esa-sceva/satcom-chunk-collection huggingface dataset:
# A Tutorial on Retroreflectors and Arrays Used in Satellite and Lunar Laser Ranging
## 1 Introduction  
The first successful Satellite Laser Ranging (SLR) experiment was carried out on 31 October 1964 by Dr. [PERSON] and his team at the NASA Goddard Space Flight Center in Greenbelt, Maryland, USA [1]. In this first experiment, the NASA Explorer 22B satellite was equipped with an array of retroreflectors designed to reflect laser pulses from a ground station back to their point of origin. The roundtrip distance to the satellite can then be determined by multiplying the roundtrip transit time by the speed of light. In 1969, a team led by Prof. [PERSON] of the University of Maryland succeeded in ranging to a flat panel of 100 retroreflectors left on the lunar surface by NASA's Apollo 11 crew [2]. In the almost six decades since these early experiments, dozens of Earth-orbiting satellites carrying retroreflector arrays have been launched to support a wide variety of scientific investigations, and five arrays have been placed on the lunar surface by three manned US Apollo missions (11, 14, and 15) and two unmanned Soviet Lunakhod (17 and 21) missions carrying arrays provided by France. The present author has previously published a comprehensive review of the active ground-based SLR hardware and operations [3], whereas this paper will concentrate on the requirements placed on the passive spaceborne segment, i.e., the retroreflector arrays. For a more complete discussion of recent SLR capabilities and trends, the reader is referred to the following reviews [4, 5].
## 4 Results  
Since the focus of this study is to delineate flooded buildings, the availability of a building footprint of Houston is a pre-requisite in our approach, as depicted in Figure 1.  
Applying the approach proposed in [PERSON] et al. (2019), we obtained an S-1 building map at 5 and 20 m resolutions, depending on the S-1 operation mode considered. A sample of the product obtained is displayed in Figure 3c,d. We notice that for both resolutions, the main built-up areas are delineated, although some differences are present due to the different spatial resolution. As shown in Figure 3d, the map derived from SM contains more buildings, as its higher resolution also allows the detection of small houses. From Figure 3e, it is possible to observe that the area is quite vegetated. This means that in a 20 m resolution pixel, the double-bounce contribution is mixed with that from the vegetation. This is an issue that needs to be taken into consideration in the evaluation of the final floodwater map.  
To quantitatively analyze the resulting floodwater maps, we counted the number of pixels that were detected as flooded in bare soil areas, i.e., 2,910,880 and 43,533,877 in case of IW and SM  
Figure 3: Building footprint derived from S-1 data over a Houston area: (**a**) IW, 20 m, (**b**) SM, 5 m SAR multi-temporal filtered image. (**c**) IW, 20 m, (**d**) SM, 5 m; buildings are depicted in red while the non-building areas are depicted in white. (**e**) Google satellite imagery.  
images, respectively, and those added by the novel algorithm able to detect flooding in urban areas, which amounted to 322,147 and 1,687,048 pixels in the case of IW and SM acquisition, respectively. The affected areas increased by 11% (IW) and 4% (SM), which is quite relevant, especially if we consider that urban areas are the most sensitive in terms of risk and impact. The percentages are different because of the different spatial resolutions and, especially, the different spatial coverage (see Figure 2).  
In the following section, we present qualitative results for three case studies that were extracted from the data described above.
# An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevine
## 2 Materials and Methods
### Model Integration to the Raspberry Pi 4 for Online Disease Assessment
#### 2.3.2 Model Integration  
After the training process, the trained models with the highest validation accuracy (the accuracy of the model in the end of the validation phase, before the model deployment) on the disease identification on the field conditions from both MobileNet V2 and EfficientNet B0 were chosen to be used for the online deployment. Prior to the integration of the models with the RPi, they need to be converted to a format that can be easily deployable [65, 66, 67]. The saved models were subjected to post-training quantization. This technique involves converting a pretrained TensorFlow model into a TensorFlow Lite format using the TensorFlow Lite Converter, enabling quantization of the model's parameters. By applying this approach, the model size is reduced and CPU and hardware acceleration latency are improved while maintaining satisfactory levels of classification accuracy [68]. This quantization technique better balances the trade-off of model size reduction and potential accuracy degradation as the complexity and the architecture of the deep learning models can influence the effectiveness and compatibility of the different approaches. Additionally, other factors that should be considered are the hardware constraints and requirements. For these reasons, the full integer quantization approach was considered as it is supported by microcontrollers such as the RPi, providing a 4-fold model compression and maintaining most of the models' predictive ability.  
