esa-sceva/satcom-chunk-collection huggingface dataset:
# Buckling-Restrained Bracing System with Ultra-High-Performance Fiber Concrete
## 3 Finite Element Analysis Results for UHPFRC and Conventional Concrete Filler
### Concrete Compressive Strength Effect on BRB Performance  
Since the main focus of this study is on BRB's filler material, a parametric study was conducted to investigate the effects of different concrete filler materials on the performance of a buckling-restrained brag. For this purpose, three types of concretes, including low-grade concrete with compressive strength of 25 MPa, medium-grade concrete with 50 MPa strength and high-grade concrete with 90 MPa compressive strength were considered to implement as filler material for the BRB device.  
As is illustrated in Figure 21, the results for the various concrete fillers regarding their effect on BRB performance indicate that filler material does not exhibit a noticeable effect on the BRB performance during tensional movement. However, as shown in Table 10, in compression, the expected trend of increased concrete compressive strength leading to higher resistant force in the BRB during applied cyclic movement is observed. By changing the concrete filler material from low grade to medium grade, the maximum resistance force increased by 15%. Similarly, replacing low-grade concrete with high-grade concrete leads to a 23% enhancement in the maximum compression resistance force. As was mentioned previously, this increase in resistant force is attributed to the improved confinement capability of the concrete with higher compressive strength.  
Figure 21: Cont.  
Hence, the result of the parametric study revealed that the effect of concrete compressive strength of more than 50 MPa on BRB performance is negligible and it may not justify using concrete with very high compressive strengths due to its associated costs.
# An Integrated Cognitive Radio Network for Coastal Smart Cities
## 1 Introduction
The service provided by these VNs is routing, which means the two environments can establish the same routing path for different vehicular and marine applications. We will extend the applications for both vehicular and maritime networks, along with different services in the near future. Our motive is to apply the idea of integration in a cognitive coastal city that combines vehicular and maritime networks to provide a stable path between the source car and destination ship for hybrid communications. This hybrid communication could be required for cargo transport, establishing data links between different types of transportation and security control, as well as for surveillance. To the best of our knowledge, this is the first study that has considered a routing service in hybrid communications for a cognitive coastal city with the integration of SDN, NFV, and FC. The main contributions are as follows:  
* An integrated coastal smart city is designed in which a user is able to fulfill the service demands from different applications in less response time. This concept of integration also enables different users from different applications to be served at the same time. * We provide hybrid, as well as pure communications between the vehicular and maritime networks by using an integration of SDN, NFV, and FC. * To alleviate the issues of spectrum scarcity for both automobile and maritime communications, we also consider the concept of cognitive technology to maintain stable networking. The categorization of the remaining paper is as follows. An integrated cognitive hybrid communications framework for a coastal city is presented in Section 2. In Section 3, we discuss the simulation results of the proposed coastal smart city, while Section 4 provides the conclusion of the paper.
# Compact Multilayer Yagi-Uda Based Antenna for IoT/5G Sensors
## 4 Discussions  
Regarding the feeding structure, it was verified that the used microstrip-to-coplanar stripline transition provides good matching results between the simulated and practical measurements, for both designed antennas. However, this microstrip structure is not physically symmetrical, because of the balun's arms that cause the 180\({}^{\circ}\) phase shift. This asymmetry causes a slight deviation on the main lobe of the radiation pattern in the planar antenna. In the multilayer implementation, the influence of the balun in radiation is negligible, as a reflector element was used, and the main radiation lobe is vertically aligned with the directors.  
Furthermore, the impedance matching made on the planar antenna is very accurate, however, in the multilayer antenna, a small frequency shift was observed. This might be a consequence of the construction method of this antenna, which was very simple, just to demonstrate the concept. In fact, this vertically stacked Yagi-Uda antenna has its elements surrounded in substrate, and, possibly, there may have existed a slight difference between the practical and theoretical, used in simulation, and as a result, the minimum return loss happens at a slight different frequency than in the simulation.  
The concept of converting the planar into a multilayer structure, as shown in Table 3, is clearly promising, as not only it is possible to reduce the antenna's overall size, but also, improvements are seen, both in bandwidth and gain.
