esa-sceva/satcom-chunk-collection huggingface dataset:
# Co-Processing Parallel Computation for Distributed Optical Fiber Vibration Sensing
## 3 Data Parallel Processing Principle
### Data Parallel Processing Based on GPU  
The graphics card chosen was NVIDIA's GEFORCE series GTX 1060. It used a GP106-300 chip based on more advanced Pascal architecture, which has the properties of high performance and low cost. For the used GPU, the number of multi-processors and the max number of threads per multi-processor are nine and 2048, so the number of maximum available threads of the GPU is 9 \(\times\) 2048 = 18,432. Compute unified device architecture (CUDA) is a parallel computing platform for the NVIDIA's GPU, which contains instruction set architecture (ISA) and a parallel computation engine. By using the CUDA technique, the stream processors can be mapped to thread processors to deal with the computation of large-scale dense data. CUDA application programs can use GPU hardware for parallel computing by directly calling the underlying CUDA API, or by using the CUDA runtime library which encapsulates the underlying CUDA API to simplify the programming process. The processing program then needs to be converted to an executable binary file by the compiler, and the CUDA programming code is divided into the host code executed in the CPU and the device code executed in the GPU. Therefore, the compiler should ensure that the two parts of code can be compiled into a binary file and executed on different devices.  
The key of the rapid parallel data processing of the \(\Phi\)-OTDR sensing system by the GPU is the allocation of multiple threads to execute the same instruction at the same time and the lower switching frequency of the context, which could reduce the data processing time and improve the real-time performance of the system. Many threads in the same thread block have the same instruction address and can be executed in parallel. In addition, they can communicate indirectly through shared memory and synchronization primitives to maintain dependency. However, threads in different blocks of the same grid or different grids are independent of each other. They do not need to communicate with each other and have a highly independent and parallel relationship.  
The processing architecture of CUDA is illustrated in Figure 5. It consists of threads, thread blocks, and thread grids. The global memory is shared by some thread grids, and these grids can call and handle data which come from the global memory. Every thread grid consists of numerous two-dimensional thread blocks. The blocks have an \(X\)-axis and a \(Y\)-axis, and they have different settable index value sizes in different dimensions. Similarly, the thread blocks are constructed by large amounts of thread in the form of a two-dimensional data structure. Coordinates are adopted in Figure 5 in order to display this special two-dimensional data structure conveniently. The first parameter of the coordinate represents the index value in the direction of the \(X\)-axis, and the second parameter signifies the index value of the \(Y\)-axis.
# Independent System Calibration of Sentinel-1B
## 9 Interferometric Verification
### Repeat-Pass Interferometric Consistency Evaluation
The first row shows the physical baseline (B), perpendicular baseline (\(B_{perp}\)), and parallel baseline (\(B_{par}\)) for all analyzed pairs, as a function of the geographic latitude. The second row shows the burst mis-synchronization values of the analyzed pairs. The third row shows the percentage of the final common Doppler bandwidth with the master acquisition date. antenna mispointing of the majority of the analyzed acquisitions for the given period, with a Doppler difference of about 120 Hz (cf. Figure 28). For the data takes acquired shortly after the beginning of September 2016, when the attitude of Sentinel-1B was corrected, the common Doppler bandwidth is again above 90% when combining S1A and S1B acquisitions. It should be mentioned that in the above analysis, no relevant dependence was found in the plots as a function of the latitude.
###### Abstract
The primary approach is to manipulate the downlink transmission parameters in the Earth observation communication system (onboard TX and ground station RX) by using CR techniques in the underlay paradigm to remove degradation in the image caused by these interferences. This combined strategy allows spectral, temporal, and spatial coexistence, achieving the required throughput. The contribution of this work addresses the mitigation of harmful interference effects in coexisting systems by evaluating case studies of EOS with images from a WFI camera (wide field imager). We conducted bench tests to determine the no-failures reception threshold as a function of the signal-to-interference plus noise ratio. The article is organized as follows: Section 2 presents the materials and methods for CR paradigm exploitation, MODCOD in ACM, and our combined solution for the EESS downlink coexistence scenario, along with the strategy to mitigate the harmful effects caused by interference in the desired signal. Section 3 presents the main empirical-analytical results for the chosen combined MODCOD/CR strategy in the case studies and the Lab emulation of the communication system. The results are discussed in Section 4. A brief conclusion of the work is provided in Section 5.
