esa-sceva/satcom-chunk-collection huggingface dataset:
# A Set of Tools and Data Management Framework for the IoT-Edge-Cloud Continuum
## 3 Data Management Framework
### Data Management Configurator  
The Data Management Configurator, visible in Figure 3, is a centralized software application that interfaces with all plugins within an IECC environment to seamlessly manage their configurations. This Configurator can be used to provide an interface for editing, validating, and deploying configuration changes to all plugins without having to manually edit many configuration files. Its implementation ensures that the configurations are consistent, synchronized, and secure across all devices and services.  
The architecture of the Data Management Configurator includes several key components that work together to provide a robust configuration management solution.  
1. Configuration API: Provides interactive configuration activities for retrieving, updating, and restoring configurations. For data processing or analytics, the Flask framework is suitable because it provides fast iterations without too many complex built-in features. For the purpose of handling real-time requests in a multi-tenant setup, Apache Pulsar is suitable.
2. Configuration Repository: Centralizes and manages plugin configurations, storing node settings to define the interconnected node environment, useful for auditing and tracking changes.
3. User Interface: Provides intuitive tools for modifying and validating complex configurations.
4. Authentication and Authorization: Ensures only authorized users can access and modify configurations, providing a secure and scalable authentication and authorization layer. The majority of integration tools are already compatible with OAuth 2.0, OpenID Connect, or JWT tokens.
5. Deployment and Monitoring: Offers automated deployment and feedback monitoring tools.  
In an IECC environment, efficiently managing data streams is crucial for enhancing operational efficiency, reliability, and security. Key considerations in this context include traffic segregation, scalability, and security. Traffic segregation involves isolating user and system data streams to minimize interference and optimize performance. Scalability is necessary to adapt to varying load demands and accommodate the expansion of IoT systems. Additionally, security remains a priority, requiring robust monitoring and control measures to safeguard data integrity and prevent unauthorized access.  
A tool for organizing and validating data flow has been developed and verified for IoT-Edge-Cloud solutions. The tool supports the analysis, visualization, and secure validation of privacy and security measures, including differential privacy in machine learning. The toolkit works efficiently on both high-performance Cloud servers and low-power IoT devices, balancing energy efficiency and security.
## 3 Results  
The measured SSA and \(f_{\mathrm{CM}}\) of SC, BC450, BC850, and AC are listed in Table 2. The order of SSA was AC (525.1 m\({}^{2}\)-g\({}^{-1}\)) \(>\) BC850 (79.6 m\({}^{2}\)-g\({}^{-1}\)) \(>\) BC450 (26.5 m\({}^{2}\)-g\({}^{-1}\)) \(>\) SC (21.4 m\({}^{2}\)-g\({}^{-1}\)). The order of \(f_{\mathrm{CM}}\) was AC (56.93%) \(>\) BC850 (37.29%) \(>\) SC (14.49%) \(>\) BC450 (12.47%). The order of \(f_{\mathrm{CM}}\) and SSA of the four CMs showed that AC and BC850 had better SSA and \(f_{\mathrm{CM}}\) than SC and BC450, which could be related to their structures, given that BC850 was mainly consisted of an aromatic lamellar structure and had many irregular holes; compared with BC850, AC had smaller grain size, more pores, and less inorganic minerals [23]. While compared with AC and BC850, SC and BC450 had lower organic matter content and contained fewer pores [24]. The \(f_{\mathrm{CM}}\) value of sediments was calculated by the \(f_{\mathrm{CM}}\) value of SC shown in Table 2. The value was only 0.17%, because the sediments had a low content of organic matter which was mainly composed of nonliving organic matter such as humic substances [22].  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **SD** & **SC** & **AC** & **BC450** & **BC850** \\ \hline \(f\) CM (\%) & 0.17 a  & 14.49 & 56.93 & 12.47 & 37.29 \\ SSA (m\({}^{2}\)-g\({}^{-1}\)) & - & 21.4 & 525.1 & 26.5 & 79.6 \\ \hline \end{tabular}
\end{table}
Table 2: The specific surface area (SSA) and black carbon content (\(f_{\mathrm{CM}}\)) of different kinds of carbonaceous materials.