After generating the quantized model, it was imported into the RPi, which was equipped with a camera and a GPS module. Two image acquisition approaches were considered for RPi integration. The first approach involved using the RPi as a standalone handheld device, acquiring images directly from the mounted RGB camera module. The second approach involved incorporating the RPi onto an unmanned ground vehicle (UGV) and making use of its own camera sensors. For this reason, a Robot Operating System (ROS) node was developed to subscribe to an external camera's specified topic on the UGV platform and publish the results. In both cases, the imported images from the camera underwent the same image compression techniques applied during model training, including compression to a size of 224 \(\times\) 224 pixels and image normalisation within the range of [0, 1]. The output of the RPi model consisted of string-type messages containing the predicted class for each captured image and the corresponding GPS coordinates.
## 2 Seismo-Tectonic Environment of UB Area
### The Songino Fault  
The Songino Fault (see location in Figure 1) was discovered in 2012 by a geomorphological approach ([PERSON] et al., 2012 [2]). Branched at the northeastern end of the Khustai active fault, it is about 20 km long and situated at 25 km southwest of UB. The geomorphology of the Songino fault is clearly visible on a high-resolution satellite image, with a smoothed but high scarp observed in the northwestern part of the fault (Figure 2). From northwest to southeast, the height of the Songino scarp gradually changes. While being quite high (about 20 m) at its northwestern end, it decreases towards the south before almost disappearing in some places in the middle. By continuing to the southeast, it gradually increases again up to about 0.5-1.5 m high. This is due to the curvature of the fault. The rather linear central part, oriented NW-SE with left lateral slip, induces at the northwestern end, nearly NS, a transgressional deformation and at the southeastern end, nearly EW, a transgressional deformation (Figure 2b).  
No large earthquake has historically been reported on this fault; however, its structure gives indications of activity and a potential large seismic event (M 6.5) which could impact the city of UB.
## 3 Results
### Radiometric Indices  
The training data were divided into crop classes, and the mean value of each radiometric index was calculated for each class. The temporal profile of each radiometric index was obtained using these mean values. NDVI and LAI temporal profiles (Figure 8) show a clear pattern for the summer crop (e.g., maize), with the peak values in the summer season. All the profiles are coherent with the crop calendar shown in Figure 2. The only exception is represented by winter cereal. This class shows a maximum peak of NDVI in August (DOY 223) and minimum values in November and December (DOY's 313 and 363), while according to the crop calendar, these peaks should occur in April-May and August,  
Figure 7: Intermediate crop classification map, resulting from MESMA.  
respectively. This behavior can be explained by the local agricultural practice that is well identifiable in the NDVI multi-temporal profile (Figure 9): winter cereals (WC), mainly ray and durum wheat, are sown in October/November, and the plants sprout in January. The cereals are harvested in May-June, and forage is sown immediately after. Finally, they are harvested in August, and the cycle begins again.  
Figure 8: Temporal profiles of (**a**) NDVI and (**b**) LAI for each class of crop.  
Figure 9: NDVI temporal behavior of winter cereals. The trend can be explained by the crop rotation between winter cereals (i.e., ray and durum wheat) and forage.  
LAI temporal profiles showed a slightly different trend than NDVI, mainly due to the structure of the plants. Using this index, we obtained added-value information on crop typologies that maximized class (Figure 8b).  