## 4 Finite Element Implementations
### Consistent Tangent Modulus and Calculation of Strain Increment
#### 4.4.2 Uniaxial Ratcheting Prediction  
We simulated the ratcheting strains of the specimen under four different uniaxial loading conditions, first taking the mean stress of 290 MPa and the amplitude stress of 240 MPa as an example. The finite element model of the specimen was established with element SOLID45. In the finite element model, the bottom surface was fully constrained, and the axial load of 353-3746 N was cyclic and applied on the master node of the rigid region for the top surface. Figure 17 indicates the boundary condition of the finite element model. The cyclic loading will generate a uniform cyclic stress of 50-530 MPa in the measuring cross section of the specimen.  
Figure 16: Uniaxial tensile verification curve.  
Figure 15: Uniaxial tension of the FEA model.  
As shown in Figure 18, the maximum plastic strain of the specimen appeared in its middle section. Therefore, the ratcheting strain at the middle of the specimen was selected for comparison.  
Figure 19 shows the uniaxial ratcheting curve for the ratcheting strain of the specimen under different loading conditions by ANSYS. It can be seen from Figure 19 that the simulation of the uniaxial ratcheting strain with ANSYS was consistent with the experimental results and the MATLAB predictions.  
Figure 19: The simulation results of uniaxial ratcheting for the specimen.  
Figure 17: Boundary conditions applied to the specimen finite element model.
# Conceptual Design of the Combinable Legged Robot Bio-Inspired by Ants' Structure
## 5 Results
### Walking Motion for a Single Robot  
Figure 7a-c displays the foot trajectory for each leg during the walking motion of a single robot with respect to the \(x\)-, \(y\)-, and \(z\)-axes on the base frame of the robot (i.e., the center of the body). Figure 7d-f presents the rotation angle for each joint of the robot's legs. For \(t\) = 0-2.5 s into the motion, the trajectory for Leg 1 in the \(x\)-, \(y\)-, and \(z\)-axes changes from \(-247.3\) mm to \(-185.3\) mm (Figure 7a), \(232.6\) mm to \(144.6\) mm (Figure 7b), and \(-212.5\) mm to \(-252.2\) mm (Figure 7c), respectively. At the same time, both joints 2 and 3 of Leg 1 rotate to their maximum angle of \(130.0\lx@math@degree\) (Figure 7e) and \(87.19\lx@math@degree\) (Figure 7f), respectively, to lift Leg 1 off the ground, while Joint 1 of Leg 1 is rotated \(-10\lx@math@degree\) (Figure 7d) to face forward (Figure 4). These results indicate that Leg 1 is walking forward and is the only leg in motion at this time.  
The sequence of walking motions remains the same with respect to the time interval for all four legs, with movement first initiated for Leg 1 at \(t\) = 0 s, then Leg 2 at \(t\) = 5 s, followed by Leg 3 at \(t\) = 10 s and Leg 4 at \(t\) = 15 s. The proposed robot thus fulfills the static stable walking requirement in that three legs are always in contact with the ground while walking [17].  
