esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Modelling of Ricochet
### Calculation of Ricochet Range  
To conveniently define the motion trajectory of the projectile after the first impacting, new coordinate systems are established herein, which include a fixed and moving one. The \(X\) axis of the former is parallel to the direction of \(v^{\prime}_{c}\), and the \(Y\) axis is perpendicular to that. For the latter, the mass centre of the projectile after the \(t\) moment of the first impacting is \(C_{1}\), where \(t\) is the time interval between two impacts. Figure 3 shows the diagram of the motion trajectory of the ricocheting projectile, and the equation of the relative movement trajectory \((x^{\prime},y^{\prime})\) of its vertex is defined as:  
\[\left\{\begin{array}{c}x^{\prime}=l^{\prime}\cdot\cos(\theta+\beta-\omega_{2 }t)\\ y^{\prime}=-l^{\prime}\cdot\sin(\theta+\beta-\omega_{2}t)\end{array}\right. \tag{8}\]  
where \(l^{\prime}\) is the distance from the mass center to the vertex. By a coordinate transformation, the equation of absolute movement trajectory \((x,y)\) of the vertex can be expressed as:  
\[\left\{\begin{array}{c}x=v_{c}t+l^{\prime}\cdot\cos(\theta+\beta-\omega_{2 }t)\\ y=l^{\prime}\cdot[\sin(\theta+\beta)-\sin(\theta+\beta-\omega_{2}t)]\end{array}\right. \tag{9}\]  
Figure 2: Momentum analysis in terms of the first impacting.  
Similarly, the absolute movement trajectory \((x_{PN},y_{PN})\) of the vertex in the ricochet process can be obtained. Moreover, the corresponding impacting point \(A_{1}^{\prime}\) can be analytically determined when the two trajectories intersect each other.
# Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction
## 2 Method and Model Establishment
### Frequency Band Feature Extraction Method of UAV Sound
#### 2.2.2 Short Time Fourier Transform of Sound Signal  
Continuous audio signals are long signals with inconsistent sampling lengths and large data volumes. A common approach involves framing-dividing long-duration signals into shorter segments for short-term analysis, typically implemented through the STFT. It is generally believed that the sound signal is non-stationary, but it can be considered stable in a short period of time. Therefore, the sound can be divided into several short-time signals by frame operation. This article set the frame length to 25 ms and the frame shift to 10 m so that there is a smooth transition between frames.  
After the signal is divided into frames, it is necessary to further window the frame signal, The window function can smoothly process the edge of the frame, reduce the discontinuity between frames, and avoid spectrum leakage. In this paper, the Hamming window is used to window the frame signal, which can effectively suppress the sidelobe frequency of spectrum leakage with slightly lower frequency resolution. The formula for the Hamming window is Equation (6).  
\[w(n)=0.54-0.46\cos\left(\frac{2\pi n}{N-1}\right),n=0,1,2,\ldots,N-1 \tag{6}\]  
Here, \(N\) denotes the window length in samples, and \(n\) represents the sample index. After framing and windowing, each frame undergoes Fourier transformation to convert time-domain signals to frequency-domain representations using Equation (7).  
\[x_{sft}(k)=\sum_{n=0}^{N-1}x(n)\cdot e^{-\frac{j\pi}{N}kn},\quad k=0,1,2,\ldots,N-1 \tag{7}\]  
Here, \(x(n)=w(n)\cdot y(n)\) denotes the windowed short-term signal, \(k\) is the frequency domain sampling point, \(j\) is the imaginary unit, and \(x_{sft}(k)\) is the output of the Fourier transform for a single-frame signal. Combining the frame sequence, \(x_{sft}(k,t)\) is the STFT of signal \(y(n)\).
