esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Investigation of Passive Jet Control Method on Bridge Tower Wake
## 3 Results and Discussions
### Dominant Frequency Distribution  
Here, we processed the wind speed time history data to gain the power spectrum density. In this part, if there is zero data history, this is also taken into consideration, which means the total length of time history data is the same. We gathered the dominant frequencies of the spectrum in all measurement points, then transformed them into dimensionless frequencies (Strouhal number, \(St=f_{s}L/U_{0}\), where \(f_{s}\) is the shedding frequency, i.e., the dominant frequency in the wake). The distribution of dimensionless frequencies is obtained, as shown in Figure 9. To eliminate interference from dominant frequencies with small peak values, and for the comparison between cases, a threshold value of power spectral density (PSD) was set. Only the frequencies whose peak value was over the threshold value were counted, and the remaining zones were filled with zeros. If no PSD threshold was set, there is always a dominant frequency anywhere in the wake field, no matter how weak the energy level corresponding to that frequency. The threshold value of PSD in Figure 9 is set as 0.5. In this figure, most St numbers of wake in each case are uniform, but some  
Figure 8: Turbulence intensity distribution at \(X/B=8.48\).  
divergences are mostly located in the middle of wake, especially in Case 0. Comparing the cases, we can find that divergences decline in Cases 1 and 2; especially in Case 1, the St numbers are almost uniform, and the distribution appears clean.  
In Cases 3 and 4, the distribution region shrinks to 0 and 10 degrees. For a better comparison of the area change in the distribution region, Figure 10 shows the dimensionless area (\(S_{2}/BD\)) of the region in each case. The threshold value of PSD is also set as 0.5. Two trends in different sections of the yaw angle appear. When the yaw angle is small (0-10 degrees), Cases 1, 3, and 4 all reveal a great control effect. Cases 1-4 attain [36.0, \(-47.2\), 59.1, 50.6] and [63.3, 26.9, 78.0, 61.9] percent reduction of area in 0 and 10 degrees, respectively. However, when the yaw angle increases, the area expands, where Case 1 shows the least expansion, except at 80 degrees, with no more than 4% of the area expanding. As in the turbulence intensity distribution, a drop of  
Figure 9: Dimensionless dominant frequency (\(fD/LU_{0}\)) distribution of tower wake.  
the area appears at 80 degrees in Case 0, which causes the amplification of the negative control effects of all cases at 80 degrees. Case 2 has the same trend of the area variation by degrees as Case 1, but the effect is worse--29% amplification of area is the maximum at 30 degrees, without considering 80 degrees. Cases 1 and 3 have larger suction coefficients, so these cases perform better than others. If only a small yaw angle is taken into consideration, Case 3 would be recommended, because the position of the suspenders would come out of the wake region at large yaw angles. For engineering applications, Case 1 would be better for various yaw angles.
###### Abstract  
In an area where unmanned aerial system (UAS) traffic is high, a conflict detection system is one of the important components for the safety of UAS operations. A novel UAS traffic management (UTM) monitoring application was developed, including a conflict detection system using the inverted teardrop area detection based on real-time flight data transmitted from the network remote identification (Remote ID) modules. This research aimed to analyze the performance of the UTM-monitoring application based on flight test data using statistical and machine learning approaches. The flight tests were conducted using several types of small fixed-wing unmanned aerial vehicles (UAVs) controlled by a human pilot using a Taiwan cellular communication network in suburban and rural areas. Two types of scenarios that involved a stationary, on-the-ground intruder and a flying intruder were used to simulate a conflict event. Besides the statistical method calculating the mean and standard deviation, the random forest algorithm, including regressor and classifier modules, was used to analyze the flight parameters and timing parameters of the flight tests. The result indicates that the processing time of the UTM application was the most significant parameter to the conflict warning parameter, besides the relative distance and height between UAVs. In addition, the latency time was higher for the flight in the rural area than the suburban area and also higher for data transmitted from the flying position than the ground position. The findings of our study can be used as a reference for aviation authorities and other stakeholders in the development of future UTM systems.  
