esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Target Tracking Method
### Mutual Observation Information Based on a Cooperative Platform
#### 3.1.2 Error Analysis  
The Earth coordinates of UAV \(A\) and \(B\) and the cooperative platform are \(\mathbf{X}_{A}^{e}\), \(\mathbf{X}_{B}^{e}\), \(\mathbf{X}_{C}^{e}\). The transformation matrix of the earth coordinate system to the local geographic coordinate system is \(\mathbf{T}^{e}\).  
The true value of the local geographic coordinates of UAV \(B\) at the center of UAV \(A\) is  
\[\mathbf{X}_{AB}=\mathbf{T}_{A}^{e}(\mathbf{X}_{B}^{e}-\mathbf{X}_{A}^{e}) \tag{15}\]  
The approximation of the local geographic coordinates of UAV \(B\) in the center of UAV \(A\) is  
\[\begin{array}{rl}\mathbf{\tilde{X}}_{AB}&\approx\mathbf{X}_{AC}-\mathbf{X}_ {BC}\\ &=\mathbf{T}_{A}^{e}(\mathbf{X}_{C}^{e}-\mathbf{X}_{A}^{e})-\mathbf{T}_{B}^{e }(\mathbf{X}_{C}^{e}-\mathbf{X}_{B}^{e})\\ &=(\mathbf{T}_{A}^{e}-\mathbf{T}_{B}^{e})\mathbf{X}_{C}^{e}+\mathbf{T}_{B}^{e }\mathbf{X}_{B}^{e}-\mathbf{T}_{A}^{e}\mathbf{X}_{A}^{e}\end{array} \tag{16}\]  
Comparing Equations (15) and (16), we can obtain the observation information of UAV \(A\) on UAV \(B\) so that the approximation is approximately equal to the true value, then we need to ensure that  
\[\begin{cases}\mathbf{X}_{CB}=-\mathbf{X}_{BC}\\ \mathbf{T}_{A}^{e}=\mathbf{T}_{B}^{e}\end{cases} \tag{17}\]This means that the following two conditions are satisfied: (1) the distance between UAVs \(A\) and \(B\) cannot be too far; (2) the distance between UAV \(B\) and the cooperative platform cannot be too far.
## 3 Traffic Model
The conducted research demonstrated that M/M/1 and M/G/1 models are sufficient for analysis of \(E(CSD)\) and \(E(CDD)\) times. They offer good conformity with reference simulation results and lead to fast calculations without the need for additional experimental data. It is worth noting that the compliance of the analytical and simulation results for individual service systems (mean message waiting times) was not analyzed. The aim of the performed research was to assess overall IMS/NGN network responses, which are important for users and operators. The experiments presented in Ref. [11] indicate that the overall analytical traffic model containing the IMS/NGN service stratum and the MPLS-based transport stratum works correctly. Consequently, it can be used in thorough investigations on how parameters of traffic sources and an IMS/NGN network with an MPLS-based transport stratum influence CPP parameters. The results of these investigations are presented in the next section.
# A CNN-Based Strategy to Classify MRI-Based Brain Tumors Using Deep Convolutional Network
## 1 Introduction
Deep learning was recently introduced to the medical imaging area and has shown substantial success in classification problems, specifically the multiclass classification problem with better accuracy [18; 19; 20; 21; 22]. Deep learning algorithms use a matrix of multiple layers of asymmetric processing techniques to extract features. Convolutional Neural Network (CNN) comprises several convolutional layers, pooling layers, and fully connected layers for segmentation and feature extraction, reduction of the spatial size of the representation, and classification [23; 24]. The frequently used activation functions in CNN include ReLU (Rectified Linear Unit) [25], FReLU [26], LeakyReLU [27], Swish [28], ACON (Activate or Not) [29], and SoftMax [30]. In this study, our goal was to create an artificial model which can predict the types of tumors accurately within a few seconds. The proposed model is a modified CNN model, inspired by the Visual Geometry Group (VGG) architecture, which performs significantly well than many other architectures. Working with medical images is harder than usual, because of the sensitivity of the results, as a wrong diagnosis can result in a life-threatening condition. Therefore, our focus was primarily on getting a high accuracy that can exceed the results of other commonly used architectures. After the introduction, we discuss related works regarding our study and their research analogy in the Related Works section. Training of the model, along with performance measurements presented in the Materials and Methods section. We have analyzed the result of our proposed architecture in the Results section, discussed the findings and compared its performance with other methods in the Discussion section. Finally, we conclude the paper in the Conclusions section.