## 2 Materials and Methods  
### Preliminary  
#### 2.1.1 Gaussian Process  
GP is a statistical distribution, whose observations are defined in a continuous temporal or spatial domain. Each input observation is associated with a random variable that subjects to a multivariant Gaussian distribution [35; 36]. Thus, the GP distribution is regarded as a joint distribution of all input random variables. A GP is mathematically denoted by Equation (1), in which \(\mu(\vartheta)\) and \(k(\vartheta,\vartheta*)\) are the mean and covariance function, respectively. In addition, the covariance function can be further represented by Equation (2) [36]:  
\[f(\vartheta)\sim GP(\mu(\vartheta),k(\vartheta,\vartheta*)) \tag{1}\]  
\[k(\vartheta,\vartheta*)=\mathbb{E}[(f(\vartheta)-\mu(\vartheta))(f(\vartheta* )-\mu(\vartheta*))] \tag{2}\]  
Given the known training data \(\{(\vartheta_{i},\xi_{i})\}_{i=1}^{n}\) and the observation is \(\Phi=[\vartheta_{1},\cdots,\vartheta_{n}]^{\mathrm{T}}\), the joint distribution of the observations is represented as Equation (3), where both \(\mu=[\mu(\vartheta_{1}),\cdots,\mu(\vartheta_{n})]^{\mathrm{T}}\) and \(\mathbf{K}\) are \(n\times n\) matrix. The \((i,j)^{\mathrm{th}}\) element in \(\mathbf{K}\) is obtained by Equation (4):  
\[[f(\vartheta_{1}),\cdots,f(\vartheta_{n})]^{\mathrm{T}}\sim\mathcal{N}(\mu, \mathbf{K}) \tag{3}\]  
\[\mathbf{K}_{ij}=k(x_{i},x_{j}) \tag{4}\]  
It is also known as the kernel function in machine learning approaches. Several classical kernel functions for GP are summarized as follows [33; 37]:  
1. Constant: \(k_{C}(x,y)=C\);
2. Linear: \(k_{L}(x,y)=x^{\mathrm{T}}y\);
3. Gaussian noise: \(k_{GN}(x,y)=\sigma^{2}\delta_{xy}\);
4. SE: \(k_{SE}(x,y)=a^{2}\exp(-\frac{\left\|x-y\right\|^{2}}{2\lambda^{2}})\), i.e., squared exponential.  
Based on the Gaussian property, for a new input observation \(O=[o_{1},\cdots,o_{m}]^{\mathrm{T}}\), the GP predicts its distribution \(\mathbf{f}_{*}=f(O)\) by using a joint distribution with that of the known data:  
\[\left[\begin{array}{c}\mathbf{y}\\ \mathbf{f}_{*}\end{array}\right]\sim\mathcal{N}\!\left(\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!
## 4 Result and Discussion
### Results for PDR with Circular-Shaped UAV Movement  
Both the LSGB and BFFFNN models produced consistently high PDR values across the coverage area during a 150 s simulation with circular UAV movement. In fact, both models produced an average PDR of 100%, demonstrating strong performance in preserving reliable information transfer. This finding implies that bandwidth allocation is efficiently optimized by both machine learning techniques to maintain high PDR in fixed-area UAV operations.  
Figure 5: The simulated UAV movement patterns: (**a**) circular, (**b**) random.
# Mechanical and Acoustic Emission (AE) Characteristics of Rocks under Biaxial Confinements
## 3 Results and Discussion
### Stress-Strain Properties
#### 3.2.2 Lateral Strain  
The \(\sigma_{1}\)-\(\epsilon_{2}\) (\(\epsilon_{2}\) is the strain along the \(\sigma_{2}\) direction) curves of granite, marble, and sandstone are shown in Figure 7. The \(\epsilon_{2}\) is an important index to study the mechanical properties of rocks under biaxial compression, which can well reflect the changing trend of crack propagation under biaxial confinements. The main conclusions of the \(\epsilon_{2}\) are as follows: (1) During the simultaneously loading process of the \(\sigma_{1}\) and \(\sigma_{2}\), the \(\epsilon_{1}\) and \(\epsilon_{2}\) both increase in the positive direction, which indicates that the internal natural microcracks are compressed in both lateral and axial directions; (2) In the process of \"keeping \(\sigma_{2}\) and increasing \(\sigma_{1}\)\", the \(\epsilon_{2}\) and \(\sigma_{1}\) initially show a positive correlation when \(\sigma_{2}\) is at a low level (\(\leq\)10 MPa), whereas \(\epsilon_{2}\) is almost constant with the increase of \(\sigma_{1}\) when \(\sigma_{2}\) comes to a high level (\(>\)10 MPa), which proves that the higher \(\sigma_{2}\) can restrict the development of lateral strain; (3) Figure 7a,d,g shows that the \(\epsilon_{2}\) is negatively correlated with \(\sigma_{1}\) when \(\sigma_{1}\) is increasing continuously under \(\sigma_{2}\) of 5 MPa, which proves that a smaller \(\sigma_{2}\) has a lower influence on lateral constraints, and the specimens expand laterally until it failed. With the increase of \(\sigma_{2}\), the larger expansion constraint on the lateral direction of rock induces the less obvious increases of \(\epsilon_{2}\) in the negative direction (Figure 7b,c,e,f,h,i).  
