esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Using the transfer matrix method, the electromagnetic responses of 1D deformed and non-deformed quasi-periodic photonic crystals arranged in accordance with the Fibonacci sequence are theoretically studied. The gallium selenide (GeSe) and plasma materials (that is, electron density) are used to construct the multilayer Fibonacci structures. If this study is experimentally validated in the future, we intend to insert two transparent polymer film materials at the top and bottom of the structure, which are intended to protect the plasma material and prevent it from escaping and spreading outside the structure. The effect of the order of the Fibonacci sequence, the deformation of the thickness of the layers using a mathematical rule and the role of the plasma material in the reflectance response are discussed. We notice that the position and the width of photonic band gaps are sensitive to the Fibonacci sequence, the thickness and the density of the plasma material layers. In addition, the intensity of the resonance peaks can be controlled by adjusting the plasma material properties. The width of the photonic band gaps can be notably enlarged by applying a structural deformation along the stacks. The proposed structures have potential applications in tunable filters, micro-cavities for LASER equipment, which allow us to obtain an intense laser, and they are very useful in the communication field.  
Fibonacci sequence; plasma; localization; photonic; defect; resonance mode; tunability +
Footnote †: journal:  
Acticle  
Localized Modes and Photonic Band Gap Sensitivities with 1D Fibonacci Quasi-Crystals Filled with Sinusoidal Modulated Plasma
# Simplified Calibration Method for Constant-Temperature Hot-Wire Anemometry
## 4 Conclusions  
This study proposed a unique approach to convert a voltage signal obtained from a hot-wire anemometry to flow velocity data by making a slight modification to existing temperature-correction methods. The proposed approach is a simplified calibration method for constant-temperature hot-wire anemometry without knowing exact wire temperature. Other than the hot-wire signals, the necessary data are the freestream temperature and a set of known velocities that gives reference velocities obtained from the pitot-tube measurement. The proposed approach was applied to various boundary layer profiles with large temperature variations while the wire temperature was unknown. By using a best-fit approach between the velocities in the boundary layer obtained by hot-wire anemometry and by the pitot-tube measurement, which provides reference data, the unknown wire temperature was sought. The proposed approach was confirmed to be applicable to a velocity range between 20 and 80 m/s and with temperature variations up to 15 \({}^{\circ}\)C with an uncertainty level of 2.6% at most for the current datasets. Thus, the proposed approach could be used as a simplified calibration method for the constant-temperature hot-wire anemometry without knowing the wire temperature in case large temperature variations exist.  
[PERSON] is responsible for the entire work from data acquisition at wind tunnel experiments, conceptualization, establishing methodology, data analysis, and writing the original manuscript entirely; M.K. managed the overall project and reviewed; H.I. and S.K. worked on data acquisition at wind tunnel experiments. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study was conducted as part of the Flight Investigation of Skin-Friction Reduction Eco-Coating (FINE) project led by JAXA Aeronautical Technology Directorate. The authors wish to thank the aerodynamics research unit and [PERSON] at the JAXA Aeronautical Technology Directorate for assistance with the wind tunnel experiment, and [PERSON] for help with the hot-wire anemometry. The authors also acknowledge [PERSON] and [PERSON] at Tokyo Metropolitan University, [PERSON] and [PERSON] at the Tokyo University of Agriculture and Technology, and [PERSON] at O-WELL corporation for their support regarding riblet design and its fabrication.
## 4 Trust Evaluation Mechanism
### Indirect Trust Value of Nodes  
The ITV of the node is calculated according to the DTVs of their common neighbors. However, if the common neighbor node is an MN, the MN may tamper with the DTV of node k. Therefore, to prevent the network from being attacked, the average value of the DTVs of common neighbor nodes is considered the ITV of the evaluated node. Take Figure 4 as an example for further explanation. It can be seen that node a and node b are the common neighbor nodes of node n and node k.  