The temporal BSI trend provided information on the presence of bare soil. The profile in Figure 10 shows that summer crops (e.g., maize) have the minimum value of this index in summer months, consistently with their growing season according to the crop calendar. Moreover, tree crops (TCs) showed a different behavior, with a significantly lower presence of bare soil.
## 5 Proposed Heuristic
### Stage I and II: Determining Sojourn Locations and RPs
#### 5.1.3 Sojourn Locations Selection
The procedure of scoring is to consider the current sojourn location and the candidate sojourn locations. Then the function for each candidate sojourn location identifies the number of non-intersected RPs with the candidate sojourn location. And finally if the score for other candidate sojourn locations becomes \"zero\"; it means the selected sojourn locations in the sub-trajectory can cover all of the RPs. Figure 6: An example of two RPs in the communication range of each other which are located in the periphery of the trajectory. ```
Input: i) RP-Nodes, \(\Delta=\{\delta_{1},...,\delta_{m}\}\), \(m\leq|S|\); ii) The trajectory of the mobile-sink, \(\Gamma=\{\gamma_{1},\gamma_{2},...,\gamma_{l}\}\), \(l\leq|\Delta|\); Output: i) Sojourn locations, \(\Psi=\{\psi_{1},...,\psi_{k}\}\), \(k\leq m\);
1 for\(i=1\) to \(m\)do
2 if\(SupportedRP(\gamma_{i})==1\)then
3 Calculate the position of closest distance to the RP and add it to \(\Psi\);
4 end if
5 if\(SupportedRP(\gamma_{i})==2\)then
6 Find the middle point in the intersected line which is between the two RPs;
7 if The middle point balances energy consumption then
8 Add the point's position to \(\Psi\);
9 end if
10 else
11 Move the selected point to the direction that balances the energy-consumption in the intersected line and add the balanced point to \(\Psi\) and in case of reaching to the end of line and not finding any balanced point then add the position of the end point in the interested line to \(\Psi\);
12 end if
13
14 end for
15 if\(SupportedRP(\gamma_{i})>2\)then
16\(\mathrm{Y}=MaxPoint(\gamma_{i})\);
17\(\mathrm{\Phi}=MaxLine(\gamma_{i})\);
18\(\mathrm{Y}=LineToPoint(\mathrm{\Phi})\);
19\(\mathrm{\Psi}=MidPoint(\mathrm{Y})\);
20 while\(Sc==0\)do
21\((Sc,\Psi)=Score(\mathrm{Y})\);
22
23 end if
24
25 end for
```  
**Algorithm 2**Determining Sojourn Locations
## 3 Research Model and Hypotheses
### Technological Factors  
Complexity represents perceived difficulty in the usage of an innovation [23]. Organizations tend to adopt new technologies that are easy to use, as complex technologies can result in decreased adoption [29]. Therefore, we posit:  
**Hypothesis 4 (H4).**_Complexity is negatively related to the adoption of cloud computing for HPC._  
Compatibility refers to \"the degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters\" [6]. Organizations are less likely to adopt cloud computing if it is not compatible with these values, as adoption may require major adjustments in organizational processes and considerable learning [6; 37]. Compatibility has been found to influence cloud computing adoption [5]. It encompasses a wide range of expectations including performance and cloud compatibility with extant practices in an organization and HPC. Thus, we propose:  
**Hypothesis 5 (H5).**_Compatibility is positively related to the adoption of cloud computing for HPC._  
In the context of HPC in the cloud, industry reports suggest concerns within the HPC community that cloud computing may not be a reliable medium to process HPC workloads and that cloud communication speeds may not be sufficient to handle the data movement necessary for such workloads [12; 38; 39]. Security concerns are consistently reported as a major barrier to cloud computing adoption [12]. Security is perceived to directly relate to the reliability of a cloud computing system and is positively associated with the cloud computing adoption decision with higher perceptions of security increasing adoption intentions [14]. Thus, we hypothesize:  
**Hypothesis 6 (H6).**_Perceived reliability and security are positively related to the adoption of cloud computing for HPC._
## 4 Alternating-Width Vertical-Wall Labyrinthine Slit Perforation in a Porous Matrix
This is illustrated in Figure 7.  