Figure 6: Axial stress–strain curves of rock specimens including (**a**) granite, (**b**) marble, (**c**) sandstone, and the (**d**) elastic modulus.
## 3 Results
### Case Study: Information Gap Analysis Results
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Type of Limitation** & **\# of User Personas** & **Requirement Consolidated Across Personas** \\ \hline Social Media & 5 & Social media information shall include filters by: “deep fakes”, misinformation, bots, verified accounts, etc. \\ \hline Risk Futures & 5 & Risk futures that project risk, as defined by how it is messaged rather than just acres burned, under different management scenarios to link cost of management to risk mitigation benefit. \\ \hline Risk General & 3 & Risk information shall provide uncertainty by each layer: hazard, exposure, vulnerability. \\ \hline \multicolumn{3}{p{113.8 pt}}{} & Risk information should consider scalability beyond data limitations of the United States. \\ \cline{3-3}  & & Risk information shall include more than simple maps of the Wildland Urban Interface. \\ \hline Hazard & 2 & Hazard information shall provide fuel maps that are updated frequently as fuels change. \\ \hline Vulnerability & 2 & Vulnerability information shall include building ignition potential today and into the future. \\ \hline Incident Reporting & 2 & Incident information shall automatically populate based on curated data from different data sources. \\ \hline Exposure & 1 & Exposure information shall include building locations today and likely locations into the future. \\ \hline \multirow{5}{*}{General} & 1 & Information technologies shall focus on proactive solutions rather than only reactive solutions (i.e., suppression). \\ \cline{2-3}  & 1 & Information of value shall include metadata. \\ \cline{2-3}  & 1 & Impact information shall link building damage to insurance policies. \\ \cline{2-3}  & 1 & Information of value shall be verified with local knowledge. \\ \cline{2-3}  & 1 & Information of value shall provide the granularity needed to inform decisions. \\ \cline{2-3}  & 1 & Information technologies shall enable analytics (e.g., trend analyses). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information gaps identified across user personas and a summary definition listed as a requirement for providing that information.
## The Underwater Ocean Environment
The image shows significant amount of organic material and reflections from the plankton. We can also observe the mist effect created by the phytoplankton. (DOM) or suspended sediments [32]. Propagating light is also elastically scattered by thermal fluctuations in water (Rayleigh scattering) and by hydrosol particles suspended in water. The major portion of absorbed energy is transformed into heat. The rest of the absorbed energy is re-emitted as Raman scattering and fluorescence. Elastic scattering occurs without a change in energy only the direction of propagation changes [33]. Underwater optical imaging systems can be broadly classified into two areas: passive and active. Passive systems utilize light in order to image objects that have been illuminated by some source other than that associated with the imaging system. Examples are imaging of objects using sunlight or other sources of illumination such as bioluminescence. Active systems take advantage of a user generated source of light. A simple example is an underwater camera system which uses either strobe or continuous artificial illumination [34]. In both systems (active and passive) backscattering or diffuse reflection (reflection of light waves back to the direction from which they came) is another effect that is commonly seen as shown in Figure 14. In the active system, we can control the intensity of backscatter by reducing the intensity of the light or changing the position of the strobe relative to the camera, whereas in a passive system we need to change the camera orientation or use special filters. Colour propagation underwater is yet another important aspect we need to consider in our simulation. In Figure 15, the absorption coefficients of fresh water at different wavelengths are shown on a logarithmic scale. The ratio between the red spectrum (approximately 650 nm) and the blue spectrum (approximately 450 nm) is almost 40 which means that the red part of the spectrum will be attenuated rapidly and after a short distance it won't be visible whether the white light comes from the sun or from a strobe. The green light will propagate a little further until only the blue light is seen. This is why in order to get colourful results ocean photographers try to get as close as they can to the object of interest. Intrinsic characteristics such as the camera hardware plays an important part in the effects we see. Motion blur caused by camera movement is common in underwater systems. The long exposure times that are used to compensate for the lack of underwater lighting is responsible for that effect. We will discuss this effect in the following sections. Figure 15: Absorption coefficient versus Wavelength. Logarithmic plot showing the absorption coefficient of fresh water [35; 36]. There are some additional optical and underwater imagery effects such as the chromatic aberration [37], which is caused by a relatively wider field of view. Those effects which are supported by modern game engines and can be easily added to the simulation [38].