# An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks
## 4 Motivation and System Model
### System Model
#### 4.2.2 Mobile Users  
If a user wants to obtain service from the LBSP in a region, he must register himself to the SCS, and there are also three missions for the user. Firstly, he gets his pseudo-ID \(PID_{i}\) from SCS, and the pseudo-ID \(PID_{i}\) is unique and different from other users. Secondly, according to the BVHE parameters (\(n\), \(q\), \(\delta\), \(R\), \(R_{q}\)), the user generates his own public-private key pair (\(pk_{i}\), \(sk_{i}\)) as explained in Section 3.1. Thirdly, the user should ask for the corresponding kind of service category according to his preference from SCS at this moment. Note that, in this paper, there is not an anonymizer as a trusted intermediary who passes information between users and LBSP, and no dummy in the clustering process. Therefore, if a user wants to obtain service from the LBSP, he should firstly aggregate other users' requests to meet the conditions of k-anonymity and l-diversity. Then these requests would be sent together to the LBSP by their representative. In the clustering process of these users, when the clustering group finally achieves the condition of success (i.e., \(k\geq th_{k}\) and \(l\geq th_{l}\)), the representative of the clustering group firstly will inform these users to prepare their request packets. Then he will repackage their packets as an aggregated package. Finally, he will send the aggregated package to the LBSP of this region.
# Applications of Raman Microscopy/Spectroscopy-Based Techniques to Plant Disease Diagnosis
## 8 Raman Spectroscopy in Cancer Research  
Raman spectroscopy can be used for non-invasive diagnosis of cancer, allowing for early detection and treatment. The method provides molecular-level information on the chemical composition of cancer cells, enabling researchers to identify biomarkers of cancer. Applications of Raman spectroscopy in cancer diagnosis show that Raman spectroscopy can be used to diagnose cancer in tissues, understand the evolution of disease and improve prognostic evaluation [42; 62].  
In cancer diagnosis, one of the most important aspects of Raman spectroscopy is its sensitivity and specificity for identifying breast cancer at the asymptomatic or very early stages [42]. Moreover, at the early stage of cancer, in cancerous tissues compared to healthy tissues, (i) when cancerous lesions arise and their degree increases, the intensity of \(\beta\)-carotene decreases sharply and even almost disappears, and (ii) during cancer progression, the amounts of tryptophan, nucleic acids and proteins increase accordingly. Raman microspectroscopy was conducted in this context using an Alpha 500R confocal Raman microscopy system (WITec GmbH, Ulm, Germany) coupled with a continuous 532 nm semiconductor laser [63]. All the acquired spectral data were pre-processed by using the NWU-Spectral-Analysis (NWUSA) (Northwest University, Xi'an, China) Toolbox, a graphical user interface for Raman spectral processing [64]. Further, for Raman imaging processing, K-means cluster analysis (KCA) was adopted to identify and visualize the chemical components [63].  
Overall, we believe that Raman spectroscopy in plant disease diagnosis is a key tool, especially in early pre-symptomatic, middle pre-symptomatic and symptomatic plant disease for testing and studying indicators.
## Materials and Methods  
### Chemical and Reagents  
Graphene oxide (GO) was purchased from Graphene (San Sebastian, Spain). Manganese sulphate monohydrate (MnSO\({}_{4}\)\(\cdot\)H\({}_{2}\)O), polytetrafluoroethylene (PTFE), and carbon mesoporous were obtained from Sigma-Aldrich (Steinheim, Germany). Iron sulphate heptahydrate (FeSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O) and ethanol (95%) were purchased from HmbG (Hamburg, Germany) and John Kollin Corporation (Midothian, UK), respectively. Sodium sulphate (Na\({}_{2}\)SO\({}_{4}\)) was obtained from Merck Darmstadt, Germany. Hydrazine monohydrate (N\({}_{2}\)H\({}_{4}\)\(\cdot\)H\({}_{2}\)O) and nickel foam was purchased from Nacalai Tesque Inc. (Kyoto, Japan) and Goodfellow (Huntingdon, UK) respectively. Deionized (DI) water was supplied from a Millipore water system (Darmstadt, Germany).
# Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism
## 3 Main Model
### The Model Discussion  
In summary, the model has the following three main advantages.  
1. Effective fusion of multi-source information using second-hop neighbor information: The introduction of second-hop neighbor information in this model provides the model with richer context and auxiliary information, which helps to better understand the relationship between users and items and realizes the effective fusion of multiple information of users, items, and meta-paths. In this way, the model can understand user preferences and item characteristics more comprehensively, thus making up for the limitations of a single data source, further enhancing the generalization ability of the model, and improving the accuracy and diversity of recommendations.
2. Reinforcement learning mechanism: The model uses a negative sampling technique to learn model parameters, similar to the \"reward\" mechanism in reinforcement learning. Through negative sampling technology, the model can effectively learn under limited feedback, which helps the model to better adapt to the data distribution and improve the prediction ability of unobserved interactions.
3. Interpretability and efficiency: The model can provide users with an explicable reasoning process and enhance the trust and acceptance of recommendation results. For example, through the interpretation of meta-path and neighbor information, users can better understand how recommendations are made. The model structure is flexible and efficient, enabling fast training and reasoning on large datasets. This high efficiency enables the model to provide a personalized recommendation service to users more quickly and provides an effective solution to solve the problem of data sparsity and cold starts.
## 2 Materials and Methods
### Experimental Approach  
Figure 2 is the schematic diagram of the experimental system. The experimental system mainly consists of an inverted fluorescence microscope (IX-70, Olympus, Tokyo, Japan), an EMCCD camera (ixon+885, Andor, Orlando, FL, USA), a self-prepared microfluidic chip, a syringe pump (PUMP 33, Harvard Apparatus, Holliston, MA, USA), an injector (50 \(\upmu\)L, Hamilton, NV, USA), a pair of platinum wire electrodes (Jieyu Electronic Technology Co., Ltd., Shanghai, China), and an external high voltage power supply (PS 8000 2U, EPS, Germany). The image data were analyzed and processed using Image-J software  
Figure 1: Fabrication process of the microfluidic chip: (**a**) UV exposure; (**b**) casting the mold; (**c**) punching and bonding; (**d**) microfluidic chip; (**e**) local amplification of microchannel.  
([[http://rsbweb.nih.gov/ij/](http://rsbweb.nih.gov/ij/)]([http://rsbweb.nih.gov/ij/](http://rsbweb.nih.gov/ij/))). The laboratory temperature was maintained at 23 \({}^{\circ}\)C, and all experiments were conducted in a dark room.  
The experimental procedure can be described briefly as follows: Tris-HCl buffer was injected into the cis chamber using a syringe pump at a rate of 5 \(\mu\)L/h first. After about 20 min, the microchannels were filled with buffer, then the \(\lambda\)-DNA/YOYO-1 sample solution was injected into the cis chamber at a rate of 5 \(\mu\)L/h. After about 20 min, the injector was removed to relieve pressure in the inlet port and reduce the effect of pressure between the inlet and outlet ports. Finally, a voltage was applied at a pair of platinum electrodes to translocate the movement of the \(\lambda\)-DNA molecules through the microchannel. The external voltage on the electrodes was adjusted between 0 and 100 V. It should be noted that before the start of the experiment, the microfluidic chip was pre-degassed. The movement of DNA molecules in microchannels was recorded in real-time using an EMCCD and analyzed using Image-I to calculate the migration velocity of the DNA molecules. The specific procedure was as follows: The EMCCD was set with an exposure time of 50 ms and an acquisition interval of 100 ms to record experimental images. The coordinates of the DNA molecules were obtained from two consecutive images (using the midpoint of the spot to represent the location of the DNA molecule) and the displacement of the DNA molecules was calculated. The migration velocity of the DNA molecules was then determined using the displacement divided by the exposure time.