## 4 Emerging Biogas Upgrading Technologies
### Separation via Phase Addition
#### 4.1.2 In Situ Methane Enrichment  
In situ methane enrichment aims to increase methane's proportion in the biogas inside a digester. Although the first approach to this technology was performed more than 20 years ago, it is still under development at a pilot scale [66; 98]. However, the limitations of this technology could be addressed by combining two or more methods to create a hybrid system [98]. Its main advantages are its lower CAPEX and operational expenditure (OPEX) compared to developed upgrading technologies [99].  
It consists of continuously stripping CO\({}_{2}\) from digester sludge [99]. Since CO\({}_{2}\) is soluble in water, it is present in the liquid phase of the digester sludge. Sludge is circulated to a desorption column, where the CO\({}_{2}\) is desorbed, and the sludge is reconducted back to the digester. This causes a shift in the carbonic equilibrium in the digester towards the liquid phase, resulting in an increase in methane proportion in the biogas leaving the digester [49]. The desorption of CO\({}_{2}\) from the sludge can be achieved by pumpinga counter-flow of air or nitrogen to the sludge or applying a vacuum (0.4 bar) in the desorption column [66; 100]. In some cases, organic acids can be added to the desorption column to enhance CO\({}_{2}\) removal [100]. In addition to CO\({}_{2}\), H\({}_{2}\)S and NH\({}_{3}\) can also be removed with this technology [66; 100].  
The results and obtained biogas purity vary among the references, as different substrates lead to different results, and it is difficult to optimize the operation parameters (Table 5) [100]. The results of a simulation show that methane purity of 95% and methane losses of 2% can be achieved, but the obtained experimental results from the simulated facility show purity of 87% and losses of 8% [49; 100]. The results obtained from another simulation with other characteristics show an increase in the methane concentration from 58% to 69% and methane losses of 1.3% [100]. The Swedish Institute of Agricultural and Environmental Engineering (_JTI_) has conducted further research and obtained other data for a pilot plant using manure as a substrate. The results show an increase from 60% to 81% of methane in the biogas, a reduction of H\({}_{2}\)S from 1100-1500 ppm to 150-300 ppm, and methane losses of 1,8%. Also, nitrogen from the was reduced by 11-21% [101]. When using N\({}_{2}\) in the desorption step, values of 95% and 87% methane concentration have been obtained at the laboratory and pilot scales, respectively; however, methane losses of 8% have been reported [66]. These results show that, although in situ methane enrichment significantly increases the methane content of biogas, it does not reach sufficient purity. Therefore, it can be used as a pretreatment step or in combination with other upgrading technologies.
###### Abstract  
A high beam quality hundred picoseconds single-longitudinal-mode (SLM) laser is demonstrated based on stimulated Brillouin scattering (SBS) pulse compression and aberration compensation. Flash-lamp-pumped Q-switched Nd\({}^{3+}\):Y\({}_{3}\)Al\({}_{5}\)O\({}_{12}\) (Nd:YAG) SLM laser with Cr\({}^{4+}\):Y\({}_{3}\)Al\({}_{5}\)O\({}_{12}\) (Cr\({}^{4+}\):YAG) as a saturable absorber is used as the seed source. By combining master-oscillator-power-amplifier (MOPA), a compact single-cell with FC-770 as working medium is generated as pulse compressor. The 7.8 ns SLM laser is temporally compressed to about 450 ps, and 200 mJ energy is obtained at 1064 nm without optical damage. The energy stability is better than 3% with beam quality factor \(M^{2}\) less than 1.8, which makes this laser system an attractive source for scientific and industrial applications.  
p pacs: +
Footnote †: These authors contributed equally to this work.
# Measurement of Dielectric Properties of Thin Materials for Radomes Using Waveguide Cavities
## 3 Results
### Measurement Setup  
#### 3.2.1 Material Preparation  
For the cavity measurements, two pairs of shims were manufactured out of aluminum with the irises in the center of each shim and the bolt holes to the WR650 and WR137 standards. The foam to hold the sample in place was also cut to fit each cavity. This foam was split into two pieces so a sample piece of material could be held between the blocks of foam within the y-z plane. The pieces of fabric were manually cut to the size of the cavity with a ruler and scissors.
# Ultrasonic Nondestructive Testing Image Enhancement Model Based on Super-Resolution Imaging
## 3 Experiments  
### Experimental Setup and Test Specimens  
The experimental data of laser ultrasound is obtained through the more representative \"USimgAIST\" [33] data set. The experimental system uses a pulsed laser to scan the material to generate ultrasonic waves. A receiving sensor is installed on the tested material to obtain the ultrasonic propagation signal. The laser ultrasound system is shown in Figure 6 below, and the relevant main components are marked in the comments picture.  