The practical validation of the stability of the single robot walking is provided in Figure 8 in the form of IMU results. These results consist of the rotational angle of the roll (r), pitch (p), and yaw (y) at the center of the robot body while walking in relation to the \(z\)-axis, the opposite of the \(x\)-axis, and the \(y\)-axis, respectively. Although there is an error caused by a delay in the response of the body center to the leg movements while walking, the overall results in Figure 8 show that no overturning occurred. At \(t\) = 20 s, which represents the end of a walking motion period, the angle of rotation along the roll  
Figure 6: Combination and walking processes. (**a**) Operation begins. (**b**) Ready pose. (**c**) Robots are approaching each other. (**d**) Robots combined. (**e**) Center legs moving forward. (**f**) Front-left and hind-right legs move. (**g**) Front-right and hind-left legs move. (**h**) All legs actuate, and one step is taken.  
and pitch axis is \(2.40^{\circ}\) and \(6.25^{\circ}\), respectively, which indicates that the body of the robot is slightly tilted toward the left and toward the back, but there is no overturning.  
Figure 7: Displacement of the feet trajectory on the walking motion for a single robot along the (**a**) \(x\)-axis, (**b**) \(y\)-axis, and (**c**) \(z\)-axis. The angle of rotation of the leg joints for a single robot: (**d**) Joint 1, (**e**) Joint 2, and (**f**) Joint 3.
# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 2 Materials
### Sentinel-1 SAR Time Series  
The research involves 30 C-band (5.405 GHz) Sentinel-1 SAR images in descending mode (path 97, frames 659 and 665, slices 1 and 2, morning at 08h53-08h55 UTC--morning at 05h53-05h55 BRT)  
Figure 2: Crop calendar of sugarcane in the state of São Paulo: Type A—winter planting (first harvest); Type B—summer planting (first harvest); Type C—ratooning (second to fifth harvest).  
(Figure 3). All the Level-1 SLC images are acquired in the Interferometric Wide (IW) swath with VV and VH polarization. The choice of the SLC-product is stipulated by the fact that the algorithm of harvest dates determination proposed in the paper relies on the coherence time series, which construction needs the information concerning amplitude and phase of a radar signal. The data in VV polarization, being more sensitive to harvest-related changes in plant height, were used [24]. The coherence time series with a time interval of 12 days and a spatial resolution of 15 \(\times\) 15 m was constructed.  
The images were obtained from the European Space Agency (ESA) Copernicus Open Access Hub [40] and Alaska Satellite Facility (ASF) VERTEX service [41]. The image pairs were processed using the ESA Sentinel Application Platform (SNAP) 7.0 (Sentinel-1 Toolbox) software [24,42]. It included the following steps of pre-processing:  
1. Applying Orbit Information about satellite position during the SAR data acquisition.
2. Back Geocoding--coregistration of two split products based on the orbit information added in the previous step and information from a digital elevation model.
3. Coherence Estimation. Coherence is calculated as a separate raster band and shows how similar each pixel is between the slave and master images on a scale from 0 to 1.
4. TOPSAR Deburst--removing the seam lines between the single bursts.
5. Multilook--speckle reduction.
6. Range Doppler Terrain Correction--geocodes the image by correcting SAR geometric distortions using a digital elevation model and producing a map projected product.
## 8 Future Challenges for the Enhancement of ED Limitations
### Research Challenges Related to the Fading Channels and Hidden Node Problem  
Although ED in low SNR environments cannot achieve accurate and reliable sensing results, previous research has also shown that ED, being a simple technique, lacks detection reliability in deep fading environments [66; 67; 124] (Table 4). The performance analysis of ED in fading channels was performed in Refs. [115; 116; 117; 118; 119; 120; 121]. Additionally, setting the detection threshold with respect to the frequency notches in wireless frequency-selective fading channels is one limitation. The other is the inability of the ED method to detect (direct sequence and frequency hopping) spread-spectrum signals [3]. The ED technique is also susceptible to spurious tones and baseband filter effects [137].  
To overcome such challenges, more sophisticated SS methods based on collaboration among the SUs must be devised. Determining whether a collaboration between different SUs and corresponding SS techniques can improve the sensing performance in fading environments is a possible approach to overcoming some of the aforementioned limitations (Table 4). Collaborative detection is based on utilising versatile SS techniques by spatially dislocated SUs in order to achieve accurate and reliable detection decisions. It was demonstrated by [PERSON]. [68; 116; 122] that a collaboration among distributed SUs that employ ED alleviates the effects of NU when the users are experiencing independent and identically distributed fading or shadowing.  