Let the DTVs of node a and node b calculated by node n be \(DTV_{n,a}\) and \(DTV_{n,b}\), respectively. Let the DTVs of node k calculated by node a and b be \(DTV_{a,k}\) and \(DTV_{b,k}\), respectively. The ITV of node k calculated by node n can be expressed as  
\[ITV_{n,k}=\frac{DTV_{n,a}DTV_{a,k}+DTV_{n,b}DTV_{b,k}}{2} \tag{10}\]  
Moreover, let the set \(P=\{p_{1},p_{2},p_{3},\ldots,p_{e}\}\) be the common neighbor nodes of node n and node k, where \(e\) is the number of common neighbor nodes, then the generalized form of the ITV of node k calculated by node n is  
\[ITV_{n,k}=\frac{1}{e}\sum_{m=1}^{e}\Big{(}DTV_{n,p}\times DTV_{p,k}\Big{)} \tag{11}\]
## 4 Discussion and Conclusions
However, the associated data quality flags should be used in evaluating the level of confidence in the FT results, because the 9 km product shows a visibly stronger performance decline with increasing landscape heterogeneity relative to the 36 km grid product. Further improvements to the L1C_B_E product may include mitigation of surface water contamination in the Tb retrievals over adjacent land areas and alternative Tb interpolation approaches such as rSIR [79] or machine learning; these refinements are expected to propagate to better accuracy and performance in the 9 km grid FT product. The latest (R16) version of the SMAP L3_FT products examined in this study covers more than half of the global land area, including all areas where the frozen temperatures are a key limitation to annual ecosystem productivity. However, future products may include a larger domain covering all snow/ice affected regions (e.g., Greenland and Antarctica). A potential future product enhancement may also include FT retrievals from both FT-SCV and NPR algorithms, where conditions are suitable for their joint application. Enhanced delineation of different landscape FT components, including soil, snow cover and vegetation, may allow for an improved understanding of FT related controls on terrestrial carbon, water and energy cycles, and their interactions. Vegetation optical depth (VOD) information from SMOS could help to improve the delineation of landscape FT components and/or vegetation biomass effects on NPR derived FT sensitivity. Potentially complimentary information from other satellite observations, including higher frequency Tb retrievals from AMSR2 and C-band (In)SAR from Sentinel-1, may also provide complimentary information to improve SMAP Tb and FT spatial enhancements and the delineation of landscape FT elements. Future L3_FT products may include these algorithms and product enhancements while also benefitting from ongoing Tb calibration refinements enabled through a longer data record. Y.K. and J.S.K. designed the study and analyzed the data; [PERSON], R.S.D., A.C. and C.D. contributed to data and analysis; all the authors contributed to the writing. This research was funded by NASA (NNX14 AB20A, 80 NSSC18K0980, NNX15 AT74A, NNX14 AI50G), while portions of this work were carried out the University of Montana and Jet Propulsion Laboratory, California Institute of Technology under contract with NASA. The SMAP operational data products are publicly available through the National Snow and Ice Data Center (NSIDC). The authors declare no conflict of interest.
# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
## 2 Materials and Methods
### Growth Analysis  
Crop performance was also verified by destructively harvesting all aboveground plant material in a 2 m long section of each row on two sampling dates to derive classical crop growth indices. Plants were sampled on 9 August 2018 (immediately after SIF measurements) and 23 August 2019 (a two week interval). On each sampling date, plants were placed in plastic bags with moist paper towels to ensure that plant tissues did not desiccate between harvest and measurement. In the laboratory, plants were separated into leaves and stems, and leaf area was determined using a leaf area meter (LI-3100, LI-COR Corp., Lincoln, NE, USA). Total dry weight was assessed following a 48 h drying period at 80 \({}^{\circ}\)C in a forced-air oven.  
Five growth parameters were calculated, including crop growth rate (CGR), net assimilation rate (NAR), relative growth rate (RGR), the difference in leaf area index (\(\Delta\)LAI), and the difference in leaf mass fraction (\(\Delta\)LMF) between the two sampling dates. The five parameters were defined by Equations (7)-(11).  