Figure 5: Flowchart of laser ultrasound super-resolution.  
In laser ultrasonic nondestructive testing, the type, shape, size, and depth of defects are the variable factors of detection. Therefore, a batch of stainless-steel plates with different defects were prepared in the experiment and their uniform size was 3 mm in thickness and 100 mm \(\times\) 100 mm in size. These defect samples can be divided into two categories: drilled holes with a diameter of 1 mm-5 mm and slits with lengths ranging from 3 mm to 10 mm. Table 1 gives the details of the samples.  
The experiment also prepared an undamaged stainless-steel plate as a comparative experiment. To better explain the robustness of the system, let the transducer vary the incident angle from 0 to 90 at 22.5-degree intervals. A total of 7004 laser ultrasound images were collected in the experiment. They were divided into 3615 non-defective images and 3389 defective images. In order to ensure the simplicity and accuracy of the experiment, we randomly selected 1954 pictures as the training set and testing set of the model. We used 80% of the total data set as training set and the remaining 20% as test set.
## 2 Algorithm
### Particle Swarm Optimization  
As a popular parameter optimization tool, Particle Swarm Optimization (PSO) is also applied in this paper. Besides SVM, PSO is introduced to obtain the optimal parameters in SVM. It is an evolutionary computation technique [24] that was inspired by the social activity of birds primarily and then established a simplified model based on birds searching for food. Birds in a swarm cooperate with each other like swarm intelligence to search for the optimal position in order to obtain the food. The group of birds whose amount is m, called a \"particle\", form a population in a D-dimensional target search space. If the solution space of the optimization problem is \(D\) dimensions, the space vector \(X_{i}=(x_{i1},x_{i2},x_{i3},\cdots x_{iD})\) is represented as the \(i\)-th particle, where \(i=1,2,\cdots,m\), \(X_{i}\) is the position of the \(i\)-th particle and also a possible solution.  
The velocity and position of the particles are iterated to obtain the equation as follows [35]:  
\[v_{id}^{t+1}=\omega\cdot v_{id}^{t}+c_{1}\cdot r_{1}\cdot(p_{id}-x_{id}^{t})+c _{2}\cdot r_{2}\cdot(p_{gd}-x_{id}^{t}), \tag{8}\]  
\[x_{id}^{t+1}=x_{id}^{t}+v_{id}^{t+1}, \tag{9}\]  
where \(V_{i}=(v_{i1},v_{i2},v_{i3},\cdots v_{iD})\) is the velocity of the \(i\)-th particle, and \(P_{i}=(p_{i1},p_{i2},p_{i3},\cdots p_{iD})\) is the optimal position of this particle. The optimal swarm position is \(P_{g}=(p_{g1},p_{g2},p_{g3},\cdots p_{gD})\). Under the condition of the \(i\)-th particle at the \(t\)-th iteration, \(x_{id}^{t}\) and \(v_{id}^{t}\) are the \(d\)-th location and speed component. Positive coefficient \(c_{1}\), \(c_{2}\), \(r_{1}\) and \(r_{2}\) are the random number, the range is 0 to 1, and \(\omega\) is the inertial weight of PSO algorithm.  
The PSO algorithm can deal with the continuous optimization problem and support multi-point search. Therefore, we take the PSO algorithm determine the parameters of SVM to improve the performance of SVM model.
# IRONEDGE: Stream Processing Architecture for Edge Applications
## 3 Proposed Architectural Framework
### Case Study of the Ferrovia 4.0 Project
#### 3.2.2 Distributing Data Reports and Events  
Considering Apache Kafka as the AMM, an integration with its ecosystem offers a natural extension of its functionalities. Kafka Mirrormaker v2, in particular, can offer the capabilities expected from the Event Output component. In contrast with architectures where the nodes used in the Edge Layer are interconnected, as a single Apache Kafka cluster, with the nodes of the Cloud Layer, in the Ferrovia 4.0 project, it is assumed that there are extended periods of interrupted networking between layers. Additionally, only a subset of topics, those containing Events, will require distribution to the Cloud Layer.  
Kafka Mirrormaker v2 operates by creating pairs of workers that subscribe to a configurable list of topics from the local cluster and publish them to the remote cluster. These pairs are connected by internal buffers. Together with the local persistence of topics, it allows for the composition of a push-like Event Output during periods of network connection.  