Figure 7: Normal incidence absorption coefficient spectra predicted for 0.045 m thick hard-backed layers consisting of either alternating width (\(w=1\) mm, \(b=3\) mm, \(l=40\) mm, \(\delta=7\), \(N=6\), continuous line) or constant width (\(w=1\) mm, \(b=3\) mm, \(l=40\) mm, \(N=10\), broken line) vertical-wall labyrinth in a slit pore matrix (0.04 mm wide slits at \(70^{\circ}\) to the normal, porosity 0.75). The dash-dot line is the prediction for the same non-uniform labyrinthine perforation in a non-porous solid and the dotted line is the prediction for the slit pore medium (no perforation). The absorption peak at 170 Hz, corresponding to the layer thickness of 4.5 cm being 1/45 th of the incident wavelength, is predicted for either a ten-turn 3 mm wide labyrinthine slit or a six-turn labyrinthine slit with alternating widths of 3 and 27 mm. A second consequence of the extra tortuosity associated with alternating the width of a vertical-wall labyrinthine slit is that a narrow-band low-frequency absorption peak is predicted even if it is used to perforate a relatively low-flow-resistivity and low-porosity matrix. This is illustrated in Figure 8. A low-frequency high-absorption peak at 200 Hz is predicted for a 6-fold alternating-width vertical-wall labyrinthine slit perforation in a 4.5 cm thick matrix containing 0.3 mm wide slits at 60\({}^{\circ}\) to surface normal with a porosity of only 0.1. A lower absorption peak at 220 Hz is predicted for a 10-fold 3 mm wide labyrinthine perforation in the same porous matrix. 0.3 mm wide micro-slits are readily manufacturable using a commonly available 3D printing method [10]. If an alternating-width labyrinthine slit perforation is created in a lower-flow-resistivity and low-porosity matrix, then the tortuosity of the porous matrix is predicted to have a strong influence. This is illustrated by the predictions in Figure 9, where, keeping all other parameters the same, increasing the tortuosity of the matrix from 1 (matrix slits normal to surface) to 8.55 (matrix slits inclined at 70\({}^{\circ}\) to the normal) reduces the frequency of the lowest-frequency absorption peak from 300 to 210 Hz. Figure 8: Normal incidence absorption coefficient spectra predicted for 0.045 m thick hard-backed layers consisting of either an alternating width (\(w=1\) mm, \(b=3\) mm, \(l=40\) mm, \(\delta=8\), \(N=6\), continuous line) or a constant width (\(w=1\) mm, \(b=3\) mm, \(l=40\) mm, \(N=10\), broken line) vertical-wall labyrinthine slit in a slit-pore matrix (0.3 mm wide slits at 60\({}^{\circ}\) to the normal, porosity 0.1). The dash-dot line is the prediction for the same labyrinthine slit perforation in a non-porous solid and the dotted line is the prediction for the slit pore medium alone (no perforation).