Additionally, ED in other non-cooperative local spectrum sensing methods (presented in Table 5) is sensitive to a hidden node problem (Table 4). When a cognitive SU is at a far distance from the PU, the PU signal may be too weak for reliable detection. An advantage of collaborative SS can be the possibility of improving the sensing performance through mutual communication between the SUs [123]. The information on the signal of the PU can be relayed to the distant SU. This can be done after reliable local signal detection has been performed by the SU located near the PU. The challenges related to the integration of SUs which use ED as the method for local SS in such a relaying collaboration detection concept have to be investigated. Hence. further experimental investigations are needed in order to improve the sensing performance in fading and hidden node environments, based on collaboration among SUs which can use ED as one of the sensing techniques.
## 3 Results and Discussion
### Identification and Quantification of Phenolic Acid, Flavonoids, and Vitamin C
The vitamin C content in garambullo flowers decreased after treatment, which could be due to the less rigid structure of the flowers, allowing compounds to be released easily from the compartment cells and lost in the cooking water. On the other hand, the degradative enzymes, such as AA oxidase, cytochrome oxidase, and peroxidase naturally affect the retention of vitamin C in food matrices due to them appearing naturally in foods. However, after undergoing a heat treatment known as bleaching, they inactivate these enzymes (industrial application). It has also been found that there are factors, such as some phenol groups (flavonols) and sugars causing the reduction of oxygen solubility and its availability, thus preserving the retention of vitamin C in foods. On the other hand, the phytochemicals will be released due to changes in the cellular structures and the breakdown and dissociation of molecular linkages between components of plants as ascorbic acid [53; 54; 55]. The leaves of mallow had a concentration of 252.44 \(\pm\) 1.17 mg/100 g db, and the flowers of garambullo had a concentration of 143.69 \(\pm\) 1.30 mg/100 g db after thermal treatment, as the vegetables were eaten. The vitamin C content on a wet basis (wb) was 7.56 mg/100 g in mallow and 2.88 mg /100 g wb in garambullo flower. These values are lower compared to other vegetables such as chilis (43 and 247 mg/100 g wb) [56], guavas (62.3 to 168.9 mg wb) [57], peppers, green peas, spinach, pumpkin, and carrots (25.72-61.56 mg/100 mg wb) [58]. However, in places where fruits and vegetables rich in vitamin C are not available, these plants can be an important source of vitamin C.  
According to the dietary reference values for the Mexican population established by [PERSON] et al. [59], the Recommendation of dietary intake (RDI) for vitamin C is 60 mg/day for adults. Therefore, these plants provide an important supply of vitamin C in cooked plants (252.44 mg/100 g in mallow and 143.64 mg/100 g in garambullo flowers), plants both contributing at least 2.38 times the RDI in boiled samples of vitamin C.  
Some studies have mentioned that the boiled water used in food preparation is important because low-molecular compounds such as phenolics and vitamins diffuse into the cooking water and affect the nutritional composition of the foods. Therefore, it is important to use the water from vegetables in other food preparations to take advantage of the released soluble compounds [46].
## 3 Air Pollution Monitoring System Infrastructure
### MoDisNet Network Architecture  
The MoDisNet system is constructed based on a novel network environment, which is designed as a two-layer network architecture - the mobile sub-network formed by the Mobile Sensor Nodes (MSN in short) and the stationary sub-network organized by the Static Sensor Nodes (SSN in short). The network architecture is shown in Figure 3.  
Here, MSNs are installed in the vehicles. They sample the pollution data and execute the AD conversion to get the digital signals. According to the system requirements, the MSNs may pre-process the raw data (such as the noise reduction, local data cleaning and fusion, etc.) and then send these data to a nearest SSN. The SSNs take in charge of the data receiving, update, storage and exchange works. Cooperating with the e-Science Grid architecture (which will be discussed in detail in section 3.3), the SSNs can realize the distributed data analysis and mining. According to different requirements from the users or the server, the SSNs may send the raw air pollution data or the distributed mining results to the central data warehouse for further process.