## 3 End-to-End Modeling
### Laser Footprint Size  
For a given PRF, the small-footprint of the laser beam at the ground level brings more details and accuracy to assess forest vertical structures; whereas the large-footprint increases the probability of ground echo detections by increasing the laser penetration ability. The latter is more suitable for spaceborne lidar observations.  
In our airborne measurements performed at 355 nm, a small-footprint size (\(\sim\)2 m in diameter at the ground level) was used for temperate forests. With this footprint size the laser penetration is good enough to detect forest vertical parameters for deciduous forests in winter and conifer forests. The probability density functions (PDF) of GE detections (\(PDF_{GE}\)) for lidar profiles was found to be \(\sim\)1 for these cases. However, \(PDF_{GE}\) in summer for temperate deciduous forests was found to be \(\sim\)0.2. This was due to the signal attenuation by canopy leaf strata and the effects of the ground cover vegetation. We performed the lidar measurements by increasing the footprint size to be \(\sim\)4 m in diameter for the same site, leading to a \(PDF_{GE}\sim\)0.4. The footprint size was increased to be \(\sim\)10 m for tropical forests, which improved the capacity of the laser penetration in the canopy. As it was found that 10 m is not enough for ground echo detections (70% probability) through dense forests (\(FOT>3\)), the footprint diameter has to be set to a larger value, which will be a compromise between the probability to detect the ground, the horizontal sampling density of lidar footprints, and the SNR.  
To simulate large laser footprints, lidar profiles measured with a small footprint size (e.g., 2 m) were combined to get a simulated lidar profile with a larger footprint size (as schematized in Figure 7), taking into account the Gaussian energy distribution of the laser beam.  
Figure 7: Illustration of the combination of lidar profiles to simulate a lidar signal of a larger footprint. \(R\) is the radius of the simulated larger footprint, \(r\) is distance between \(i^{th}\) laser shot and the simulated footprint center (\(x_{0},y_{0}\)).  
The sample case of temperate deciduous forests in summer was chosen, since the corresponding _FOT_ is the biggest one. By entering different footprint sizes into our model, corresponding _PDFGE_ was calculated through simulated lidar profiles. A relationship between _PDFGE_ and the footprint diameter was found as shown in Figure 8, which indicates an optimal laser footprint diameter of \(\sim\)20 m for the oak forest in summer (_FOT_\(\approx\) 3). This optimal footprint of 20 m diameter will be considered in the following.
## 4 MOFs as Smart Carriers and Delivery Systems of Sensitive Volatile Compounds
### Zinc Based MOFs
This attempt has led to the formation of an advanced composite material with antioxidant activity combined with an antipathogen activity against several _Gram_ - and _Gram_+ pathogens, due to the synergistic action of Zn\({}^{2+}\) and MEO. The antioxidant activity of MEO@ZnAsc was evaluated using a DPPH radical scavenging assay and demonstrated sustained-release behavior primarily attributed to l-ascorbic acid (l-asc). While free l-asc exhibited rapid and high scavenging (\(>\)95% at 0.5 mg/mL in 1 h), ZnAsc and MEO@ZnAsc showed delayed but increasing activity over 24 h, confirming the controlled release from the MOF matrix. Notably, MEO@ZnAsc achieved an average 83% increase in DPPH inhibition after 24 h, indicating the composite's ability to preserve antioxidant efficacy over time despite initial slower release due to interactions between MEO and the MOF structure [135]. Zn\({}_{2}\)(BDC)\({}_{2}\)(DABCO) is another example of a MOF that was tested for its thymol encapsulation capability, due to its high surface area, appropriate pore size, and strong interactions with thymol, facilitated by \(\pi\)-\(\pi\) stacking and H-bonding. The combination of the thymol loaded Zn\({}_{2}\)(BDC)\({}_{2}\)(DABCO) with chitosan resulted in the enhancement of thymol control release and the materials' overall antibacterial activity against _E. coli_ and _S. aureus_, with high zones of inhibition and low minimum inhibitory concentrations. In detail, chitosan covers the MOF, controlling in this way thymol release [149].
