esa-sceva/satcom-chunk-collection huggingface dataset:
# Laboratory Evaluation of Eco-Friendly Pervious Concrete Pavement Material Containing Silica Fume
## 4 Conclusions  
Sustainable pavement materials--silica fume-modified pervious concrete and control pervious concrete--were prepared and investigated in the lab. Porosity, permeability, mechanical properties, and freeze-thaw resistance of pervious concrete were tested. The effects of silica fumes on pervious concrete properties were analyzed. Based on the test results, following conclusions can be drawn.  
* The silica fume-modified pervious concrete designed with equivalent volume replacement of cement will not affect the effective porosity, thus the permeability of silica fume-modified pervious concrete hardly changed with the amount of silica fumes compared to the unmodified control pervious concrete.
* The compressive and flexural strengths of pervious concrete can be improved by the addition of silica fumes. The higher the content of silica fumes, the higher the compressive and flexural strengths.
* The addition of silica fumes significantly improves the freeze-thaw resistance of pervious concrete. The compressive strength losses of silica fume-modified pervious concrete under freeze-thaw cycles are obviously lower than the unmodified control pervious concrete. The freeze-thaw resistance of silica fume-modified pervious concrete increases with the increasing silica fume content, and the 12% silica fume is recommended for freeze-thaw durability of pervious concrete.  
Conceptualization: H.L.; investigation: [PERSON], H.L., and [PERSON]; writing (original draft): [PERSON], [PERSON], W.W., and L.W.; writing (review and editing): [PERSON].  
Funding: This research was funded by the Science Technology Development Program of Jilin Province (grant number 20180201026 SF), the Transportation Science and Technology Program of Jilin Province (grant number 2018-1-9), and the Education Department's \"13 th Five-Year\" Science and Technology Program of Jilin Province (grant number JJKH20190015 KJ).  
Acknowledgments: The authors would like to appreciate the anonymous reviewers for their constructive suggestions and comments to improve the quality of this paper.  
Conflicts of Interest: The authors declare no conflicts of interest.  
References
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 6 Types of Information Transmitted
### Video Streaming  
This requires a high-speed internet connection such as UMTS or higher for viewing online content over the internet without the need to download media files. Such media content may include medical TV shows, healthcare educational content, and live event broadcasts. Communication protocols used in video streaming include HTTP Live Streaming protocol (HLS), Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), and MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH). A major challenge with live video streaming is transmission delay. The work of [127] proposed that the H.265 codec provides better quality and more efficient video compression and decompression techniques with reduced video transmission delay compared to the H.264 codec.
# Can Urban Environmental Problems Be Accurately Identified? A Complaint Text Mining Method
## 3 Results and Discussion
### The Sentiment of Environmental Complaints  
The box plot (Figure 4) shows that the mean (air: \(-0.11\); water: \(-0.10\); noise: \(-0.10\); waste: \(-0.04\); EM radiation: \(-0.15\); light: \(-0.18\)) and median (air: \(-0.09\); water: \(-0.08\); noise: \(-0.08\); waste: \(-0.04\); EM radiation: \(-0.10\); light: \(-0.19\)) of all types of environmental complaint sentiment are both lower than zero, which indicates that the complainants' overall sentiment tendency is negative. Comparing the mean and median of various environmental complaints, electromagnetic radiation and light have the lowest value. The sentiment value distribution of electromagnetic radiation is the most scattered (0.30), followed by light (0.23), which is presumably due to the wide differences between cognitive and individual. There is little difference in the sentiment value distribution of air, water, and noise pollution complaints.
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 3 The Proposed Approach
### Multi-Scale Feature Extraction
#### 3.2.2 The Coarse-to-Fine Pipeline  
To efficiently handle large deformations while preserving anatomical completeness, we progressively downsample the input point clouds using farthest point sampling (FPS) [39], ensuring that the sampled points are broadly distributed across the lung's geometry without over-concentrating on local regions. Since the original point clouds were extracted primarily from critical anatomical structures such as bronchial trees and vascular networks, FPS maintains essential structural coverage with high fidelity. This results in a hierarchical set of point clouds \(\{\mathcal{X}^{(l)}\}_{l=1}^{L}\), from the finest resolution (\(l=1\)) to the coarsest resolution (\(l=L\)), where each sampled point at the coarsest level encodes a broader anatomical region of the lung, facilitating global alignment. This multi-scale representation is critical for our attention-driven alignment strategy, where global coarse-level correspondences are first established and subsequently refined at higher resolutions.