\[CGR=\frac{W_{l2}^{total}-W_{t1}^{total}}{(t2-t1)\times A^{land}} \tag{7}\]  
\[NAR=\frac{W_{l2}^{total}-W_{t1}^{total}}{t2-t1}\times\frac{\ln A_{l2}^{leaf}- \ln A_{t1}^{leaf}}{A_{t2}^{leaf}-A_{t1}^{leaf}} \tag{8}\]  
\[RGR=\frac{\ln W_{t2}^{total}-\ln W_{t1}^{total}}{t2-t1} \tag{9}\]  
\[\Delta LAI=\frac{A_{t2}^{leaf}-A_{t1}^{leaf}}{A^{land}} \tag{10}\]  
\[\Delta LMF=\frac{W_{t2}^{leaf}}{W_{t2}^{total}}-\frac{W_{t1}^{leaf}}{W_{t1 }^{total}}, \tag{11}\]  
where \(W^{leaf}\) and \(W^{total}\) represented the leaf and total dry weights. \(A^{leaf}\) and \(A^{land}\) represented the leaf and land areas. In the present study, the \(A^{land}\) was 1.82 m \({}^{2}\) (0.91 m \(\times\) 2 m). \(t2\) and \(t1\) were the sampling dates in days after planting (DAPs).
## 3 Results
### Throughput Using MODCOD in Adapted ACM Mode  
Subsequently, we analyze the effectiveness of our proposed strategy in Table 5, varying the ACM according to the INR in a satellite pass.  
Thus, the throughput (82.878 Gbits) is higher than the minimum for WFI image transmission (computed as 36.922 Gbits). In this case, we would not expect failures in image processing.
## 2 Materials and Methods
### Migration Assays  
Migration analysis was carried out by total immersion of the film samples in three different food simulants: 10% ethanol (simulant A), 3% acetic acid (simulant B), and 95% ethanol (simulant D2), following the specifications of Regulation (EU) No. 10/2011. The films were cut into strips measuring 1 \(\times\) 4 cm\({}^{2}\) for simulant D2 and 1 \(\times\) 5 cm\({}^{2}\) for simulants A and B, ensuring the appropriate surface area-to-volume ratio. Each sample was placed in 20 mL glass vials and 18 mL of the corresponding simulant was added under gravimetric control. All samples were incubated in the oven at 60 \({}^{\circ}\)C for 10 days. Blank samples were prepared and treated under identical conditions. Migration tests were conducted in triplicate, with blank extractions performed after every three test samples. The  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Sample Name**} & \multirow{2}{*}{**Sample Code**} & \multicolumn{4}{c}{**Film Properties**} \\ \cline{3-6}  & & **Oxygen Permeability** & **Water Vapor Permeability** & **Tensile Strength** & **Elongation at Break (\%)** \\  & & **(cm\({}^{3}\) mm/m\({}^{2}\) day\(\cdot\) atm)** & **(g\(\cdot\) mm/m\({}^{2}\) day\(\cdot\)KPa)** & **(MPa)** & **Break (\%)** \\ \hline PBAT Resins & R & - & - & - & - \\ PBAT Films & F & - & - & - & - \\ PBAT/TPS & C & 16.69 \(\pm\) 0.08 & 4.20 \(\pm\) 0.14 & 14.43 \(\pm\) 0.51 & 656.11 \(\pm\) 11.50 \\ PBAT/TPS + Sodium & N & 4.65 \(\pm\) 0.26 & 4.51 \(\pm\) 0.18 & 11.64 \(\pm\) 0.66 & 794.62 \(\pm\) 17.45 \\ PBAT/TPS + Sodium & E & 5.26 \(\pm\) 0.18 & 3.54 \(\pm\) 0.18 & 12.82 \(\pm\) 0.58 & 651.94 \(\pm\) 18.46 \\ PBAT/TPS + Sodium & T & 21.42 \(\pm\) 0.73 & 4.01 \(\pm\) 0.06 & 10.60 \(\pm\) 0.24 & 571.37 \(\pm\) 16.18 \\ Tripolyphosphate & H & 11.22 \(\pm\) 1.89 & 3.11 \(\pm\) 0.14 & 10.42 \(\pm\) 8.78 & 595.95 \(\pm\) 8.78 \\ PBAT/TPS + Tetrasodium & P & 29.07 \(\pm\) 3.81 & 4.30 \(\pm\) 0.20 & 9.31 \(\pm\) 0.31 & 521.30 \(\pm\) 6.19 \\ Pyrophosphate & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Name and characteristics of neat PBAT and PBAT/TPS film samples, which incorporated additives, namely sodium nitrite, sodium erythorbate, tetrasodium pyrophosphate, sodium tripolyphosphate and sodium hexametaphosphate, produced via blown-film extrusion.  
simulant samples were analyzed using and UPLC-Q-TOF-MS to determine the migrated compounds from film samples. This offered a high-resolution mass analysis suitable for non-targeted and semi-targeted screening of migrants (e.g., additives, degradation products, and monomers).