# Determining the Optimal Positions of Infill Balise Groups for ERTMS/ETCS Level 1 Applications
## 3 Results
### Test Case  
#### 3.2.1 Setup  
To test the algorithm and to quantify the effect of different parameters, a test case using one train was designed. The calculation itself takes less than 1.5 s to complete on a standard notebook with an i7-10510U processor and 16 GB of system memory running on Windows 11. Additional time is needed to plot the desired graphical outputs.  
The line is considered to be entirely flat and to allow speeds of up to 160 km/h with a fixed RS of 20 km/h. The train is modelled after a recent regional Electric Multiple Unit (EMU) deployed in various German regions. It has an overall length of about 70 m, a total weight of around 120 t, and can apply up to 2.6 MW of traction power, leading to a fast acceleration. The acceleration values were derived from the recognized and proven runtime calculator of the software LUKS(r) [48] in version 3.8.1. The deceleration values used for the test case are derived from the indication curve of this particular vehicle modelled in the official ERA braking curves simulation tool [44]. We use the current NV of Germany's largest infrastructure manager DB InfraGO AG [49] and model the train as a passenger train in brake position \"P\" with 200 brake percentages. The resulting values of the step functions for acceleration and deceleration from this preprocessing are presented in Table 4.  
The IP is taken from the braking curves simulation tool as well, as shown in Figure 6. The rotating masses are not used, as the input data already take them into account and no gradient is present. As a minimum cruising time, 6 s is set. The processing time of the onboard unit is set to 1.5 s. Balise groups are allowed to be as close as 50 m to each other.
# A Study on the Maximum Reliability of Multi-UAV Cooperation Relay Systems
## 2 System Model and Problem Formulation
### Optimization Problem Formulation  
In UVA-assisted multi-hop communication systems without reliable direct communication links, there is no need for a reliable direct communication link. In order to obtain the maximum system reliability, the goal is to minimize the total BER by jointly optimizing the height \(\mathbf{H}\), elevation \(\theta\), transmission power \(\mathbf{P}\), and path loss \(\mathbf{PL}\) and subject these to the maximum allowable path loss constraints, transmission power allocation constraints, and UAV height and elevation constraints. At the same time, the coverage radius should be maximized and the total path loss should be minimized.  
\[\min_{\mathbf{H},\mathbf{\Theta},\mathbf{P},\mathbf{PL}}P_{e}, \tag{13}\]  
\[s.t.\,\,\,0\leq P_{\mathrm{G}}\leq P_{\mathrm{max}},\,\,0\leq P_{\mathrm{U}} \leq P_{\mathrm{max}}, \tag{13a}\]  
\[PL_{n}\leq PL_{\mathrm{max}},\,\,\forall n\in\left\{1,2,3\right\}, \tag{13b}\]  
\[r=\frac{h}{\tan(\theta)}\leq\,r_{\mathrm{max}}|_{PL_{\mathrm{max}}}, \tag{13c}\]  
\[\theta_{\mathrm{min}}\leq\theta\leq\theta_{\mathrm{max}},\,\,H_{\mathrm{min}} \leq H\leq H_{\mathrm{max}} \tag{13d}\]  
where (13a) are power constraints, (13b) are maximum allowable path loss constraint, and (13c) and (13d) are UAV height and elevation constraints.
