esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Back-Projection Algorithm for GNSS-R BSAK Imaging Based on CPU and GPU Platform
###### Abstract  
Global Navigation Satellite System Reflectometry Bistatic Synthetic Aperture Radar (GNSS-R BSAK) is becoming more and more important in remote sensing because of its low power, low mass, low cost, and real-time global coverage capability. The Back Projection Algorithm (BPA) was usually selected as the GNSS-R BSAK imaging algorithm because it can process echo signals of complex geometric configurations. However, the huge computational cost is a challenge for its application in GNSS-R BSAK. Graphics Processing Units (GPU) provides an efficient computing platform for GNSS-R BSAK processing. In this paper, a solution accelerating the BPA of GNSS-R BSAK using GPU is proposed to improve imaging efficiency, and a matching pre-processing program was proposed to synchronize direct and echo signals to improve imaging quality. To process hundreds of gigabytes of data collected by a long-time synthetic aperture in fixed station mode, a stream processing structure was used to process such a large amount of data to solve the problem of limited GPU memory. In the improvement of the imaging efficiency, the imaging task is divided into pre-processing and BPA, which are performed in the Central Processing Unit (CPU) and GPU, respectively, and a pixel-oriented parallel processing method in back projection is adopted to avoid memory access conflicts caused by excessive data volume. The improved BPA with the long synthetic aperture time is verified through the simulation of and experimenting on the GPS-L5 signal. The results show that the proposed accelerating solution is capable of taking approximately 128.04 s, which is 156 times lower than pure CPU framework for producing a size of 600 m \(\times\) 600 m image with 1800 s synthetic aperture time; in addition, the same imaging quality with the existing processing solution can be retained.  
Global Navigation Satellite System Reflectometry (GNSS-R); Back-Projection Algorithm (BPA); Graphics Processing Units (GPU) 2021  
Article An Improved Back-Projection Algorithm for GNSS-R BSAK Imaging Based on CPU and GPU Platform  
[PERSON]  
School of Electronics and Information Engineering, Beihang University, Beijing 100191, China; [PERSON]L_ADDRESS]edu.cn (S.W.); [EMAIL_ADDRESS] (Z.X.); [EMAIL_ADDRESS] (D.Y.); [EMAIL_ADDRESS] (G.G.)  
* Correspondence: [PERSON]S]; Tel.: +86-18801493608
# Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country
## 2 Prerequisites for Using the UAVs in Some Spheres of Economy
### Monitoring of the Hazardous Geophysical Processes (MoHGP)  
Geological and geophysical processes underground and on the surface can lead to catastrophes and natural disasters. Natural and man-made disasters in densely populated areas lead to deep social upheavals. They are considered to be one of the serious  
Figure 6: Monitoring of wild animal life. Source: Generated by the authors.  
destabilizing factors [81; 82]. Monitoring in order to identify the sources and to assess the consequences of catastrophic events and possible assistance to victims is the purpose of using UAVs [83]. The examples of this kind are as follows: the tasks of mapping the displacement of landslides (Italy, Tessin) [84; 85], the organization of communications in the affected areas [85], and the delivery of small goods [86]. Possible monitoring tasks (left) and the use of UAVs (right) are shown in Figure 7 (the Figure is based on [83; 87; 88; 89]).  
The use of UAVs for monitoring the development of hazardous geological and geophysical processes is economically profitable. According to approximate estimates given in [11], the possible savings from preventing the consequences of annual floods in Kazakhstan can amount to up to USD 10 million per year.  
The main prerequisites of using IUAVT for monitoring hazardous geophysical processes are as follows: destabilizing factors [82; 89], significant economic losses, threats to life, and health of the population [81; 90]. The estimated annual expenses are USD 200 million.  
It is necessary to realize the possibility of prevention of emergencies, and to develop methods of forecasting and operative monitoring of dangerous geophysical processes and changes of engineering constructions with the application of a UAV and data mining tools. The following scientific and technical problems should be solved: development of methods for recognition and classification of images in this subject area. The evaluated economic effect of IUAVT employment is USD 10 million (see Appendix A Table A6).