# Assessment of Novel Modal Testing Methods for Structures Rotating in Water
## 4 Results
### Results in Air
#### 4.1.2 Experimental Detection of the Natural Frequencies of the Non-Rotating Disk in Air  
Figure 11 shows the spectra of the structural response measured by different sensors mounted at different locations when the disk was not rotating in air. As the sensors measured different physical magnitudes, i.e., acceleration, strain or displacement, all the spectra were normalized using their maximum values in the range of 0 to 100 Hz. The accelerometers placed on the lower and upper bearings in the radial direction (\(\mathrm{ALB_{y}}\) and \(\mathrm{AUB_{y}}\)) primarily detected the 1 ND at 39.8 Hz, while the accelerometers in the axial direction (\(\mathrm{ALB_{x}}\) and \(\mathrm{AUB_{x}}\)) primarily detected the 0 NC at 41.2 Hz. The 1 ND resulted in radial displacements on the bearings and was consequently detected by \(\mathrm{ALB_{y}}\) and \(\mathrm{AUB_{y}}\), whereas the 0 NC resulted in axial displacements, and thus it was detected by \(\mathrm{ALB_{x}}\) and \(\mathrm{AUB_{x}}\). Both on-board sensors, the strain gauge SG3_9 and the accelerometer A0 detected the 2 ND at 69.1 Hz with similar relative amplitudes. However, SG3_9 detected the 1 ND at 39.8 Hz with a higher relative amplitude than A0 because the latter was mounted close to the nodal line of the 1 ND mode shape, producing a low acceleration at that point. Finally, D0, D1 and D2 primarily detected the natural frequency associated with the 1 ND at 39.8 Hz because their measuring directions coincided with the main displacement direction of this mode on the disk and shaft. Therefore, the locations of the sensors had a strong influence on which natural frequencies could be detected. A sensor may not have detected a natural frequency because it was located on a nodal position or because its measuring direction did not coincide with the main displacement direction of the corresponding mode shape.
# Energy-Efficient Spatial Query-Centric Geographic Routing Protocol in Wireless Sensor Networks
## 1 Introduction
QTGrid was discussed from the aspects of cluster partitioning and encoding, CH election, routing strategy, and data aggregation. First, the monitoring area was logically divided into clusters by a quadtree structure, and each grid's location was encoded to reduce the memory overhead. Second, CH nodes were selected based on several metrics, such as distance from a candidate node to the grid center and adjacent CHs and residual energy. Third, the next-hop routing node was selected depending on the residual energy of the candidate node and its distance to the sink node. Fourth, a lossless data aggregation algorithm and a flexible spatial query algorithm were adopted to reduce the transmission of redundant data and meet the application requirements, respectively. Lastly, QTGrid and several related methods were compared and analyzed through simulation experiments. The rest of this paper is organized as follows. Section 2 discusses the existing work related to the proposed technique. Section 3 introduces the details of the newly proposed routing protocol. Section 4 evaluates the protocol through simulations, and Section 5 presents the conclusions.
# Exercise Suppresses Appetite in Obesity: A Biochemical, Metabolic, and Molecular Approach
## 5 Exercise and Eating Behaviors  
Exercise indirectly regulates appetite by mitigating the neural response in areas of the brain involved with visual process, attention, and motivation [487], as shown in a study where cycling for 1 h (at 83% of maximum heart rate) reduced neuronal responses to pictures of food [488]. Eating behaviors in humans are not only determined by hunger, satiety, and energy stores but also by complex factors such as sensory stimuli, attention, emotion, and some cognitive aspects [489]. The insular cortex (a distinct lobe of the cerebral cortex) is important in determining human behaviors related to cognitive function [490, 491], and individuals motivated to eat have a constant neural response in the insular cortex [492, 493]. Thus, the neural response to visual cues of food is diminished in this cortex by a single bout/constant exercise (Figure 6). Prefrontal and orbitofrontal cortices, the hippocampus, and the visual cortex are brain regions responding to reward with roles in reducing or increasing appetite and eating motivation (Figure 6). Exercise reduces the activity of these brain regions, as shown by an fMRI analysis [487, 488, 493, 494, 495]. Details of the mechanisms by which both acute and chronic exercise impact the response of the brain's reward systems to food cues require further investigation. However, it is likely that body movements cause dynamic changes in hemodynamic and metabolic regulation, myxkine release, and muscle inflammation to influence cognitive and emotional brain circuits regulating appetite and eating motivation.  
Figure 6: Some biological pathways activated by exercise; impact on appetite shown in red. BAT, brown adipose tissue; HPA, hypothalamic-pituitary axis; POMC, pro-opiomelanocortin; SNS, sympathetic nervous system; TRPV1, transient receptor potential vanilloid-1.