## 2 Materials and Methods
### Methods of Signal Analysis  
The original hydrophone data include time information and signal strength information, but the most notable feature of _Cetaceans_' acoustic signals is their frequency information. Therefore, we must transform the original time domain data into time-frequency domain data. In this research, we chose three different methods to process the data, including one manual method and two digital methods, STFT and WT. The manual method and STFT are commonly used for _Cetacean_ acoustic signal detection. WT is a new method that has been applied for the processing of non-stationary signals or filters. We aimed to determine whether WT was valuable for _Cetacean_ acoustic research by comparing these three methods.
## 3 Design Characteristics
### Element Selection  
The element selection is a key step in reflectarray design, because it determines the reflection efficiency and the bandwidth of the antenna. The shape of each element must be selected and the change in phase must be characterized as a function of a change in dimensions or rotation, which is called the \"S\" curve of the element. This curve must cover a phase-shift of at least \(\pm 180^{\circ}\) with a smooth slope [12].  
In order to cover all the needed range of phases, with a smooth slope, three different elements are employed: circle plus triangle and ring with two different widths \(w_{ring}\) (Figure 5 and Table 1). The patch elements are located over an FR4 substrate 1.6 mm thick which is over a 6 mm thick air substrate. The ground plane is located below the air substrate.  
The S-curves of the selected elements were simulated by unit cell analysis configured for horizontal polarization in the final design (Figure 6). The reflection phase varies significantly with the angle of incidence, so it must be taken into account in the reflectarray design process. On the other hand, there is a gap of non-covered phases within the range \([-120,-90]\) (Figure 6a). The proposed designed method will solve this problem by not selecting these phases for the central elements.  
Figure 5: Elements selected for the design.  
Figure 6: S-curve of selected elements for normal incidence and \(\Theta_{inc}=20^{\circ}\), highlighting the valid ones in each case according to the maximum of the allowed losses.
# Nano Aerial Vehicles for Tree Pollination
## 2 Related Works
### Nano Aerial Vehicles
#### 2.2.2 Communication  
The Crazyflie 2.0 platform has an integrated nRF51822 component that enables communication with a multiprotocol 2.4 GHz radio. The latest generation of this platform does not require any additional components for the NAV to establish communication.  
For radio communication, enhancements can be added with the installation of a Crazyradio PA [28], which has a power amplifier of 20 dBm, thereby increasing the signal range ([[https://www.bitcraze.io/products/crazyradio-pa/](https://www.bitcraze.io/products/crazyradio-pa/)]([https://www.bitcraze.io/products/crazyradio-pa/](https://www.bitcraze.io/products/crazyradio-pa/)), accessed on 27 March 2023).
## 3 Experimental Regime
### Analysis of Adhesion Strength by Adhesion Area
#### 3.6.2 AR 80 \(\pm\) 2% Specim  
For the AR 80 \(\pm\) 2% specimens, out of the 40 tile pieces of the specimen, 26 (65% of the entire tile surface) of No.1 specimens showed stable adhesion, and the pieces that satisfied the ISO standard requirement (0.5 N/mm\({}^{2}\)) were eleven (42.3% of the stable attached pieces). For the No. 2 specimens, 15 (37.5% of the entire tile surface) showed stable adhesion, and 13 (54.2% of the stable attached pieces) satisfied the ISO standard requirement. For the No. 3 specimens, 24 pieces (60% of the entire tile surface) showed stable adhesion, and the pieces that satisfied the ISO standard requirement were 13 (54.2% of the stable attached pieces). The overall average adhesion strength of No.1, No. 2, and No. 3 specimens were, respectively, 0.32 N/mm\({}^{2}\), 0.32 N/mm\({}^{2}\), and 0.29 N/mm\({}^{2}\), resulting in a comprehensive average of 0.31 N/mm\({}^{2}\). When considering the average of only the pieces with stable adhesion, the average was, respectively, 0.49 N/mm\({}^{2}\), 0.88 N/mm\({}^{2}\), and 0.48 N/mm\({}^{2}\), resulting in a comprehensive average of 0.61 N/mm\({}^{2}\). Refer to Table 8 and Figure 15 below.