# Target Identification via Multi-View Multi-Task Joint Sparse Representation
## 6 Experiment and Result Analysis  
To verify the effectiveness of the proposed method (MVMT), several classical trackers with excellent performance are selected as comparison methods. The experimental performance of these algorithms is compared on the image sequence frames dataset.  
To make full use of the advantages of complementary features, three popular features are used in the experiment: gradient direction histogram (HOG), local binary pattern (LBP), and scale-invariant feature transformation (SIFT). HOG is edge distributions of objects captured based on gradient features. LBP has a powerful function of representing object texture. SIFT has scale invariance for changing image brightness, rotation angle or shooting angle. In addition, to ensure the quality of the extracted features, the illumination normalization method is used to extract the feature vectors from multiple perspectives of each particle before feature extraction.  
MVMT (multi-view multi-task) is compared with the other three special cases: SVST, MVST, and SVMT algorithms. The source code of all algorithms runs in MTLAB. The experimental hardware environment is as follows: the CPU is i5-3612 qe; the memory is 8 GB; the video memory is 2 GB; the operating system is windows 10; and the software development environment is matlab2018.  
The number of initial candidate templates for MVMT algorithm, L1T, and MTT proposed in the algorithm parameter setting is \(n\) = 10, and the number of training templates is  
Figure 11: Flow chart target selection.  
100. Gaussian distribution is used to sample the particles. After selecting the candidate template, the sparse learning method is used to construct the redundant dictionary for later sample training and updating. Scale-invariant feature transformation (SIFT), gradient direction histogram (HOG), local binary pattern (LBP), and Hu invariant matrix are used. The gradient descent iterative optimal algorithm is adopted to continuously update the candidate templates in the dictionary. Finally, the template with the smallest residual error is selected as the method of the next frame of image.  
The experimental data are divided into two groups of data for experimental verification. One group is a public dataset, including video picture frame sequence. The other group is custom dataset, including the video sequence of ships in key waters and port.
# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
## 2 Motivation Scenarios  
In this section, we introduce two specific examples, including multi-user edge server selection with a single service and multi-user edge server selection with composite services. We show that different edge server selection processes will lead to different total user waiting time.
## 2 Materials and Methods
### Data Acquisition
#### 2.1.1 Renewable Energy Potentials in Austria  
In order to develop meaningful scenarios that cover the industrial energy demand with renewables, it is imperative to know the availability of renewable energy sources. Therefore, seven studies were analyzed, which assess the technical potential of renewable energies in Austria. Depending on the study, estimates for Austria range from 219 to 359 TWh of renewable energy in total (cf. [7; 12; 13; 14; 15; 16; 17]). For comparison, the Austrian FEC in 2019 was 316 TWh in total and 87 TWh for industry [5]. For this work, the total technical potential of renewables was assumed to be 231.5 TWh. To determine this value the authors assessed the latest available potential studies [7; 16; 17]. The considered total technical potential for electrical energy is 118.9 TWh/a, consisting of 51.1 TWh/a hydropower, 32.7 TWh/a photovoltaics, 22.5 TWh/a wind power and 11.7 TWh/a biogenic sources. Furthermore, the current level of waste and geothermal energy of 0.9 TWh/a is also considered. The technical potential for thermal energy is 112.6 TWh/a, consisting of 59.9 TWh/a biogenic fuels and 46.0 TWh/a ambient heat. In addition, it is assumed that the current energy carriers of combustible waste with 3.4 TWh/a and district heating with 3.2 TWh/a will be still available for the industry in the future. Hence, the availability of these two energy carriers is kept constant in terms of absolute values in all scenarios. However, the expansion of renewable energy supply faces limitations such as regional availability and acceptance. Especially, hydropower facilities can have large environmental impacts resulting in a competition between sustainable energy generation vs. ecology and environmental compatibility (see, inter alia, [18; 19; 20; 21]).