## 3 Results and Discussion  
### Characterizations  
In our previous study [27], the influence of reaction time on the morphology of PEI-functionalized Fe\({}_{3}\)O\({}_{4}\) MNPs was carefully investigated; when the solvothermal reaction lasted for 3 h, the submicrometer Fe\({}_{3}\)O\({}_{4}\) sphere was fully formed, which was also confirmed by X-ray diffraction and FTIR results. The Fe\({}_{3}\)O\({}_{4}\) microsphere prepared at 3 h showed superparamagnetic behavior, and its magnetization saturation was \(\sim\)62 emu/g [27]. The morphology and size distribution of spherical MNPs are shown in Figure 1a-d. The size of PEI-functionalized Fe\({}_{3}\)O\({}_{4}\) MNPs ranged from 100 to 350 nm, and its average size was estimated to be \(\sim\)209 nm; in comparison, the size of pure Fe\({}_{3}\)O\({}_{4}\) MNPs ranged from 100 to 450 nm, and its average size increased to 256 nm. According to the nitrogen adsorption-desorption experiments, the surface area of PEI-functionalized MNPs was calculated to be \(\sim\)45 m\({}^{2}\)/g. Moreover, the PEI-functionalized MNPs could be well dispersed in water, whereas the pure Fe\({}_{3}\)O\({}_{4}\) MNPs sunk together in water phase. These results suggested that PEI could not only stabilize the crystalline grain in reaction system and decrease the size of synthetic Fe\({}_{3}\)O\({}_{4}\) MNPs, but also significantly improve the water dispersibility of MNPs. Figure 1e shows the FTIR spectra of pure and PEI-functionalized Fe\({}_{3}\)O\({}_{4}\) MNPs, as well as the FTIR spectra of PEI-functionalized MNPs after Pb(II) adsorption. The vibration peak at \(\sim\)1638 cm\({}^{-1}\) was ascribed to the COO\({}^{-}\) group [27], suggesting the existence of carboxylate in Fe\({}_{3}\)O\({}_{4}\) microsphere. The carboxylate groups were mainly originated from sodium acetate and coordinated with iron ion. The vibration peaks at approximately 2920 and 2857 cm\({}^{-1}\) were assigned to the methyl and methylene groups. Meanwhile, as compared with pure Fe\({}_{3}\)O\({}_{4}\) MNPs, a characteristic vibration peak at 1595 cm\({}^{-1}\) was observed for PEI-functionalized Fe\({}_{3}\)O\({}_{4}\) MNPs, which corresponded with the bending vibration of -NH\({}_{2}\) group [28], suggesting the successful coating of PEI on Fe\({}_{3}\)O\({}_{4}\) MNPs. After Pb(II) adsorption, characteristic band of -NH\({}_{2}\) group disappeared at 1595 cm\({}^{-1}\) but moved to 1521 cm\({}^{-1}\) instead, suggesting the Pb(II) binding with -NH\({}_{2}\) groups through chelating interaction mechanisms. Meanwhile, the peak of COO\({}^{-}\) group became stronger and shifted slightly to 1628 cm\({}^{-1}\), which was ascribed to the adsorption of carboxylate groups from aqueous solution along with Pb(II) capture for charge balance [28]. This is because Pb(Ac)\({}_{2}\) was used as the source of Pb(II) in this study, and hence the carboxylate groups was also existed in the aqueous solution.
## 4 Experimental Evaluation
### Scenario 1: Antenna Direction
#### 4.2.2 Results of IP Network Layer in Scenario 1  
Figure 3 shows the throughput results of the TCP and UDP communications on the AP side. Both graphs have almost the same performance trend, except for the A/D and B/B combinations. In addition, we can see that the UDP result is slightly superior to the TCP result because UDP has no traffic control like TCP. The results show that even at close range, the direction of the antenna may have some impact.  
We next examine retry and jitter as the other indicators of _iperf3_. The results demonstrated no retry for TCP communication. Figure 4 shows the jitter results of UDP communication on the AP side, and we can see that the jitter is relatively stable. Hence, the _iperf3_ results provide the necessary information to verify that the communication requirements of the application are met. If an application requires real-time, we also examine the results of the _ping_ tool.  
Figure 3: Throughputs of TCP and UDP communications (Scenario 1).  
Figure 2: Measurement environment for antenna direction scenario.  
The _ping_ provides RTT and packet loss between a sender and a receiver. In the measurement, the results had no packet loss. That is, we can see that the network did not have problems such as congestion and link quality degradation. In Figure 5, the RTT of A/D is higher than the others during TCP communication. In addition, during UDP communication, the RTTs of A/D, B/B, and D/D on the STA side have a somewhat significant variance compared with the other combinations. However, the results are considered to show adequate communication performance in IEEE 802.11 ad communication by actual equipment.