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Specimen Number** & **Coordinates** & **x1** & **x2** & **x3** & **x4** & **x5** & **x6** & **x7** & **x8** & **Avg.** & **Results** \\ \hline \multirow{8}{*}{1 st Specimen} & y1 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.13 & 0.00 & 0.02 & \multirow{8}{*}{Total Avg.} \\  & y2 & 0.00 & 0.20 & 0.19 & 0.16 & 0.40 & 0.17 & 0.99 & 0.13 & 0.28 & \\  & y3 & 0.00 & 0.71 & 1.06 & 1.14 & 0.99 & 0.69 & 1.19 & 0.00 & 0.72 & \\  & y4 & 0.14 & 0.81 & 0.45 & 0.81 & 0.59 & 0.25 & 0.78 & 0.13 & 0.50 & 0.32 N/mm\({}^{2}\) \\  & y5 & 0.14 & 0.15 & 0.00 & 0.14 & 0.00 & 0.16 & 0.00 & 0.00 & 0.07 & 26 Pieces measured \\  & Avg. & 0.06 & 0.37 & 0.34 & 0.45 & 0.40 & 0.25 & 0.62 & 0.05 & 0.32 & \\  & y1 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & \\  & y2 & 0.00 & 0.00 & 0.85 & 1.53 & 1.29 & 0.57 & 0.00 & 0.00 & 0.53 & \\
2 nd Specimen & y3 & 0.00 & 0.00 & 0.98 & 1.12 & 1.44 & 0.75 & 0.24 & 0.00 & 0.57 & Total Avg. \\  & y4 & 0.00 & 0.00 & 0.41 & 0.74 & 1.19 & 0.90 & 0.22 & 0.00 & 0.43 & 0.32 N/mm\({}^{2}\) \\  & y5 & 0.00 & 0.00 & 0.00 & 0.52 & 0.00 & 0.00 & 0.00 & 0.00 & 0.07 & 15 Pieces Measured \\  & Avg. & 0.00 & 0.00 & 0.45 & 0.78 & 0.78 & 0.44 & 0.09 & 0.00 & 0.32 & \\  & y1 & 0.12 & 0.23 & 0.00 & 0.00 & 0.00 & 0.06 & 0.00 & 0.00 & 0.05 & \\  & y2 & 0.00 & 0.34 & 0.17 & 0.16 & 0.77 & 0.35 & 0.71 & 0.00 & 0.31 & \\  & y3 & 0.00 & 0.65 & 0.81 & 0.88 & 0.44 & 0.54 & 0.65 & 0.00 & 0.50 & 0.29 N/mm\({}^{2}\) \\  & y4 & 0.23 & 0.74 & 0.55 & 0.97 & 0.71 & 0.77 & 0.59 & 0.00 & 0.57 & 24 Pieces Measured \\  & & \(\
u\)5 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.06 & 0.08 & 0.02 & \\  & Avg. & 0.07 & 0.39 & 0.31 & 0.40 & 0.38 & 0.34 & 0.40 & 0.02 & 0.29 & \\ \hline \end{tabular}
\end{table}
Table 8: AR 80 \(\pm\) 2% specimen result.  
Figure 14: AR 60 \(\pm\) 2% specimen adhesion strength per piece coordinate (average).
# Wavelength Selection for Satellite Quantum Key Distribution
## 3 Channel Model
### Background Noise  
For a given temporal filter \(\Delta t\), spectral filter \(\Delta\lambda\) and spatial filter field of view \(\Omega_{FOV}\), the number of sky noise photons, \(N_{b}\), entering the receiver is described by the following radiometric expression [20]:  
\[N_{b}=\frac{H_{b}\Omega_{FOV}\pi D_{r}^{2}\lambda\Delta\lambda\Delta t}{4 hc} \tag{7}\]  
where \(H_{b}\) is the spectral radiance shown in Figure 1, \(h\) is [PERSON]'s constant, and \(c\) is the speed of light. It is important to note the quadratic relationship between \(D_{r}\) and \(N_{b}\). Although increasing the receiver diameter increases the channel efficiency, it also increases the number of noise photons entering the detector. This places upper limitations on the secret key rate generation for each wavelength, which is determined by the atmospheric transmittance and spectral radiance at that channel. Despite reductions in spectral radiance, the responsivity of photon detectors is higher at longer wavelengths, resulting in more  
Figure 2: The intensity profile for the beam at initialization (**left**) and the resulting wavefront after a 500 km propagation including 20 km of turbulence when the wavelength is 1.55 μm (**centre**) and 10 μm (**right**).  
noise photons incident on the detector. The background probability, \(Y_{0}\), is the probability of a noise photon entering the detector [59]:  
\[Y_{0}=N_{b}\ \eta_{det}\ \eta_{opt}+4f_{dark}\ \Delta t \tag{8}\]  
and it is dominated by \(N_{b}\) but also includes a contribution from detector dark counts \(f_{dark}\).