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 2 Materials and Methods
### Fundamentals System Models
#### 2.1.2 Ultra-Massive MIMO  
This paper focuses on different channels based on potential real situations. This was considered as much as possible by using the Saleh-Valenzuela signal model, in which the signal consists of a combination of discrete radiation bundles. This is a typical model for mmWave signals, which have high reflectivity and low diffusion. Mathematically, the ray/cluster signal matrix can be represented as  
\[H=\sum_{v=1}^{Nclust}\ \sum_{u=1}^{Nray}\beta_{u,v}a_{rx}(AoA_{u,v})a_{tx}(AoD_{u,v})^{*} \tag{3}\]  
where \(u\) and \(v\) denote the number of subarrays at \(M_{T}\) and \(M_{R}\), and where the signal comprises cluster \(N_{clust}\) is the cluster angle. In each group, \(N_{rays}\) is the ray, where each ray is considered in the loop of the simulation, accounting for the multipath effects encountered by the signal. Cluster \(\beta_{u,v}\) shows complex gain. In addition, \(AoD_{u,v}\) is the angle of departure (from the transmitting array) and \(AoA_{u,v}\) is the angle of arrival (from the receiver array). The subsequent sending and receiving arrays are \(a_{tx}(AoD_{u,v})\) and \(a_{rx}(AoA_{u,v})\), respectively.  
This section focuses on signal detection in ultra-massive MIMO systems, which is crucial for transmitting large amounts of data across multiple transmitting antennas arranged in a matrix, while simultaneously dealing with interference signals. \(k\)-QAM modulation is commonly employed to introduce and simulate intricate scenarios in ultra-massive MIMO systems, specifically in relation to the proposed modulation schemes for 6G. The modulation process is controlled by the phase of constellation mapping. The procedure involves receiving binary bits as input, converting them into complex numbers, and subsequently using them as symbols. The analysis involves a multiple-input multiple-output (MIMO) wireless system that experiences flat fading. This system utilizes multiple transmitting antennas \(M_{T}\) and multiple receiving antennas \(M_{R}\). The symbol \(X_{N}(p)\) denotes the transmission conducted by the antenna \(M_{T}\) at a precise moment \(p\). The symbols that have been sent are organized into a vector of length \(M_{T}\) such as \(X_{N}(p)=[X_{1}(p),...,X_{M_{T}}(p)]^{T}\), which is referred to as  
\[X_{N}(p)=|N(p)|\cos(\arg\{N(p)\})\cos(2\pi f_{c}p)-|N(p)|\sin(\arg\{N(p)\})\sin (2\pi f_{c}p) \tag{4}\]  
\(k\) is the number of modulations that covers all QAM modulation,  
where \(|u(t)|\) and \(argu(t)\) are the amplitude and phase of the complex baseband signal, respectively.
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 5 Visual Simulations and Analysis (Constellation Scenarios)
### Moon-Based Constellation Simulation
#### 5.2.1 Autonomous Multi-Parametric Analysis (Moon's Side)  
Even for Moon, the continuity in the parametric analysis needs to be autonomously generated and observed while the small satellite constellation performs its tasks with the lunar environment in consideration. Figure 11 below shows this analysis visually.
# Rethinking Modbus-UDP for Real-Time IIoT Systems
## 4 Experimental Results
### Sensitivity to Interfering Network Load  
A set of experiments have been carried out to confirm the effectiveness of the packet prioritization mechanisms specified in Section 3.6 to shield Modbus-UDP communication from other traffic. These experiments are based on the same test programs previously discussed, configured to issue RH or WM transactions involving \(N=120\) registers, plus a varying network load.  
The interfering network load has been generated by means of a TCP connection established by the iperf3 tool with payload length 256 B and an offered bandwidth from 5 Mb/s to 40 Mb/s, the highest TCP load that the boards used for the experiments are able to sustain (as assessed in Section 4.2), in 5 Mb/s intervals. The TCP connection involves data traffic being transmitted to the Modbus-UDP master or slave node. The experimental results are plotted in Figure 10a,b. They show that the interfering traffic has virtually no effect on the Modbus-UDP round-trip time in all scenarios covered by the tests.