## 2 LoRaWAN Network Downlink Communication
### Downlink Communication Bandwidth Occupancy  
Assuming that the link \(l_{j}\) and the \(q\) port of the SDN switch node \(v_{a}\) are connected to the \(g\) port of node \(v_{b}\), the used bandwidth \(b_{j}(t)\) of link \(l_{j}\) at time \(t\) can be calculated as follows:  
\[b_{j}(t)=\frac{p_{a,q}^{4}(t)-p_{a,q}^{4}(t-T)+p_{b,g}^{4}(t)-p_{b,g}^{4}(t-T)} {T} \tag{2}\]  
Figure 3: Gateway and server downlink communication protocol.  
In Equation (2), \(p_{a,q}^{4}(t)\) is defined as the transmitted data of node \(v_{a}\) at port \(q\) at time \(t\), and \(p_{b,q}^{4}(t)\) is defined as the transmitted data of node \(v_{b}\) at port \(g\) at time \(t\). Here, \(q\), \(g\) are the port numbers of \(v_{a}\) and \(v_{b}\) (\(q,g\in N\)).  
In the process of downlink routing, \(\max(B_{j})\) is defined as the maximum bandwidth of the data flow that can be transmitted by link \(l_{j}\), and the bandwidth occupancy rate of link \(l_{j}\) at time \(t\) is calculated by the following equation:  
\[\mu_{j}(t)=\frac{b_{j}(t)}{\max(B_{j})} \tag{3}\]  
Here, Table 3 is set up to demonstrate the congestion level of the link bandwidth occupancy rate, in which \(s_{j}(t)\) is used to represent the congestion degree of link \(l_{j}\) at time \(t\). The large value of \(s\) means the network tends to be congested.
# Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
## 2 Overview of State-of-the-Art Approaches
### Critical Analysis of Contemporary Approaches Based on Evaluation Criteria
#### 2.4.3 Analysis of Metadata-Based Approaches  
The state-of-the-art approaches which exploit the metadata of the documents have performed single-label classification. These approaches have been shown in Table 3. These approaches predicted the most relevant class for a particular document from the multiple pre-defined classes. Very few state-of-the-art approaches have performed document classification by exploiting only metadata [16; 42; 43]. One important finding from the literature is that the systems which utilize the metadata of research papers were only able to classify papers into a single class. For single-label classification, [PERSON] [42] applied an algorithm on two thousand documents (2000) for the classification of documents into 99 pre-defined classes and achieved precision up to 0.79, recall value 0.81, and F-measure value 0.79, respectively.  
[PERSON] [16] applied different algorithms on a collection of 400 documents but they used very few generic classes (i.e., four classes) and achieved accuracy up to 0.84 for their document classification technique. [PERSON] [43] also used the genetic algorithm (GA) which is from the family of evolutionary algorithms to perform metadata-based document classification. They applied their techniques to a collection of 30,000 features to classify documents into eleven pre-defined classes.  
The authors in [50; 58] investigated their approaches on J.UCS and ACM standard datasets. In [50], the authors investigated the multi-label classification using metadata title,metadata keywords, and metadata title plus keywords and achieved the highest accuracy of 0.88. Similarly, the authors in [58] exploited the references section as the metadata of the articles to classify them into multiple classes and achieved the highest accuracy of 0.74.  
The main advantage of the metadata-based approaches over the content-based approaches is minimum dependency on the content availability. However, in contrast to the content-based approaches, metadata-based approaches exhibit relatively degraded performance and that is quite understandable.
## 3 Wideband-Narrowband Switchable Tapered Slot Antenna for Breast Cancer Diagnosis and Treatment  
[PERSON] et al. [2] presented a wideband-narrowband switchable tapered slot antenna (TSA) with a compact meander line resonator for an integrated microwave imaging and hyperthermia system. To minimize the degradation of the wideband characteristics, the TSA has a compact meander line resonator using one PIN diode. Moreover, the meander line resonator transfers the electromagnetic energy to ensure effective frequency switching. The authors proved that the proposed antenna is suitable to a treatment system and integrated breast cancer detection.  
Statistical Indoor Exclusion Zone Analysis by Investigating Electromagnetic Fields inside a Nuclear Power Plant  
[PERSON] et al. [3] investigated a statistical indoor exclusion zone (EZ) that can be efficiently applied to a nuclear power plant (NPP). To obtain the statistical indoor EZ, the received powers inside the Korea Institute of Nuclear Safety (KINS) simulator room are obtained using the Wireless InSite electromagnetic simulation software. Then, a margined regression model is applied to the simulation data, which can determine a reasonable boundary of the statistical indoor EZ. To validate the statistical indoor EZ, the authors compared the measured results with the simulated results, and two results are in good agreement.
# Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios
## 3 System Model and Problem Formalization  
### System Architecture and Operation  
Figure 1 shows a scenario of mixed coverage of BSs and RSUs in urban road segments. An RSU is deployed on a road and provides computing services for the covered passing vehicles. In cases of the abnormal traffic congestion on the road, with a large number of vehicles in the coverage area of the RSU, all these vehicles may offload tasks to the RSU, leading to an excessive load on the RSU, and it is unable to complete tasks within the specified time. At the same time, the roadside BS in the same area has more computing resources and a larger coverage range. It can also provide computing services for vehicles. However, due to communication protocol issues, vehicles are unable to know the real-time BS loading status.  
Actually, along with the development of artificial intelligence and deep learning methods, the historical load status of BSs and the vehicle access in the previous time periods can help the decision center predict the future load status of the BS for a certain period. This helps the BS provide EC services for vehicles. In this paper, we minimize the system overall service delay, providing more suitable task offloading strategies for vehicles. After the overall design of the optimization strategy, the optimization strategy is divided into the following stages.  
The BS provides historical data of the load state to the cloud computing center of the ITS. Within the current time period, the cloud computing center utilizes the historical data of the load state and the vehicle access to the BS in the previous time periods to predict the load state of the BS for the next time period via using LSTM. In addition, when it is in the first time period, it only relies on the historical load status data provided by the BS.  
Then, vehicles upload information such as the task size, location, speed, acceleration, etc., to the cloud computing center. During the current time period, based on the complexity of the tasks uploaded by vehicles, the load status of the RSU, the predicted load status of the BS, the relationship between the vehicles and the RSU/BS, and the vehicle moving speed, the cloud computing center uses the PPO algorithm to obtain an approximately optimal task offloading scheme.  
Finally, the decision results are sent back to the vehicles. Vehicles perform task offloading and receive the results of whether they can access the BS/RSU or the cloud computing center. In the remaining time of the current time period, the cloud computing  
Figure 1: A scenario of mixed coverage of BSs and RSUs in urban road environments.  
space predicts the load status of the BS for the next time period based on the historical data of the BS and the results of whether vehicles could access the BS.
## 4 Model Based Self-Triggered Strategy  
In this section, in order to avoid continuous event detection communication between adjacent agents, we apply the pull-based event-triggered strategy proposed in Section 3 in the scenarios of discontinuous communication, i.e., self-triggered scheme. The self-triggered strategy indicates that the next triggering time \(t_{k+1}^{i}\) can be predicted from using the information at the previous trigger time \(t_{k}^{i}\).  
The model is given as:  
\[\delta_{i}(t_{k+1}^{i})=\delta_{i}(t_{k}^{i})+(t_{k+1}^{i}-t_{k}^{i})\sum_{j=1 }^{N}L_{d}^{T}(i,j)\delta_{j}(t_{k^{\prime}}^{i}). \tag{51}\]  
Then, the state of agent \(i\) at \(t\in[t_{k^{\prime}}^{i},t_{k+1}^{i}]\) (\(t_{k+1}^{i}\) to be determined) can be written as:  
\[\delta_{i}(t)=\delta_{i}(t_{k}^{i})+(t-t_{k}^{i})\delta_{i}(t_{k}^{i}),t\in[t_ {k^{\prime}}^{i},t_{k+1}^{i}]. \tag{52}\]  
Since agent \(j\in\mathcal{N}^{in}\) transmits trigger state to agent \(i\) whenever agent \(j\) triggers, at any given time instant \(t\), agent \(i\) can obtain the estimation of agent \(j\) based on  
\[\delta_{j}(t)=\delta_{j}(t_{k}^{j})+(t-t_{k}^{j})\delta_{j}(t_{k}^{j}), \tag{53}\]  
until agent \(j\) triggers next. Then, from Theorem 2 we have Algorithm 1.  
```
1:loop
2: Integrate the dynamical model \(\dot{x}_{i}(t)=u_{i}(t)\) from the initial condition \(x_{j}(0)\) for all the neighbouring agents \(j\in\mathcal{N}^{in}\), while listening to possible transmission from them;
3: Substituting (52) and (53) into (36), the following maximizing problem is solved to find out \(t_{k+1}^{i}\): \(t_{k+1}^{i}=\max_{\tau>t_{k}^{i}}\big{\{}\tau:\|\bar{e}(t)\|^{2}\geq s^{-1}(t) \big{\}}\);
4:if any in-neighbors of agent \(i\) trigger at time \(t\in[t_{k^{\prime}}^{i},t_{k+1}^{i}]\), then
5: Agent \(i\) receives renewal information form its in-neighbors;
6: Updating \(\tau\gets t\) and go to step (3);
7:endif
8:if any of agent \(i^{\prime}\) in-neighbors do not trigger during \([\tau,t_{k+1}^{i}]\), then
9: Agent \(i\) triggers at time \(t_{k+1}^{i}\leftarrow\tau\);
10:\(\bar{e}(t)\gets 0\);
11: Update the control input to the value \(u_{i}(t)=\sum_{j=1}^{N}L_{d}^{T}(i,j)\delta_{j}(t_{k}^{i})\), and sends the renewed information to all its out-neighbors immediately
12:endif
13:endloop
```  
**Algorithm 1**Self-triggered update  
Consequently, the system (4) achieves the desired circle formation, and [PERSON] behavior can be ruled out.  