The IoT devices considered in the use case require lightweight communication protocols, so using Apache Kafka as the Data Collection is not an acceptable option based on the amount of resources consumed by the IoT devices. Furthermore, the initial implementation case for the case study was a set of LoRa devices, which will have their messages exposed though a local MQ Telemetry Transport (MQTT) broker. As such, it was defined that the Data Collection be an MQTT broker, in common use among IoT-based solutions. This resulted in two possible architectures for the use case:  
1. [label=(0)]
2. K0-WC (with Connect)--This architecture continues the path of using existing integrations from the Kafka ecosystem by using Kafka Connect to read Data Reports from the MQTT broker and inject them into Apache Kafka (see Figure 5a);
3. K1-NC (without Connect)--As shown in Figure 5b, the architecture forgoes the closer integration for a more lightweight approach, requiring that each topology in Apache Storm consumes Data Reports directly from the MQTT broker.
## 5 Results
### Analysis of Adaptive MMAC
#### 5.3.2 Fairness  
Figure 10 describes the JFI metric of the adaptive MMAC protocol. Similar to the previous analysis of AMMAC, it is the fairest of the protocols evaluated in this study. However, in this instance, the adaptive MMAC of the three channel conditions seen in Figure 10a is similarly as fair as E-SA-MMAC, as it takes that mode. It can be seen in Figure 10b,c that when adaptive MMAC takes the BiMMAC route, it is similarly fair to that protocol. Overall, the trend is that the new protocols converge to a similar fairness level of 0.7 JFI as the number of channels and stations increase.  
Figure 9: Normalized throughput comparison of adaptive MMAC versus others. (**a**) Three, (**b**) four, and (**c**) twelve channels.
# Robust Symbol and Frequency Synchronization Method for Burst OFDM Systems in UAV Communication
## 3 Improved Burst OFDM Synchronization Method
### Symbol Timing Synchronization Method Based on 2-Bit Non-Uniform Quantization
#### 3.2.1 The 2-Bit Non-Uniform Quantization Method  
In this work, the received signal and the local training sequence are mapped to a 2-bit message comprising a sign bit and a magnitude bit. The quantization method can be expressed as \(\hat{r}(n)=\{a_{n},b_{n}\}\), where \(a_{n}\) and \(b_{n}\) are binary values representing the sign and magnitude bit of the quantization, respectively. The quantization rules can be expressed as  
\[a_{n}=\left\{\begin{array}{ll}0,&\hat{r}(n)\geq 0\\ 1,&\hat{r}(n)<0\end{array}\right., \tag{10}\]  
\[b_{n}=\left\{\begin{array}{ll}0,&|\hat{r}(n)|<T_{th}\\ 1,&|\hat{r}(n)|\geq T_{th}\end{array}\right., \tag{11}\]  
where \(T_{th}\) is the optimized threshold for quantization. \(b_{n}=1\) implies that the amplitude of \(\hat{r}(n)\) is quantized to a higher value \(T_{1}\), and \(b_{n}=0\) implies that the amplitude of \(\hat{r}(n)\) is quantized to a lower value \(T_{0}\). Here, \(T_{0}\) and \(T_{1}\) are optimized mapping values, which are analyzed in the following sections. Let \(t_{n}^{l}\) and \(t_{n}^{Q}\) denote the real and imaginary parts of \(\hat{r}_{n}\), which will be quantized into \(\left\{a_{r_{n}}^{l},b_{r_{n}}^{l}\right\}\) and \(\left\{a_{r_{n}}^{Q},b_{r_{n}}^{Q}\right\}\), respectively. Similarly, the real part (\(s_{n}^{l}\)) and imaginary part (\(s_{n}^{Q}\)) of the local training sequence at the receiving end are quantized into \(\left\{a_{s_{n}}^{l},b_{s_{n}}^{l}\right\}\) and \(\left\{a_{s_{n}}^{Q},b_{s_{n}}^{Q}\right\}\), respectively.  
Figure 5: Block diagram of the proposed AGC control circuit.
## 3 Results  
### Publication Overview  
The complete database contained a total of 214 articles. After the first screening process, 61 were excluded. From the remaining 153 articles, seven were not available for full-text analysis and 32 were excluded during the second screening phase. In the end, the database of articles comprised a total of 115 articles. In Figure 1, the PRISMA flowchart for systematic reviews is presented. This also describes the evolution of accepted articles throughout the several screening stages, where \(n\) is the number of available articles.  
The gathered results evidenced a growing interest in the presented topic over the last few decades (Figure 2). The earliest report on the development of a human aorta numerical model was in 1992 by [PERSON] [18], and until 2005, only two other works were found [19, 20]. From 2006 until the present, the remaining 111 reviewed articles were published, with 2018 being the year with the highest number of publications (12).  
