**) C20 (M2), (**b**) C21 (M3), (**c**) C29 (M9) and (**d**) C30 (M10) orbits.  
RMS values than MEOs, mainly due to the observational geometry. It should also be noted that accuracy of BDS MEOs is almost equal to that of the GLONASS constellation. Due to limited observational geometry and limited tracking stations, the accuracy of the QZSS constellation is worse than that of other constellations in external orbit comparison, which has also been recognized in a further orbit boundary discontinuity assessment. As to our investigation, another possible cause is some mismodeled or unmodeled force errors such as the solar pressure model (SRP) and the inappropriate satellite attitude model, which should be further investigated. SLR validation results show that the BDS satellite orbit radial error is in good agreement with the external orbit comparison. The a priori cuboid + ECOM1 model helps BDS-3 satellites achieve subequal accuracy with respect to BDS-2, whereas the average value of SLR validation indicates that further studies on empirical SRP modelling for BDS-3 satellites are still essential.
## 2 Proposed Method
### Framework Overview  
To alleviate the above-mentioned problem, in this paper, we implicitly model the function from the response to the load in the transform domain using a designed neural network. Figure 1 shows the main framework of the proposed method. There are four modules in the proposed methods in total, including the input module, the feature-extraction module, the prediction module and the output module.  
In the input module, the response in time domain, i.e., \(a^{i}(t),i=1,2,\cdots,N\) for \(N\) different acceleration measurement points in a certain time window are transformed into wavelet domain using Meyer wavelet. In total, 9 of 16 decomposed components of the wavelet are used in the network for each \(a^{i}(t),i=1,2,\cdots,N\). In the feature-extraction module, the convolutions designed with batch normalization and weight normalization would extract the data-driven features in the wavelet domain. In the prediction module, the learned wavelet domain features are used for predict the load in the wavelet domain, i.e., the 9 decomposed components. Finally, in the output module, the load in wavelet domain is transformed back to the time domain. Details of the proposed network is given in Section 2.2.
## 7 Nanotechnology in Food Science
### Antimicrobial Activity  
Nanomaterials synthesized from metals and metal oxides, such as silver, zinc oxide, copper, selenium, magnesium, and gold [23], have demonstrated an antimicrobial effect attributed to the cellular internalization of nanoparticles, direct interaction between the microorganism with the surface of the nanostructure, and the effect of metal ions at the nanoscale. Generally, this antimicrobial activity depends on the composition, concentration of the metal oxide, and physicochemical properties of the nanostructure, such as size, shape, surface charge, and ligands that facilitate cell internalization. On the other hand, the effect of metal ions is due to their specific binding affinity for functional groups of biomolecules present in the cell wall structures of microorganisms. In the case of bacteria, amino (-H), carboxyl (-COOH), hydroxyl (-OH), and thiol (-SH) groups in particular participate in these interactions, which lead to changes in membrane permeability and initiate the processes of lipoperoxidation and protein denaturation [61,62]. Among these, silver ions possess the highest toxicity to microorganisms, followed by other metal ions according to the following sequence: Ag > Hg > Cu > Cd > Cr > Pb > Co > Au > Zn > Fe > Mn > Mo > Sn [63]. The toxicity of silver ions is due to their ability to bind to the thiol group of a protein, altering its three-dimensional structure; they can also interfere with the transport and release of potassium (K+) in cells, thus affecting the membrane potential [64]. Similarly, the interaction mechanism between iron nanoparticles and the bacterial cell wall disrupts the permeability of the plasma membrane, involving the release of reactive oxygen species that can damage DNA and proteins, eventually causing cell death [65]. The antimicrobial activity of nanostructures is determined by the effect of coating agents and by the use of antibiotics or extracts with antimicrobial activity, which, in conjunction with the shape, size, and concentration, can increase the desired effect on a microorganism. Concerning polymeric nanocomposites, the preference for the use of biopolymers is increasing due to the availability in nature of these matrices, such as cellulose, chitosan, agarose, alginic acid, pectin, and starch, among others. In addition to their availability, they are also biodegradable and low-cost. Due to their structure, these matrices have oxygen-rich functional groups and act as good reducing and stabilizing agents and coatings. Therefore, biopolymers are considered emerging nanomaterials that can increase antimicrobial activity  
Figure 4: Hierarchical structural organization of Nepal, showing cellulose nanofiber as a part of the nanostructure (adapted from [58]).  
in combination with inorganic materials. One of the most popular biopolymers is chitosan, which is a heteropolyosaccharide noted for its biocompatibility.
## 6 Simulation and Evaluation of Results  
CTSRD has been implemented using the network simulator version 2 (NS2), and various tests are performed to analyze and compare its performance with the results obtained from EEMSR [29] and E-BEENISH [30]. The reasons for selecting EEMSR and E-BEENISH are:  
* EEMSR, like CTSRD, focuses on network security and energy efficiency simultaneously while E-BEENISH only focuses on energy efficiency.