## 1 Introduction
A large number of phytopathogenic fungi have been used to investigate the antifungal potential of bacterial volatiles. However, the response of strawberry vascular wilt-causing agents to these volatile signals has largely been ignored in previous studies, despite the heavy losses they cause in strawberry production. The objectives of this study were three-fold: (i) to investigate the response of two fungal vascular wilt pathogens to bacterial volatiles, (ii) to determine the chemical composition of bacterial VOCs, and (iii) to identify the bioactive compounds responsible for the antifungal effects. To achieve these aims, we isolated 73 strains of bacteria from maize straw compost tea and assessed their volatile-mediated effects on the mycelial growth of _V. dahliae_ and _F. oxysporum_. From the above isolates, we selected _B. velezensis_ CT32, which exhibited the highest antagonistic activity through the production of antifungal VOCs. Moreover, the VOCs produced by strain CT32 were qualitatively analysed by headspace solid-phase microextraction/gas chromatography-mass spectrometry (HS-SPME-GC-MS) and tested for their antifungal activity in vitro against _V. dahliae_ and _F. oxysporum_.
# The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products
## 4 Case Studies
### Wind Field  
The customized processing workflow of wind field consists of seven steps. The workflow has three inputs that are a file list of Level 2 data of the HY-2A scatteromter in HDF5 format, a polygon representing the AOI, and a name of the output thematic map, while the outputs of the workflow include a wind field product file in HDF5 format, a wind speed raster dataset in TIFF format, a wind field point feature class in Shapefile format, and a thematic map in JPEG format. The workflow reads the level-2 HDF files of the HY-2A scatterometer first, and converts the wind direction values and wind speed values to a point feature class. Then, it extracts the data based on the AOI. With the inverse distance weighted interpolation algorithm, the wind speed values and wind direction values are converted to raster datasets. If necessary, the workflow can mosaic the multiple raster datasets into a new raster dataset. Using a gridding algorithm with a 1/4-degree interval, the raster dataset can be mapped to a uniform grid. Finally, the thematic map can be exported for data visualization. Figure 5 shows a thematic map of a single HY-2A swath for 25 March 2015.
## 6 Performance Evaluation
### Experiment 4: Link Failure Scenario for Topology 3  
In this experiment, we also analyzed the response of the Thread network to link failure. We performed this experiment over Topology 3, where the connection between Router 1 and Leader was broken. In a network that is not based on mesh, this link failure would leave Child 1 and Router 1 isolated. As Thread is a mesh-based topology, it routes the traffic from Child 1 through Router 1 to another router as per Thread's routing algorithm. This makes Thread more reliable and resilient, providing a better choice for sensitive applications which can not tolerate link breakage. Figure 18 shows the network performance when the communication is set up between Child 1 and the Leader under both normal and link failure conditions. We can see that the jitter is increased in the link failure scenario as the traffic has to be routed through the network taking a longer route than its original path. We observed that the jitter decreased as the payload size increased, this happens because, with a small packet size, more datagrams are transmitted and the network becomes congested, as discussed in Section 6.2 in detail. We also analyzed the performance of the network in terms of packet loss, as shown in Figure 19. Even with a link failure, it can be observed that the network is still able to transfer data; however, it will experience more packet losses due to longer routes.  
Figure 19: Packet loss in link failure scenario for Topology 3.  
Figure 18: Jitter in link failure scenario for Topology 3.
# A Robust Dual-Platform GMTI Method against Nonuniform Clutter
## 3 Results and Analysis
### Application in Nonuniform Power Environment  
In this section, the ANMF detector structure was adopted to investigate the influence of different CCM estimation methods on the detection performance. The detection flowchart is shown in Figure 14.  
The simulated echoes of the background without moving target are shown in Figure 15a. A moving target is located in the water area at the junction between land and water. The sim  
Figure 14: The detection flowchart.  
Figure 13: STAP performance indexes. (**a**) Output SCNR Loss Diagram. (**b**) SCNR improvement factor. (**c**) MVD calculation of traditional method (**d**) MVD calculation of proposed method.  
the detection of multiple targets as follows. Seven targets were set, and the total Capon spectrum of the clutter and targets is shown in Figure 19. In the space-time two-dimensional domain, the position of the clutter ridge on the diagonal can be seen, while the other seven targets are scattered in the domain. Two methods were used to estimate the CCM, so as to set the threshold. The detection results are shown in Figure 20. From the two pictures, we can see that our proposed method detected six moving targets, which is relatively more accurate than the traditional method. This also shows the effectiveness of the method proposed in this paper.  