Regarding the studied anatomical region, it is evident a tendency to model either ATAA [21, 22], AAA [23, 24] or Aortic Dissection (AD) [25, 26] since these present higher incidence among aortic diseases and are potentially more catastrophic. Moreover, several works recreated the biomechanics of healthy subjects [27, 28, 29] as it is relevant to identify the main differences between healthy and diseased states. Regarding the chosen computing platforms, open-source software such as OpenFOAM [11] and SimVascular [30] was used in around 10% of the revised papers. The ANSYS finite element software was the preferred choice, being used in 40% of the works, particularly for CFD and FSI simulations. Abaqus was the second most used platform (20%), being commonly applied to CSM [9, 31] or to FSI [32, 33] studies (coupled with the Abaqus flow module or with ANSYS CFD modules). The entirety of the studied anatomies and the utilised computing platforms are presented in Figure 3.  
Figure 1: PRISMA flow chart of the systematic search strategy.  
Figure 2: Evolution of number of publications per year.
## 4 HSI in Fresh-Cut Product Quality Assessment
### Fresh-Cut Fruits
#### 4.3.3 Green Bell Pepper  
Storage chilling injuries were evaluated also on green bell peppers by [PERSON] et al. (2020) with HSI (400\(-\)2500 nm). A PLS-DA model was developed to distinguish cold-stored fruit (4 \({}^{\circ}\)C) from fresh fruit and fruit stored at higher temperatures (12 \({}^{\circ}\)C) using selected Vis-NIR (694, 719, 751, 813, 886, and 973 nm) or NIR variables (1138, 1244, 1379, and 1642 nm). The model in the Vis-NIR achieved an 83% NER (nonerror rate) in cross-validation, while the model with NIR wavelengths reached a cross-validation NER equal to 81%. Moreover, a PLSR model was developed to predict days of storage at 4 \({}^{\circ}\)C, in order to take any corrective action to avoid further damage. The model was built based on the data extracted from Vis-NIR HSI and using selected wavelengths from VIP (variable importance in projection) scores. Good predictive performances were obtained (R\({}^{2}\)CV = 0.79, and RMSE\({}_{\text{CV}}\) = 0.5 days of storage at 4 \({}^{\circ}\)C), demonstrating that it is a good option for the early detection of CI of green bell peppers and also for industrial on-line applications [34].
# Relay Selection Scheme for Multi-Hop Transmission of MU-MIMO System
## 3 Conventional Relay Selection Scheme
### Random Relay Selection Scheme  
One of the typical relay selection schemes is the random relay selection scheme. The random relay selection scheme randomly selects the relay without considering factors such as the channel condition and SNR of the received signal. Therefore, the random relay selection scheme is very simple, and the relay for multi-hop transmission can be selected quickly. However, since the relay is randomly selected, the appropriate relay may not be selected. Since the relay having a bad channel condition can be selected, the error propagation problem is caused. In order to select the relay with good channel condition, other relay selection schemes have been studied in consideration of factors such as the channel condition and SNR of the received signal.
# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 3 Localization Model  
WiFi fingerprinting is a well-known indoor localization solution, which relies on a fundamental assumption: the WiFi signals measured in the environment have a unique signature that comprises the WiFi fingerprint at a given position. In this section, we only analyze the changing characteristics of RSSI under different interferences. According to the experiment and scene analysis, this paper presents a fingerprint extraction mechanism. The system framework is shown in Figure 2. The symbols used in this paper are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(N\) & Number of grids \\ \(\Psi\) & Sampling matrix \\ \(s\) & The value of RSSI \\ \(B\) & Fluctuation matrix \\ \(b\) & Standard deviation of RSSI \\ \(V\) & The number of votes \\ \(\varphi\) & Measurement matrix \\ \(D\) & The distance matrix between grids \\ \(r\) & Anchor node communication radius \\ \(\delta\) & Localization error \\ \(L\) & Number of anchor nodes \\ \(T\) & Localization error threshold \\ \(\Delta\) & Standard deviation threshold \\ \(w\) & The trust value of fingerprint \\ \(w^{\prime}\) & The trust value of reference point \\ \hline \hline \end{tabular}
\end{table}
Table 1: The symbols used in this paper.  
The algorithm proposed in this article is to detect the secure status of the positioning device. In positioning, if the localization device is damaged or fails, the proposed algorithm filters the device by detecting the secure status of the localization device and selects the data of the localization device that is safe and trustworthy to locate the target. Therefore, when emergencies such as earthquakes, floods and explosions occur in the positioning environment, the proposed algorithm can identify the secure physical devices and locate the targets with safe and reliable data. This paper improves the robustness of the positioning system by using the security identification algorithm.