We observe that Algorithm 1 could be regarded as an implementation of Theorem 2, which eliminates continuous communication between different states.
## 2 Materials and Methods
### Identification and Quantification Volatile by GC-FID  
The extracted/adsorbed volatiles into SPME fiber were thermally desorbed into injector port of Gas chromatographs (GC) Varian 3400 (Varian, Inc., Walnut Creek, CA, SUA) that was connected with a flame ionization detector (FID). The common conditions for GC-FID assembly were: the carrier gas was Helium, a DB-1 column (30 m \(\times\) 0.32 mm i.d., 0.25 \(\upmu\)m film thickness, J&W Scientific, Folsom, CA, USA), the injector temperature was 200 and detector 250 \({}^{\circ}\)C according to the method in [PERSON] et al. [15]. At the start the column remained at 33 \({}^{\circ}\)C for 5 min then increased at the rate of 2 \({}^{\circ}\)C/min up to 50 \({}^{\circ}\)C. Finally, it was adjusted at 225 \({}^{\circ}\)C by increasing at the rate of 5 \({}^{\circ}\)C/min. A preliminary study was conducted for the first identification of off-flavoring volatile compounds by using GC-MS under the same conditions (but with a column length of 60 m), by matching their spectra with those present in Wiley-NBS library mass spectral data.  
The same volatiles were purchased as standard compounds that were first identified at GC-MS in the preliminary study. The compounds Acetaldehyde, Acetone, Butanal, Butanone, Ethanol, Hex-Propanone and Hexanon were purchased from Chem Service (West Chester, PA, USA). The compounds Propanol, Eth-Butanol, Hexanal, 2 Meth-1-Pro-ol, Heptanal, 3 Meth-1 Butanol, Trans-2-Hexenal, Octanal were purchased from Sigma-Aldrich Corp. St. Louis, MO, USA. The compounds 2,3-Butanediol, Hex-Propanone, 1-Hexanol and 1-Pentanol were purchased from purchased from Alltech (Deerfield, IL, USA). These standard compounds were run on GC-FID and got a (Figure 1) of peaks of different volatiles according to their retention times. After that, the milk samples were run on GC-FID and identification and quantification of volatiles were achieved using detector response factors established from standard compounds and their retention time.  
Figure 1: Standards volatile compounds run on gas chromatography coupled with flame ionization detector (GC-FID).
# Calculation of Guided Wave Dispersion Characteristics Using a Three-Transducer Measurement System
###### Abstract  
Guided ultrasonic waves are of significant interest in the health monitoring of thin structures, and dispersion curves are important tools in the deployment of any guided wave application. Most methods of determining dispersion curves require accurate knowledge of the material properties and thickness of the structure to be inspected, or extensive experimental tests. This paper presents an experimental technique that allows rapid generation of dispersion curves for guided wave applications when knowledge of the material properties and thickness of the structure to be inspected are unknown. The technique uses a single source and measurements at two points, making it experimentally simple. A formulation is presented that allows calculation of phase and group velocities if the wavepacket propagation time and relative phase shift can be measured. The methodology for determining the wavepacket propagation time and relative phase shift from the acquired signals is described. The technique is validated using synthesized signals, finite element model-generated signals and experimental signals from a 3 mm-thick aluminium plate. Accuracies to within 1% are achieved in the experimental measurements.  
Keywords:guided waves; lamb wave; dispersion curves; phase velocity; group velocity; signal processing
## 6 Open Challenges, Future Prospects and Research Directions
### Standardization and Regulatory Challenges  
For FSO communication to achieve widespread adoption, it is essential to establish standardized protocols and address regulatory challenges. This section discusses the current state of FSO standardization efforts and the regulatory issues that need to be resolved to facilitate the deployment of FSO systems on a global scale. By analyzing the current state of FSO standardization, this section highlights the progress made, the challenges remaining, and the significance of these efforts in facilitating the growth and integration of FSO systems into global communication networks.