* Both EEMSR and E-BEENISH use an efficient clustering method, as in our scheme.
* EEMSR presents a GA-based routing method for creating paths between CHs while our scheme uses DA for creating a routing tree between CHs.  
Thus, the comparison of our scheme with EEMSR and E-BEENISH presents valuable results. Note that EEMSR and E-BEENISH are also simulated in NS2. The evaluation operation of these results is based on the simulation parameters defined in Table 3. According to this table, 100 IoT nodes are randomly distributed on the network, and the size of the simulation environment is \(100\times 100\,\mathrm{m}^{2}\). The connection radius of IoT nodes is 20 m. IoT nodes are heterogeneous and have different energy levels. In this paper, it is assumed that there are four types of IoT nodes whose energy levels are \(E_{1}=2J\), \(E_{2}=2E_{1}\), \(E_{3}=2.5E_{1}\)and \(E_{4}=3E_{1}\), so that the percentages of type 1, type 2, type 3, and type 4 are 50%, 35%, 12%, and 3%, respectively. The size of the packets is 500 bytes, and 100 transmissions are performed at each round. Note that the simulation results are presented with regard to a number of rounds. Thus, the simulation process is executed one time.
## 1 Introduction
The harmonic cancellation between multiple SMPS operated at the same time has been presented in [10]. A Monte Carlo simulation is used to aggregate numerous SMPS loads. The author concludes that harmonic cancellation is more prominent in high power SMPS. The losses due to harmonic current injected by SPMS in a commercial building are discussed in [11]. The harmonic losses generated by modern electronics in the commercial buildings are responsible for neutral conductor overloading, overheating of the cables, and power factor reduction. The author projected an 8 kW of additional losses due to harmonic loading caused by SMPS. It will increase the total building wiring losses by up to 250%. The wiring loss caused by personal computers without harmonic elimination is 2.4 times compared to resistive loads. The comparison of electromagnetic interference in the grid due to series and parallel configuration of SMPS load is presented in another study [12]. A computer simulation is used to estimate the noise effectuated by SMPS in [13]. Topologies of SMPS are simulated to find the dimension that can impede noise emission defined by the standards. A Simulink model is used to scrutinize the current harmonics generated by the SMPS in another study [14]. The nonlinear loads can be imitated as parallel harmonic current sources with magnitude and phase angles [15]. A total harmonic distortion between 150% to 200% for different 3-phase balanced and unbalanced schemes was reported. The harmonic current injection was influenced by the number of equipment, circuit topologies, and type of equipment. The imbalance of the phase lines also altered the harmonic injection. The current harmonic generation due to SMPS inside computers and other electronic equipment and the harmonic mitigation effects in the large office building is presented in [16]. Although, the studies mentioned above have considered uncertainties like harmonic cancellation, none of them discuss the effect of time-dependent variations of the harmonic currents due to thermal stability. A recent study reported significant variation in current harmonics magnitude and phase angles of LED lamps when pure sinusoidal voltage is applied [17]. LED lamps also contain rectifier circuits and draw a current waveform similar to the SMPS. The study concludes that by using measurements performed during the period when LED lamps were unstable leads to a significant error in harmonic current estimation. This paper presents the results of the harmonic current variation of different industrial switch mode power supplies under constant load and operating conditions. A comprehensive overview of SMPS is presented in Section 2. The details of the experimental setup are described in Section 3. Section 4 includes a detailed analysis of the experimental results and framework to estimate the stability time of the power supplies. A comparison of the stability time of power supplies is also described in this section. Conclusions are presented in Section 5.
# Estimation of Driving Direction of Traffic Accident Vehicles for Improving Traffic Safety
## 3 Proposed Method
### Data Preprocessing  
Since investigators compile traffic accident data, it can contain duplicate entries, missing data, and erroneous information. In addition, even for a single accident, multiple entries may be recorded depending on the specific circumstances of the accident. To address these issues, duplicate data with the same _\"Case Number\"_ is removed, and missing data due to omitted _\"Report\"_ information is excluded from the analysis. For example, suppose the _\"Report\"_ field is missing. In that case, information about the destination cannot be obtained and, therefore, must be excluded from the analysis.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 9 Discussion
### Clarifying Accuracy and Performance Metrics  
Accuracy must not be interpreted as a one-size-fits-all measure of model quality in fairness-aware ML. Standard accuracy (i.e., number of correct predictions) can be misleading when fairness interventions alter class distributions or decision boundaries. Therefore, balanced accuracy, precision/recall across groups, and fairness-related metrics (e.g., equal opportunity difference) offer more sensitive measurements. For instance, a model can be high in overall accuracy but poor on underrepresented groups and thus violate fairness principles. Our work combines such nuanced metrics to uncover performance differences that could be difficult to discern with aggregate accuracy metrics, giving insight into where and when fairness-aware models perform or fail.