# Application of Dynamic Time Warping (DTW) in Comparing MRT Signals of Steel Ropes
###### Abstract  
Steel wire ropes used in transport and aerospace applications are critical components whose failure can lead to significant safety, operational, and environmental consequences. Current diagnostic practices based on magnetic rope testing (MRT) often suffer from signal misalignment and subjective interpretation, particularly under varying operational conditions or in polymer-impregnated ropes with delayed damage indicators. This study explores the application of the Dynamic Time Warping (DTW) algorithm to enhance the reliability of MRT diagnostics. The research involved analyzing long-term MRT recordings of wire ropes used in mining operations, including different scanning resolutions and signal acquisition methods. A mathematical formulation of DTW is provided along with its implementation code in R and Python. The DTW algorithm was applied to synchronize diagnostic signals with their baseline recordings, as recommended by ISO 4309:2017 and EN 12927:2019 standards. Results show that DTW enables robust alignment of time series with slowly varying spectra, thereby improving the comparability and interpretation of MRT data. This approach reduces the risk of unnecessary rope discard and increases the effectiveness of degradation monitoring. The findings suggest that integrating DTW into existing diagnostic protocols can contribute to safer operation, lower maintenance costs, and reduced environmental impact.  
t  
trapped; safety; carbon footprint; non-destructive testing; MRT; wire ropes; dynamic time warping
## 6 Conclusions  
The braking performance of a vehicle during emergency braking is influenced by both road surface conditions and the robustness of the controller. A hierarchical control strategy for an anti-lock braking system (ABS) employing active disturbance rejection control (ADRC) has been proposed. The main conclusions are as follows:  
* The force acting on a single wheel and the road-tire model were analyzed. For the EMB system, a road recognition algorithm based on an extended state observer (ESO) was designed. During emergency braking conditions, this algorithm observes the current longitudinal vehicle speed to obtain the current slip ratio. By calculating the utilization adhesion coefficient for typical road surfaces and comparing it with the actual road surface adhesion coefficient, the algorithm identifies the road type. This approach meets the requirements for road recognition accuracy and response time during emergency braking scenarios;
* Through ABS braking simulations, the designed ADRC-based slip ratio tracking control strategy for ABS was validated. Compared to the PID and SMC algorithms, the  
Figure 9: Braking performance on variable pavement. (**a**) Braking distance. (**b**) Vehicle speed. (**c**) Wheel speed. (**d**) Slip.  
ADRC strategy demonstrates superior performance across various road conditions, when considering factors such as overall braking distance, braking time, and control response. Under single-pavement conditions, compared to PID control, the braking distance is reduced by approximately 5.1%, and the braking time is shortened by 4.3%; compared to SMC, the braking distance is reduced by 4.2%, and the braking time is shortened by 1.6%. Under variable pavement conditions, compared to PID control, the braking distance is reduced by 22.5%, and the braking time is shortened by 10.3%; compared to SMC, the braking distance is reduced by 6%, and the braking time is shortened by 3.2%. ADRC exhibits strong robustness and better adaptability to changes in road surfaces, which is crucial for enhancing vehicle braking performance.  
Conceptualization, S.L.; Methodology, S.L.; Software, B.Z.; Validation, B.Z.; Formal analysis, B.Z.; Investigation, J.M.; Resources, X.Z.; Writing--original draft, B.Z.; Writing--review & editing, B.Z.; Project administration, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# An Improved Reference Paper Collection System Using Web Scraping with Three Enhancements
## 4 Design of Improved Reference Paper Collection System
### Server-Side Design  
On the server side, a _Python_ program handles the request by responding to the search API type and the queries given by the user. The web browser sends an _HTTP GET_ request to the web server with related parameters. The internal system utilizes multithreading using the _concurrent_,_ futures_ library to allow multiple search API requests to be processed in parallel. The _HTTP GET_ request is used because it is intended only for fetching data. The web server retrieves the requested data from the file system and returns an _HTTP_ response status code of 200 when it successfully retrieves the bibliographic metadata. The web server may return a different status code, such as 404, when the requested data is not found.  