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 4 The Proposed Scheme: Mutually-Shared Region-Based Location Verification
### D-Filtering  
The distance-inconsistency filtering (D-filtering) is the process of filtering a sensor who shows an inconsistency in its measured and estimated distances to the other sensor. The measured distance can be acquired by simple distance measuring methods, such as RSS or time-of-flight (ToF), and the estimated distance can be obtained by the calculation based on the reported location information. The filtering is used to perform the simple distance check between two sensors during the proposed  
Figure 2: An example of mutually-shared region (MSR) token usage.  
location verification process. In this paper, Received Signal Strength Indicator (RSSI) is used to acquire the measured distance. Figure 3 shows the concept of D-filtering.  
As show in Figure 3a, a sensor \(A\) measures the distance to the sensor \(B\) using RSSI. At the same time, \(A\) can get the estimated distance to \(B\) using the reported location of \(B\). \(A\) compares the measured distance to \(B\) and the estimated one to \(B\). If the distances are the same, \(B\) is considered as a potentially honest sensor, otherwise as a potentially malicious sensor. Figure 3b shows the D-filtering case with a malicious sensor. Since the distances may also be forged by an attacker by falsely reporting its location to the other place while maintaining consistent distances, the sensors who pass the D-filtering are not totally trusted. The detailed analysis of the effect of D-filtering will be provided in Section 5.1.3.
## 7 Conclusions  
In this paper, we have proposed an energy efficient distance-aware routing algorithm with multiple mobile sinks for WSNs to improve network performance in terms of energy consumption and network lifetime. We first compare the residual energy of sensor network respectively using our proposed routing algorithm and LEACH, and find that mobile sink nodes clearly contribute to energy efficiency. Then we further study the network performance with different mobile sink numbers and different sojourn location selections. Through extensive experimental analysis, we see that mobile sink nodes can greatly improve energy efficiency and prolong network lifetime. In our proposed routing algorithm, sojourn location is chosen from parking positions determined by the nodes' distribution and transmission range. In order to ensure completeness and correctness of forwarded data packets, relay sensors are selected based on the distance factor and energy factor. Besides, if a certain transmission link fails, a substitute forwarding sensor node is selected depending on the link cost between sensor nodes.  
In order to alleviate the hot spot problem and further balance the energy consumption, we use mobile sink nodes for data collection. However, all the experiments are conducted in an ideal environment, and it lacks a comparison of our proposed routing algorithm with some existing algorithms or protocols using mobile sink nodes. In this paper, we mainly put the emphasis on the study of the number of mobile sink nodes and parking position selection. The influence of different sink moving speeds on network performance is not analyzed. In the future, we will further study several known routing algorithms or protocols using multiple mobile sink nodes, and discuss the influence of sink moving speed and movement trajectory on packet delivery delay, energy consumption and network lifetime. Besides, to effectively organize and manage sensor nodes, we will study how to combine clustering technology with sink mobility technology so that network performance can be further improved.
## 3 Results and Discussion
### Selection of Spectral Feature Sets  
According to the selection conditions, the number of selected features was 16, 15, and 9 for the three angles (Table 8), and these features were used as the input variables for models. From the order of accuracy in Table 8, it can be found that the ability of the first-order derivative features to identify FHB are better than the continuum removal features and vegetation indices. The features of continuum removal are not sensitive to FHB for erect samples. The sensitive features for the front samples were almost the same as the side samples, and the number of sensitive features for the erect samples was the least. The number of sensitive features for erect samples was not only the least, but also the ability of single features to identify FHB was much lower than the ability of the front and side samples. Because the disease occurrence of the middle and bottom kernels of the ear was ignored when the erect ear was measured, the correlation between the actual disease severity and the spectral features was not significant.