UAS traffic management; conflict detection; network remote ID; inverted teardrop algorithm; communication latency; flight test; random forest algorithm Article
## 2 Turbo Coded OFDM with MIMO (2 \(\times\) 2)  
The turbo coded OFDM based on MIMO antennas is depicted in Figure 1. The information sequence of bits \(\mathbf{m}\) is passed through recursive convolutional encoder-1 (RSC-1) that forms the parity sequences of bits \(\mathbf{p}1\). At the same time, information sequences of bits \(\mathbf{m}\) are also fed into recursive systematic convolutional encoder-2 (RSC-2) via random interleaver (RI) that gives parity sequence of bits \(\mathbf{p}2\). The interleaver rearranges the information bits according certain design criteria. The purpose of the interleaver is to de-correlate the information bit sequence so that an iterative decoding, i.e., based on exchange of extrinsic information between the constituent decoders, can be applied [19]. The two corresponding parities sequences of bits along with information bits form the encoded turbo frame \(\mathbf{f}\). The encoded sequences of bits in turbo encoded frame \(\mathbf{f}\) are mapped into a mapper that generates digitally modulated complex symbols \(\mathrm{c}\), i.e., QPSK, 16 QAM etc. Then, Alamouti encoding [20] is applied to obtain the two parallel encoded signals \(\mathrm{c}_{1}\) and \(\mathrm{c}_{2}\). At time slot 1 and 2 (i.e., \(t_{1}\) and \(t_{1}+T\) where \(T\) is OFDM symbol time) \(\left(\mathrm{c}_{1},\mathrm{c}_{2}\right)\) and \(\left(-\mathrm{c}_{2}^{*},\mathrm{c}_{1}^{*}\right)\) are sent in two parallel paths, respectively. Then N-point inverse discrete Fourier transform (IDFT) operation is applied to these sequences of symbols, which transforms the corresponding signal in time-domain. Finally, a cyclic prefix is appended to get corresponding OFDM symbols, which are transmitted with the antenna \(\mathrm{T}_{\mathrm{x}1}\) and antenna \(\mathrm{T}_{\mathrm{x}2}\) respectively via fast frequency selective Rayleigh fading channel \(\mathrm{H}\). The insertion of a cyclic prefix transforms the linear convolution operation into circular convolution operation. A well-known property of the DFT is that circular-convolution in time-domain results in multiplication in frequency-domain. Moreover, it is assumed that channel entries do not change for two consecutive Alamouti space-time block codes. Therefore, the channel qualifies to be called a fast Rayleigh fading channel. Also, the transmitter does not know the about channel entries and receiver has a full knowledge about channel \(\mathrm{H}\).
## 2 Improved Wavelet Threshold Denoising Method
### Method Improvement via Optimizing Wavelet Parameters  
#### 2.4.1 Optimal Wavelet Basis  
Unlike the Fourier transform, which uses a linear combination of sine functions to fit the signal, there are different options for wavelet basis functions in wavelet transform, each of which makes different trade-offs in terms of compactness and smoothness of the wavelet. Since no wavelet basis achieves optimal effects in all cases, the selection of the wavelet basis function relies on the characteristics and the given tasks of the target signal.  
This paper intends to use wavelet analysis to conduct signal denoising. Therefore, the optimal wavelet basis function is defined as the complete separation of the input signal into each frequency layer. The more zero wavelet coefficients are obtained after the wavelet transform, the lower the probability of aliasing in scale domain occurring, with the result that better separation results could be expected. In another words, the smaller the value of the detail coefficients, the lower the probability of aliasing occurring in scale domains, with the result that a better and cleaner decomposition effect can be obtained. Referring to [PERSON]'s research results, the Sparse Index (SI) is introduced to evaluate the matching degree between the wavelet basis and the target signal [21]. The formula is shown in Equation (4):  
\[\text{SI}=\sum_{j}\sum_{k}\frac{\left\|W_{g,s}(j,k)\right\|^{2}}{\left\|W_{g,s }(j,k)\right\|^{2}+\varepsilon} \tag{4}\]  
where \(\varepsilon\) is an infinitesimal constant and \(W_{g,s}(j,k)\) are the detail coefficients at scale \(j\) and spatial position \(k\).  