The web server, which handles the requests from the client-side and manages the API calls, detects the request and forwards it to the application server according to the respective platform API _endpoint_. The application server identifies the request based on the parameters, including the search API type and the keyword search queries. The application server then gets the required information from the database through the search API _endpoint_. _The /search/bulk endpoint_ is used to retrieve metadata from _Semantic Scholar_ database, _the /search/article endpoint_ is used in _DOAJ_ database, and _the /entrez/eutils/endpoint_ is used in _PubMed_ database. The application server sends back the metadata obtained to the web server.  
After the metadata is retrieved from the web server, the internal system parses and extracts metadata using _response.json()_ and _ElementTree_. Since the data sources return responses in different formats, we standardize all extracted data and reformat them into a _JSON_ schema to facilitate data integration and ensure metadata consistencies. Subsequently, the metadata is processed in a series of data processing stages and stored in a _JSON_ format. Finally, the final metadata is sent to the user as a response from the program. Figure 3 illustrates the server-side design of a _reference paper collection system_.  
Figure 2: User interface: (**a**) main page; (**b**) performance metrics.
# A Study on the Development of Tourist Support System Using ICT and Psychological Effects
## 5 Providing Information Based on Prospect Theory
### Calculation of the Initial Value of Information  
To calculate the value of information based on Prospect Theory, we classified information for sightsseeing into four categories based on [PERSON]'s hierarchy of needs as described in Table 3.  
As the basis of the classification of sightsseeing information, we used levels 1, 3, 5, and 6 of [PERSON]'s hierarchy of needs. The reasons why we did not use levels 2, 4, and 7 are as follows. Level 2 is a \"safety\" need. Between the Nikko Station and Toshogu Shrine, that area is Nikko's central part and a very safe place. Therefore, it is no need to provide safety information to a tourist. Level 4 is \"esteem\" needs, such as posting information to SNS so that such information is not needed to be provided by sightsseeing application. Level 7 is \"self-actualization\" needs, and there is no need to provide such information.  
We set some value factors that should affect the value of sightsseeing information, which are based on the characteristics of the categories. Table 4 shows an example of the value factors of sightsseeing information. The value factor index (I) was set in five stages, which ranged from 1 (lowest) to 5 (highest).  
Figure 12: Prospect theory.  
For example, based on a restaurant's information included in [PERSON]'s biological and physiological needs, three information, i.e., \"interest\", \"rareness\", \"environment\", and \"location\" are set. At the start of the application, the \"interest\" is the information provided in the questionnaire. The \"rareness\", \"environment\", and \"location\" are obtained when the information is added in the application's database. For example, \"JR Nikko Station\" has the following information, as described in Table 5.  
* [PERSON]'s hierarchy of needs: Aesthetic needs
* Category of information: Landmark
* Value factors: Rarity:3, Attraction:2
# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 2 Methodology  
### Wrapper Method for Feature Selection  
For feature selection, a wrapper method is employed in this paper. To accomplish the task, wrapper techniques use a learning algorithm that applies a search strategy to explore the space of feasible feature subsets, ranking them according to the quality of their performance in a specific algorithm. In most of the cases, wrapper approaches outperform filter methods since the feature selection process is tailored to the specific classification algorithm being employed. Wrapper methods, on the other hand, are prohibitively time- and resource-intensive for high-dimensional data since they require the evaluation of each feature set using the classifier algorithm. Figure 1 exhibits the framework of the wrapper-based feature selection approach. It can be noticed from Figure 1 that the database was initially spilt into dedicated training and testing sets. In this paper, an 80:20 split was considered for train and test sets. The train set was then deployed in the wrapper-based feature selection framework where the metaheuristic algorithm would act as the optimizer to optimize a composite function (Equation (1)). The framework starts by selecting a subset of the features and building a classification model using the specified ML algorithm. The performance of the model is then evaluated, and based on the derived results, the optimizer feeds a revised set of features to build a new classification model. This process of iterative classification model building, model performance measurement and the feeding of revised features to the classification algorithm continues until a pre-specified termination criterion is met. In this paper, the maximum iteration limit was considered as the termination criterion. The final classification model is finally tested on the independent test data, and its performance is recorded. In this paper, KNN was considered as the classification algorithm. The KNN method employs a set of K neighbors to determine how an object should be categorized. A positive integer value of K is pre-decided before running the algorithm. To classify a record, the Euclidean distances between the unclassified record and the classified records are determined and ranked.