## 3 Results and Discussion
### Validation of Results  
In order to evaluate the reliability of the predictions obtained by the Taguchi analysis, the relative error between the experimental and predicted values was calculated. The results show that the deviations remain within acceptable limits, with the maximum relative error for microhardness and compressive strength being 2.51% and 6.01%, while the average relative error was 1.69% and 3.73%, respectively. These findings indicate a high degree of accuracy and reliability of the model, which provides a basic validation of the proposed approach within a limited dataset. Additionally, in order to illustrate and visualize the predicted and experimental values, Figure 10a,b shows a comparative analysis between the experimental and predicted results for both output quantities. The outcomes show that the predicted values were very close to the experimental results, with minimal deviations. This strong correlation between experimental and predicted values confirms the validity of the applied model. Moreover, considering that the analysis and experiments were conducted with a minimal set of only nine experiments, it can be further concluded that the prediction accuracy was remarkably high despite the limited dataset.  
Figure 8: Dependence of microhardness on (**a**) compaction pressure and sintering temperature, (**b**) compaction pressure and sintering time, and (**c**) sintering temperature and sintering time. The annotation for A, B, and C is depicted in Table 4.  
Figure 9: Dependence of compressive strength on (**a**) compaction pressure and sintering temperature, (**b**) compaction pressure and sintering time, and (**c**) sintering temperature and sintering time. The annotation for A, B, and C is depicted in Table 4.
## 3 Materials and Methods
### Recovered Frequency Standard Defined from a Special Relativity Perspective
This formulation for \(\hat{T}^{(s)}\) can be interpreted as a classical Einstein synchronization within an inertial reference frame, supplemented by a term that accounts for the positions of the ground station and satellite. All the necessary parameters can be measured directly at the ground station, avoiding the need to observe the GEO satellite's signal reception within the Earth-centered inertial frame. Consequently, the satellite's onboard time reference can be defined in real-time by the continuous closed-loop tracking of \(T_{1}^{(g)}\) and \(T_{4}^{(g)}\). Accordingly, the constant correction term in Equation (1) associated with the special relativistic effect can be corrected in the uplink and downlink, respectively, provided that the receiver and transmitter frequencies are known.
# Interest Forwarding in Named Data Networking Using Reinforcement Learning
## 5 System and Scenario Description
### Request Arrival Model  
The most common approach in analysing caching system is the assumption of the independent reference model (IRM) [51] to model the arrival of requests, with a Zipf-like distribution to represent the content popularity. IRM describes the requests arriving at a cache for a fixed catalog of items as forming a sequence of independent, identically distributed random variables. In other words, the probability that a request is for an item is a constant and independent of the past requests.  
The IRM has the advantage that it is simple and has enabled the development of tractable models in the analysis of caching systems [52; 53; 54]. Although IRM does not adequately capture  
Figure 1: Interest forwarding and probing process of NDNFS-RLRNN. It uses the \(\epsilon\)-greedy approach for the probing. _rng_(0,1) is a random number generator that returns a number in the range \([0,1)\). \(p\) is the probe parameter of the RNN. It is assumed that the RNN is no longer in its initial state and that forwarding is possible so that the best interface refers to the interface corresponding to the most excited neuron of the RNN.  
phenomena like _temporal locality_ and _spatial locality_ which have been shown to exist in real traces [55; 56], other studies [57; 58] argue that their long-term effect can be well accounted for by careful choice of a Zipf-like distribution to represent content popularity. Temporal locality occurs when recently accessed items are more likely to be requested in the near future while spacial locality describes the situation where the request for an item becomes more likely because a \"similar\" item was referenced in the recent past.
## 3 Results
### Other Environmental Tests  
As previously mentioned, to use commercial products for space applications, sufficient reliability evaluation tests, such as vacuum, vibration, and thermal tests, need to be conducted, in addition to radiation tests. In this study, a vacuum test was performed during the radiation test because the interior of the radiation chamber is maintained in a high-vacuum state with a pressure in the order of \(10^{-5}\) Pa. Because the CC1120 transceiver IC is a one-chip component, and its vibration tolerance can be expected to be notably higher than that of the printed circuit board (PCB), we combined its vibration test with that of the entire CubeSat; no defect was detected. For the thermal environment test, because the IC is surface mounted on the PCB, the generated heat is mostly dissipated by conduction, which is expected to be the same under the vacuum environment as that under atmospheric pressure. Furthermore, we performed the thermal vacuum test for the entire CubeSat system.