Figure 2: Wavelet threshold denoising process.  
If \(W_{g,s}(j,k)\approx 0\), SI = 0; else if \(W_{g,s}(j,k)\
eq 0\), SI = 1. SI indicates the number of non-zero detail coefficients. The smaller SI is, the better the separation results achieved.
## 2 Preliminaries
### Hessenberg Decomposition  
The process of decomposing a general matrix \(X\) into orthogonal similarity transformations can be achieved through the utilisation of Hessenberg decomposition, as stated in reference [49] and represented by Equation (4).  
\[X=QHQ^{T} \tag{4}\]  
If \(i>j+1\), then \(h_{ij}\) is equal to zero. This is due to the fact that matrix \(Q\) is orthogonal and matrix \(H\) is an upper Hessenberg matrix, which renders them incompatible with each other. Typically, the Hessenberg decomposition is obtained through the utilisation of a household matrix (\(\rho\)). The household matrix can be categorized as an orthogonal matrix.  
\[\rho=\frac{I_{n}-2 uu^{T}}{u^{T}u} \tag{5}\]  
The variable \(u\) in the equation for \(\rho_{n}\) represents a non-zero vector, while \(I_{n}\) denotes an identity matrix with dimensions of \(n\times n\). When the size of matrix A is \(n\times n\), the entire process consists of \(n-2\) steps. The Equation (6) is utilised for the computation of Hessenberg decomposition.  
\[H =(\rho_{1},\rho_{2},...,\rho_{(n-3)},\rho_{(n-2)})^{T}A(\rho_{1}, \rho_{2},...,\rho_{(n-3)},\rho_{(n-2)}) \tag{6}\] \[H =Q^{T}AQ\] \[A =QHQ^{T}\]  
The proposed watermarking method employs both the Hessenberg decomposition and the LWT. The suggested technique for watermarking exhibits imperceptibility to the human visual system and resilience to a diverse range of attacks. The Hessenberg decomposition is utilised to transform a generic matrix \(A\) into orthogonal similarity transformations. Colour images are composed of individual colour channels, each of which consists of a grayscale value ranging from 0 to 255. The orthogonal matrix exhibits minimal alteration if and only if the Hessenberg decomposition is employed to embed the pixel block. Upon examining a unitary matrix, it was observed that the elements in the succeeding column bear a striking resemblance to those in the preceding column. So, no two components of \(Q\) are identical to one another. Modifying the adjacent column has the potential to render the watermark indiscernible. The recommended approach combines FRT and Hessenberg embedding to achieve strong watermarking and a large payload. The watermark must be encoded so that it can be extracted from the image even if the image processing pipeline has been compromised. The use of frequency-domain modifications in image watermarking is, therefore, very common.
# An Investigation into the Influence of Sample Height on the Consolidation Behaviour of Dredged Silt
The influence of size effect on the stress-strain behaviour and strength of coarse-grained soil has garnered attention as well. Laboratory triaxial compression and direct shear tests have illustrated the specimen size's pronounced impact on the stress-strain behaviour of sands, with larger specimens generally showcasing reduced shear strengths [34]. [PERSON] et al. [35] performed confining pressure stress path tests on sandstone transition materials of varied sample diameters and maximum particle sizes using a triaxial apparatus. Their research particularly honed in on how sample diameter and particle size influenced the stress-strain-strength characteristics. Further, [PERSON] et al. [36] scrutinized the size effect on sandy gravel deformation using both triaxial compression and bearing capacity tests on two distinct sandy gravel mixture sizes, and they found that extensive experiments can eliminate the error of experimental results caused by the size effect to a certain extent. [PERSON] and [PERSON] [37] tested sands from five distinct regions using three different-sized square shear boxes (60 mm, 101.6 mm, and 304.8 mm). Their findings indicate that the friction angle was inversely proportional to specimen size. [PERSON] et al. [38] also engaged in triaxial consolidation drained shear tests on rockfill samples of varying diameters, aiming to understand the sample size's effect on the stress-strain and strength properties of coarse-grained soil. In another study, [PERSON] et al. [39] contrasted the shear strengths of expansive soil obtained from both indoor and field shear tests, identifying significant disparities attributed to the size effect. They further evaluated the origins of the size effect and proposed a reduction coefficient for shear strength in indoor tests. [PERSON] [40] aggregated a vast array of experimental data to discern how sample size affected the shear strength of expansive soil. He subsequently crafted a method to ascertain a reasonable diameter and shear strength indices c and \(\varphi\) when evaluating the shear strength of expansive soil. Notably, there is a paucity of research concerning the effect of sample height pertaining to dredged silt. To bridge this gap, the present study probes the dredged silt found on the ground surface of reinforcement treatments and land reclamation projects in Qianwan, Shenzhen. Using an automatic air pressure consolidometer, consolidation tests were conducted on dredged silt samples of three distinct heights. The study delved into the variances in compression, deformation, permeability, and consolidation attributes across samples of different sizes. Additionally, comparisons between the permeability and consolidation properties of regional marine silt and dredged silt were drawn, offering a theoretical foundation for the engineering practices of dredged silt foundation treatments.
# Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
## 6 Discussion  
The proposed data collection scheme is also applicable to the scenario where multiple mobile sinks employed in network simultaneously, by considering the distance between the mobile sinks, a feasible scheme is proposed. To show how it works in detail, two mobile sinks are taken as an example, as shown in Figure 18, in which \(R_{l}\_1\), \(R_{l}\_2\), \(R_{l}\_3\) and \(R_{l}\_4\) are the long radio ranges of \(v_{\sin R1}\), \(v_{\sin R2}\), \(v_{\sin R3}\) and \(v_{\sin R4}\). The mobile sinks move in network randomly, and according to the distance between them, there will be two data collection cases, as shown in Figure 18a,b, respectively.  
In Figure 18a, the distance between the two mobile sinks satisfies:  
\[d(v_{\sin R1},v_{\sin R2})\geq R_{l}\_1+R_{l}\_2. \tag{20}\]  
In this scenario, the mobile sinks could use the proposed data collection scheme to perform their own data collection tasks directly.  
In Figure 18b, the distance between the two mobile sinks satisfies:  
\[d(v_{\sin R3},v_{\sin R4})<R_{l}\_3+R_{l}\_4. \tag{21}\]  
In this case, the data collection scope of the two mobile sinks are overlapped. To clarify the data delivering destination for nodes within the overlapping area, when a node receives a tag message for the first time, it takes this SID of a mobile sink included in the message as its data forwarding destination until the data collection task is completed, and during this process, it neglects other mobile sinks' data collection task even if it receives the tag messages they broadcast.  
This scheme increases the flexibility of data collection, and by using multiple mobile sinks, it can collect data from multiple network regions simultaneously, which is applicable in prospect applications.
# CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario
## 1 Introduction  
### Telecommunication Evolution and Status of 5G Deployment  
During the last two decades the communication networks evolved from the first generation networks (1G) to the fifth generation (5G) by introducing a countless number of novel ideas to meet the stringent requirements set out [1]. Beyond 5G, some fundamental issues that need to be addressed are higher system capacity, higher data rate, lower latency, higher security and improved quality of service (QoS). For what concerns a global adoption of 5G, especially for low income and middle-income countries, investments for new generation of mobile telecommunication networks and infrastructure could be a crucial factor for economic growth, facilitating education, social cohesion, ideas exchange, access to global information, knowledge and learning. This kind of investment has rapidly become a priority, especially over the last couple of years, because of restrictions due to the COVID-19 pandemic and for this reason we have seen the expansion of 5G in countries across the world with rapid acceleration [2]. The penetration rate of 5G may be faster in comparison with 4G and previous technologies for different reasons, mainly due to the possibility of exploiting the existing LTE core network and the growing marketing of mobile technologies, favorable to a widespread 5G coverage, in Europe, the USA and Asia, primarily available in larger cities and in some regions [3, 4]. In certain parts of the world, 5G has already been deployed, as shown and detailed in [2, 5, 6, 7] (see Table 1). This means a further expansion of 5G across largergeographical regions unlocking the full potential of 5G, network slicing, ultra-low latency, improving the mobile experience of users. While the 5G systems will offer significant improvements, they will not be able to fulfill the demands of future emerging intelligent systems; 5G networks will not have the capacity to deliver a completely automated and intelligent network that provides everything as a service and a completely immersive experience [8]. For this reason, there is a growing interest in studying a new paradigm of wireless communication as the sixth-generation (6G) system, with the full support of artificial intelligence, expected to be implemented between 2027 and 2030 [8], as supported in a huge number of papers [8; 9; 10; 11; 12; 13] where authors agree that the 6G networks will introduce a key enabler of intelligence, new technologies and network features. For further up-to-date details on very recent 6G studies, see Table 2.