## 7 Experiments
### Test 3: From Far to Near Zone of PRN 4 with Reduced Visible Satellites  
This test was conducted to see the VTL performance in more practical situation. The number of visible satellites is restricted to three. The visible satellites are PRN 1, 4 and 6 as shown in Figure 21.  
Figure 20: Navigation results: ITL (left) and VTL (right).  
The user moves from the near to the far zone of PRN 4, and returns to the near zone. In this situation, tracking availability of PRN 4 is directly connected to positioning availability.  
Figure 22 shows the navigation results of ITL and VTL. ITL loses tracking lock of PRN 4 as the user moves to the far zone, and then navigation filter cannot be updated. On the other hand, VTL can track the signal and perform navigation consistently. When a user moves in a urban canyon, the number of visible satellites rapidly changes. So, the consistent tracking performance of VTL could cope with momentary signal blockage.
# Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions
## 3 Model Structure
### Group Convolution  
_AlexNet_[22] was the first architecture to introduce group convolutions, mainly aiming at distributing the model training over two GPUs. Its effectiveness is also well demonstrated by [17; 18; 19; 20; 21] in the image domain. In the author's understanding, group convolution presents three main advantages. Firstly, it can ease the training of deep neural networks by reducing their redundancy. Secondly, it is an efficient method to increase the width of neural networks allowing more feature channels which is beneficial to encode more information on each group if we consider a constrained complexity budget. Thirdly, it can be also treated as sparse convolution, as convolution kernels on each group are only responsible for part of the entire feature channel. Furthermore, it can also prevent overfitting to some extent.  
The memory size and computational complexity (FLOPs) for a single \(1\times 1\) group convolution can be measured by Equations (1) and (2) [32], respectively. It is trivial, but worth highlighting that it becomes a standard MLP operation when we set the number of groups to \(g=1\). Further to this, the number of parameters and FLOPs are reduced to \(1/g\) for a single operation when we split \(k\)-nn graph to \(g\) groups:  
\[params=1\times 1\times\frac{C_{in}}{g}\times\frac{C_{out}}{g}\times g=\frac{C_ {in}\times C_{out}}{g} \tag{1}\]  
\[FLOPs=\frac{N\times K\times C_{in}\times C_{out}}{g} \tag{2}\]  
where \(C_{in}\) and \(C_{out}\) indicate the input and output channel, respectively, \(N\) is the number of points, \(K\) is the number of neighbouring points and \(g\) is the aforementioned number of groups.
# Resilience of LTE-A/5G-NR Links Against Transient Electromagnetic Interference
###### Abstract  
This paper presents a comparative analysis of a long-term evolution advanced (LTE-A) and fifth-generation new radio (5G-NR), focusing on the effects of transient electromagnetic interference (EMI) caused by catenary-pantograph contact in a railway environment.A software-defined radio (SDR)-based prototype was developed to evaluate the performance of LTE-A and 5G-NR links under the influence of transient interference. The results show that both links experience considerable degradation due to interference at different centre frequencies. Performance degradation is proportional to the gain of interference. The measurement results show that both links experience considerable performance degradation in the presence of transient EMI.  