## 2 Materials and Methods
The process of loading, frequency change, and temperature was controlled by the corresponding measurement procedure. The deformation range of the component corresponding to voltage generation was selected on the basis of preliminary calibration tests. The test procedure was as follows: The temperature was raised according to the values presented in Table 1 in each stage before the temperature stabilization step began. After a period of temperature stabilization (\(t_{s}=10\) min), the tested piezoelectric transducer was subjected to concentric sinusoidal load at displacement control for 5 different frequencies presented in \[\frac{d^{2}\eta_{k}(t)}{dt^{2}}+2\xi\omega_{k}\frac{d\eta_{k}(t)}{dt}+\omega_{k }^{2}\eta_{k}(t)=f_{k}(t). \tag{15}\]4 \begin{table}
\begin{tabular}{l c c c c c} \hline \hline Frequencies to which the system was subjected (Hz) & 5 & 10 & 15 & 20 & 25 \\ Temperatures to which the system was exposed (\({}^{\circ}\)C) & 25 & 30 & 40 & 50 & 60 \\ \hline \hline \end{tabular} The manufacturer specifies an effective working temperature range from \(-35\) to \(+85\) degrees Celisus [30]. The Instron controller’s analogue output voltage signal was streamed to the National Instruments controller, which used the analogue output as the data acquisition trigger signal (changes in generated piezoelectric transducer electric voltage). The laboratory stand was controlled by a displacement control, which ensured that the transducer’s deformation value remained constant at its maximum. The tests were performed at various temperatures and frequencies, and the experimental results revealed that the temperature effects were strongly frequency dependent. The peak value of the voltage drop on the connected resistor was determined using the parameters of the analyzed system, which are summarized in Table 2.  
\end{table} 
Table 1: The frequency and temperature ranges to which laminated MFC piezoelectric transducers were subjected. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**No.** & **Description** & **Notation (Units)** & **Value** \\ \hline
1 & Copper fiber volume & \(\gamma\) (dimensionless) & 0.19 \\
2 & Capacity & \(C_{p}\left(nF\right)\) & 84.04 \\
3 & PZT fiber thickness & \(t_{p}\left[nm\right]\) & 127 \\
4 & Active length of MFC & \(t_{p}\left[nm\right]\) & 85 \\
5 & Active width of MFC & \(b_{p}\left[nm\right]\) & 14 \\
6 & Dielectric constant & \(e_{33}^{e}\left[\frac{F}{n}\right]\) & \(1.504\times 10^{-8}\) \\
7 & Elastic compliance constant & \(s_{11}^{E}\left[\frac{m^{2}}{N}\right]\) & \(16.4\times 10^{-12}\) \\
8 & Piezoelectric constant & \(d_{31}\left[\frac{RC}{N}\right]\) & \(-2.1\times 10^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The tested MFC transducer material properties and geometrical parameters [30]. Every two minutes of stabilization time, the frequency was increased by 5 Hz. The procedure was carried out beginning at 25 \({}^{\circ}\)C and progressing through temperatures shown in Table 1 (all values of excitation frequency were used in case of each temperature value).