Finally, we analyzed the detection probability of the two methods under different SCNR conditions. Using the false alarm probability of \(PFA=10^{-2}\), the detection probability curves of the two methods can be obtained, as shown in Figure 21. It can be seen that the  
Figure 19: The Capon of several targets.  
Figure 20: The results after detection, when there are multiple targets: (**a**) traditional method, (**b**) proposed method.  
Figure 18: The results after detection, when there are two targets: (**a**) traditional method; (**b**) proposed method.  
detection probability of the proposed method is higher, which also proves the effectiveness of the proposed method.
###### Abstract  
Accurately assessing terrestrial gross primary productivity (GPP) is crucial for characterizing the climate-carbon cycle. Remotely sensing the photochemical reflectance index (PRI) across vegetation functional types and spatiotemporal scales has received increasing attention for monitoring photosynthetic performance and simulating GPP over the last two decades. The factors confounding PRI variation, especially on long timescales, however, require the improvement of PRI understanding to generalize its use for estimating carbon uptake. In this review, we summarize the most recent publications that have reported the factors affecting PRI variation across diurnal and seasonal scales at foliar, canopy and ecosystem levels; synthesize the reported correlations between PRI and ecophysiological variables, particularly with radiation-use efficiency (RUE) and net carbon uptake; and analyze the improvements in PRI implementation. Long-term variation of PRI could be attributed to changes in the size of constitutive pigment pools instead of xanthophyll de-epoxidation, which controls the facultative short-term changes in PRI. Structural changes at canopy and ecosystem levels can also affect PRI variation. Our review of the scientific literature on PRI suggests that PRI is a good proxy of photosynthetic efficiency at different spatial and temporal scales. Correcting PRI by decreasing the influence of physical or physiological factors on PRI greatly strengthens the relationships between PRI and RUE and GPP. Combining PRI with solar-induced fluorescence (SIF) and optical indices for green biomass offers additional prospects.  
g r r 1 (2016) 8, 677; doi:10.3390/rs8090677  
1 CREAF, Center for Ecological Research and Forestry Applications, Cerdanyola del Valles, Barcelona 08193, Catalonia, Spain; [EMAIL_ADDRESS] (I.F.); [EMAIL_ADDRESS] (J.P.)  
CSIC, Global Ecology Unit CREAF-CSIC-UAB, Cerdanyola del Valles, Barcelona 08193, Catalonia, Spain  
Catedra de Forrajicultura, Facultad de Agronomia, University of Buenos Aires, IFEVA/CONICET, Buenos Aires C1417 DSE, Argentina; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]; Tel.: +34-935-81-3355  
Academic Editors: [PERSON], [PERSON], [PERSON] and [PERSON]  
Received: 28 June 2016; Accepted: 15 August 2016; Published: 23 August 2016
## 2 Literature Review
### Research on the Relationship between Behavior and Space  
In the wave of reflection on the oversimplification of spatial issues in traditional urban planning, scholars have begun to examine the role of people, emphasizing individual micro behaviors, the interaction between urban space and human spatial behavior, and presenting different behaviors and environments in a differentiated manner. In the study of the interaction between behavior and space, the following analysis models have been formed.  
Spatial-behavioral interaction model at different spatial scales: The study of human behavior includes micro-level research at the individual level and macro-level research at the aggregate level. The micro level includes community-scale and street-scale spaces, including streets, squares, green spaces, parks, shopping malls, etc. [PERSON] [24] (2022) studied the correlation between street quality and pedestrian walking speed and flow. At the macro level, it includes the urban scale and intra-urban scale, specifically exploring different behavioral patterns between cities and between different regions within cities and understanding the spatial characteristics of residents' behavior [4; 5; 6; 20].  
The spatial-behavioral interaction model from the perspective of different groups: By comparing different spaces and different groups, the specificity of the interaction pattern between group behavior and space is explored. Specifically, it explores behavioral differences from multiple perspectives such as gender, age, income, and mobility and explains the interaction with space. For example, [PERSON] [25] proposed that there is a clear differentiation in the clustering behavior of different income groups. [PERSON] [26] (2015) analyzed the impact of the physical environment of urban streets on children's behavior. [PERSON] et al. [9] (2022) studied the factors influencing the risky behavior of the elderly in residential spaces. Finally, [PERSON] et al. [11] (2022) observed the use preferences of the elderly in different park spaces.  
This study explores the differences in behavior and space of different groups of people by using computer vision to detect people's daily behavior in community square spaces on a microscopic scale.