## 3 Methods
### The Data Collection and Analysis Methods
#### 3.4.2 Surveys and Questionnaires  
After completing each individual gesture type on each platform, the participants filled out a usability and preference survey to evaluate their subjective user experience. In particular, to quantitatively and statistically measure user feedback on the gesture types, we used the \"User Experience Questionnaire--Short Version (UEQ-S)\" [56]. Based on three main criteria--(a) pragmatic quality (usability aspects such as efficiency, dependability, and usefulness), (b) hedonic quality (emotional aspects such as satisfaction and enjoyment), and (c) overall quality--the participants individually assessed the three gesture types that they used to interact with the interfaces of the TV and XR platforms through semantic 7-point scales. This survey was conducted immediately after each gesture type task was completed and lasted for 3 min. The reason for conducting a quick survey right after completing each gesture type was so that the participants could provide a UX rating for each of the six condition experiences while their memory was still fresh. Given that each gesture-type task was conducted three times on the TV platform and three times on the XR platform, the total number of surveys conducted was six.  
In addition to the UEQ-S, the survey includes a few custom questions targeting preferences and perceived workload. For example, we asked the participants to rank the three gesture types by preference for each platform and to rate how physically or mentally demanding each gesture interaction felt. We also included an overall comparison question (e.g., \"Which platform did you find easier to interact with, and why?\"), with multiple-choice and open-ended explanations.
# Study of the Ionospheric Scintillation Radio Propagation Characteristics with Cosmic Observations
## 2 Data and Methods
### Ionospheric Scintillation Intensity
The \(\alpha\)-\(\mu\) distribution takes both the nonlinearity effect and wave cluster effect into account. With increasing amplitude scintillation index \(S_{4}\), the peak value of the \(\alpha\)-\(\mu\) distribution decreased, and with a corresponding smaller amplitude or SNR, the curve diverged. Under the same \(S_{4}\), a larger \(\alpha\) will decrease the peak value of the \(\alpha\)-\(\mu\) distribution. The width and thickness of the curve tail also increased. The parameters \(\alpha\) and \(\mu\) have hyperbolic-like features. The \(\alpha\) increases with the decrease of \(\mu\). The \(\kappa\)-\(\mu\) distribution model is developed based on the \(\alpha\)-\(\mu\) distribution hypothesis that the signal is a combination of multipath signals passing through heterogeneous propagation media [27; 31]. The model is defined as [31]  
\[f_{R}(r)=\frac{2\mu(1+\kappa)^{(\frac{\mu+1}{2})}}{\kappa^{(\frac{\mu-1}{2})}e^{ \kappa\mu}}r^{\mu}e^{[-\mu(1+\kappa)^{2}]}r^{\mu}e^{[-\mu(1+\kappa)^{2}]}r^{ \mu}e^{[-\mu(1+\kappa)^{2}]}r^{\mu} \tag{7}\]  
where \(I_{\mu-1}\) represents the modified first kind Bessel function of order \(\mu-1\). \(\kappa\) represents the ratio between the dominant components versus the power of the scattered wave, which can be calculated by  
\[\kappa=\frac{\sqrt{2}[E\big{(}R^{4}\big{)}-1]}{\sqrt{2E^{2}(R^{4})-E(R^{4})-E( R^{6})}}-2 \tag{8}\]  
Similar to the \(\alpha\)-\(\mu\) distribution, the parameters \(\kappa\) and \(\mu\) are related to the S4 index via  
\[S_{4}=\sqrt{\frac{1+2\kappa}{\mu(1+\kappa^{2})}} \tag{9}\]