## 1 Introduction
Satellite SIF products have been retrieved from several space-borneinstruments, such as Global Ozone Monitoring Experiment 2 (GOME-2) [56], Greenhouse Gases Observing Satellite (GOSAT) [54; 57], and Scanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) [58]. Many studies have proved that satellite SIF products are able to track vegetation production (e.g., gross primary production, GPP) for various ecosystems [49; 59; 60; 61; 62; 63]. The integration of satellite SIF and process-based models could improve the mechanistic understanding and monitoring of crop productivity [48; 64]. However, the application of satellite SIF data for estimating crop yield using the statistical approach has less been practiced, mainly owing to the low spatial resolution of the above-mentioned SIF data. For example, the footprint sizes of GOME-2 and SCIAMACHY SIF are 40 km \(\times\) 80 km and 30 km \(\times\) 60 km, respectively. Within a pixel of such large sizes, several crops types might coexist, inducing a big challenge in estimating crop yield using coarse resolution SIF data in heterogeneous regions since relationships between SIF and vegetation production change with crop types [48; 65; 66]. Recently, [PERSON] et al. [24] demonstrated the applicability of GOME-2 and SCIAMACHY SIF data in conjunction with climate data in predicting wheat yield in Australia, in which wheat is dominantly distributed. Fortunately, the recent advent of the Orbiting Carbon Observatory-2 (OCO-2) [55] and TROPOspheric Monitoring Instrument (TROPOMI) [67] enables relative high-resolution SIF retrievals with footprint areas of 1.3 km \(\times\) 2.25 km and 3.5 km \(\times\) 7 km, respectively. At such spatial resolutions, it is possible to develop SIF-based statistical yield prediction models with the differentiation of different types of crops, such as C3 and C4 crops. However, OCO-2 SIF data has limitations for estimating crop yields, such as discrete footprints and low revisiting coverage. Therefore, it is worth investigating whether OCO-2 SIF data is applicable for monitoring crop yield at regional scales. In this study, the ability of OCO-2 SIF to monitor the spatial variations of soybean and maize yields in the Midwest 12 states of the U.S.A. was investigated. The DNN method was employed to construct prediction models of yields with OCO-2 SIF, MODIS EVI, climate variables, and their different combinations as inputs. The performance of different models was evaluated using census data of county-level soybean and maize yields. The importance of various variables during different periods in predicting crop yields was assessed. The specific objectives of this study are: (1) to assess the ability of OCO-2 SIF to monitor the spatial variation of maize and soybean yields relative to EVI; (2) to compare the ability of remote sensing and climate data to monitor the spatial variations of corn and soybean yields in the Midwestern US; (3) to identify key periods when remote sensing and climate data are important for predicting crop yields.
## 4 Results
### Synthetic Experiments and Sensitivity Analysis
#### 4.1.1 Flood Irrigation
When no constraint is applied, a clear decrease of the TruPosRat is observed when going from 0% error (0.71) to 10% (0.52). Likewise, at 5 days, the minimum value is 0.61 obtained at 10% error while it remains above 0.7 for the other error levels. As a conclusion, these results demonstrate: (1) the importance of constraining the timing of irrigation for the flooding technique in particular for the shorter window of 3 days; a 5-day constraint being a good trade-off; (2) shorter windows allow better identification of irrigation events, especially in complex conditions where irrigation overlaps with significant rainfall events.
## 5 Conclusions  
The public data from the Spanish cadaster offers a lot of useful information for urban planners. Nevertheless, its usefulness is restricted by the adoption of non-standard storage formats and the lack of semantically clear concepts in some of its sections. The presented methodological approach seeks to extract meaningful information from cadastral data using representational and semantic strategies.  
The visualization techniques developed allowed better communication and understanding of the information stored in the databases and their interrelationships:  
* The 3D visualization techniques allowed the discovery of patterns not obvious even for trained professionals, using the third dimension to convey information not possible in 2D, with the third dimension helping understand conceptual data.
* The 2D representation of height and use information onto the horizontal plan resulted in a very compact and synthetic visualization of the relation between the streets skyline, the planning regulations and the built environment from a pedestrian point of view.
* For data without associated cartography, multiple visualization techniques were used to extract meaningful data, in the form of charts or through aggregation operations into higher order geometric entities.
* To represent temporal information, a visualization similar to population pyramids was used for the age of buildings.  
The subject described by both the urban regulations and the cadaster is the urban built environment; however, they were conceived to serve different purposes and therefore are structured differently. To be able to integrate both sources to get better insights on the evolution of the morphology of the city in renovation processes, it was necessary to translate both classes of information into a common conceptual framework.  
Due to the nuances in the interpretation of building regulations and the legacy format used by the cadaster, the translation operation required a team that was able to work with both data sources. In the future, if a semantic Spatial Data Infrastructure (SDI) is in place, these databases could be automatically queried and their information extracted with less ambiguity [59], allowing a better collaboration of the professionals involved. However, the concepts in this semantic SDI need to be defined by the professionals involved in the planning process; this paper tries to assess some of the concepts that could be included in such SDI, in parallel to the work being developed by the Building Information Modeling (BIM) community to integrate building licensing [60] into their workflow.