5G-NR; SDR; LTE-A; transient EMI; catenary-pantograph Article
## 4 Experimental Results of Applying Robust-EKF
### Applying Robust-EKF
It plays an important role in the optimal recursive process and is written as:  
\[Q_{k}=\int_{t_{k-1}}^{t_{k}}A(\tau)\sum A^{T}(\tau)d\tau \tag{70}\]  
where \(A(\tau)~{}=~{}diagonal(A_{x,\tau},~{}A_{y,\tau},~{}A_{z,\tau},~{}A_{b,G,\tau},~ {}A_{b,E,\tau},~{}A_{b,R,\tau})\), with \(A_{x,\tau}=A_{y,\tau}=A_{z,\tau}=A_{b,G,\tau}=A_{b,E,\tau}=A_{b,R,\tau}= \begin{bmatrix} 1&\tau\\ 0&1\end{bmatrix} \). The matrix \(\sum\) is the spectral density matrix given by:  
\[\sum=\text{diagonal}\left(S_{x},S_{vx},S_{y},S_{vy},S_{z},S_{vz},S_{b,G},S_{d,G},S_{b,E},S_{d,E},S_{b,R},S_{d,R}\right) \tag{71}\]  
where \(S_{x}\), \(S_{vx}\); \(S_{y}\), \(S_{vy}\) & \(S_{z}\), \(S_{vz}\) are the power spectral densities of position noise and speed noise in the x-, y- and z-coordinates, respectively; \(S_{b}\) is the power spectral density of the clock bias noise and \(S_{d}\) is the power spectral density of the frequency drift noise. To simply, we consider to the \(\sum\) which is unchangeable. The covariance matrix \(Q_{k}\) in Equation (70) is rewritten as:  
\[Q_{k}=\text{diagonal}\left(Q_{x},Q_{y},Q_{z},Q_{b,G},Q_{b,E},Q_{b,R}\right) \tag{72}\]  
With  
\[Q_{x}=\left[ \begin{array}{cc}S_{x}\Delta T+S_{vx}(\Delta T)^{3}/3&S_{vx}( \Delta T)^{2}/2\\ S_{vx}(\Delta T)^{2}/2&S_{vx}\Delta T\end{array} \right] \tag{73}\]  
\(Q_{y}\) and \(Q_{z}\) are determined similarly to \(Q_{x}\); and \(Q_{b,G}\) is expressed as:  
\[Q_{b,G}=\left[ \begin{array}{cc}S_{b,G}\Delta T+S_{d,G}(\Delta T)^{3}/3&S_{d,G}(\Delta T)^{2}/2\\ S_{d,G}(\Delta T)^{2}/2&S_{d,G}\Delta T\end{array} \right] \tag{74}\]  
\(Q_{b,E}\) and \(Q_{b,R}\) are determined similarly to \(Q_{b,G}\). After the calculation of the covariance matrix \(R_{k}\) and the covariance prediction \(P_{k}^{-}\), it is easy to determine the matrix \(L_{k}\) using the Cholesky decomposition. Multiplying Equation (64) by \(L_{k}^{-1}\) and defining: \(y_{k}=~{}L_{k}^{-1}\tilde{Z}_{k},G_{k}=~{}L_{k}^{-1}\tilde{H}_{k},\zeta_{k}=~ {}L_{k}^{-1}\varepsilon_{k}\), Equation (64) becomes the regression equation written as:  
\[y_{k}=G_{k}X_{k}+\zeta_{k} \tag{75}\]  
_Step 3_: _Updating_  
The estimated state \(\hat{X}_{K}\) in Equation (75) is optimized by the IRWLS algorithm previously detailed in Section 3.3. After testing all weight functions in Section 3.2, we chose the bi-Tukey weight function to eliminate the outliers data and \(\alpha\) is set to 1.756 (empirically chosen after many tests) for the IRWLS algorithm. The final estimated state is calculated as Equation (24): \(\hat{X}_{k}=\left(G_{k}^{T}W_{k}G_{k}\right)^{-1}G_{k}^{T}W_{k}y_{k}\). The error covariance matrix is updated as Equation (25): \(P_{k}=\left(I-KH_{k}\right)P_{k}^{-}\); where K is the filter gain determined in Equation (26) : \(K=P_{k}^{-}H_{k}^{T}\left(H_{k}P_{k}^{-}H_{k}^{T}+R_{k}\right)^{-1}\). After the state estimation, RAIM algorithm in Section 3.4 is applied to check the position accuracy.