esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimal Trajectory
### Numerical Example  
We now present a numerical example (in the following, time, lengths and forces are given in seconds, meters and newtons, respectively). We set  
\[\mathbf{p}_{T}=\begin{bmatrix}2\\ 5\\ -5\end{bmatrix},\quad\mathbf{n}_{T}=\begin{bmatrix}1\\ 4\\ -8\end{bmatrix},\quad\mathbf{a}_{1}=\begin{bmatrix}2\\ 1\\ 1\end{bmatrix},\quad\mathbf{a}_{2}=\begin{bmatrix}-3\\ -2\\ 0\end{bmatrix},\quad\mathbf{a}_{3}=\begin{bmatrix}-1\\ 3\\ -1\end{bmatrix} \tag{37}\]  
and the maximum magnitude of the velocity at the target point as \(\dot{p}_{max}=40\). For a realistic choice of motors and winch radius, we set \(\tau_{max}=8\) and \(\dot{\rho}_{max}=7\). The mass \(m\) of the EE before and after launch is \(m_{1}=1\) and \(m_{2}=0.6\), respectively.  
We seek the optimal trajectory under the following workspace constraints:  
\[\begin{split} X_{min}&=-4,\qquad X_{max}=4\\ Y_{min}&=-5,\qquad Y_{max}=5\\ Z_{min}&=-5,\qquad Z_{max}=0\end{split} \tag{38}\]  
We also assign the \(x\) and \(y\) coordinates of trajectory center as \(x_{C}=-1\), \(y_{C}=1\).  
Several algorithms may be used to solve the optimization problem: we chose to use a genetic algorithm with the MATLAB function gamultiobj, setting an initial population of 1500 individuals and a maximum of 5000 generations. The algorithm converges within the specified number of generations with a function tolerance of \(10^{-5}\); all solutions also respect the constraints set in Equations (34)-(36). The result of the optimization is a Pareto front that displays the relationship between the first and the second element in the vector-valued function \(F\). In Figure 3, every point represents a potential solution on the Pareto front for the two objectives to be minimized \(E\) and \(\Delta t_{LT}\). As expected, we can see that we cannot minimize both objectives at the same time.  
We also show an example solution from the Pareto front, defined by  
\[\mathbf{p}_{L}=\begin{bmatrix}1.954\\ 0.815\\ -1.693\end{bmatrix},\quad\mathbf{p}_{C}=\begin{bmatrix}-1\\ 1\\ -2.43\end{bmatrix},\quad\mathbf{b}=\begin{bmatrix}-2.94&0.599\\ -0.599&-2.02\\ -1.01&-0.547\end{bmatrix},\quad\omega=1.964 \tag{39}\]Figure 4a,b show the optimal trajectory and the corresponding cable tensions; we notice that the cable tensions are discontinuous at the launch instant, as the EE mass \(m\) suddenly changes.
## 4 Model Development
### Eye-to-Hand Transformation Model  
Assuming the camera remains fixed relative to the robot's end-effector, we introduce a constant transformation matrix \(T_{E}^{C}\) that maps points from the end-effector frame {E} to the camera frame {C}. The process of finding the matrix \(T_{E}^{C}\) is called Eye-to-Hand Calibration.  
In general, the goal is to solve for \(T_{E}^{C}\), such that:  
\[A_{i}^{j}\cdot T_{E}^{C}=T_{E}^{C}\cdot B_{i}^{j} \tag{18}\]  
Here, \(A_{i}^{j}\) is transformation of the end-effector between robot poses \(i\) to pose \(j\), and \(B_{i}^{j}\) represents the transformation of a fixed object (e.g., a marker) as observed in the camera frame during the same motion.  
These transformations are computed as:  
\[A_{i}^{j}=(T_{0}^{6,i})^{-1}\cdot T_{0}^{6,j} \tag{19}\]  
\[B_{i}^{j}=(T_{M}^{C,i})^{-1}\cdot T_{M}^{C,j} \tag{20}\]  
where \(T_{0}^{6,i}\) and \(T_{0}^{6,j}\) denote the poses of the end-effector at robot configurations \(i\) and \(j\), measured in the base frame, as defined in Equation (17). \(T_{M}^{C,i}\) and \(T_{M}^{C,j}\) denote the transformations of a marker M observed in the camera frame at the same respective robot poses. Further details are provided in Appendix C.  
Various methods have been proposed to accurately estimate \(T_{E}^{C}\) using multiple \(\left(A_{i}^{j},\ B_{i}^{j}\right)\) pairs. While calibration techniques are not the primary focus of this paper, several recent approaches are summarized for reference:  
[PERSON] et al. [30] presented a technique for eye-on-hand configurations, deriving a closed-form solution for Equation (18) using two robot movements. [PERSON] et al. [31] introduced a unified iterative method based on reprojection error minimization, applicable to both eye-on-base and eye-in-hand setups. This approach does not require explicit camera pose estimation and demonstrates improved accuracy compared to traditional methods. [PERSON] and [PERSON] [32] proposed an optimal calibration method for eye-in-hand systems, utilizing a novel metric on SE(3) and an error model for nonlinear optimization. This method adapts to system precision characteristics and performs well in Equation (18) formulation.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 4 Proposed Image Compression and Encryption Algorithm
### Compression with Confusion-Diffusion through Kd-Tree, Wavelet Tree and Huffman Coding
#### 4.2.1 Confusion-Diffusion and Compression with K\({}^{2}\)-Tree  
Usually, SPIHT encoding produces a sparse bit stream. The sparseness can be increased by the linear diffusion of small wavelet coefficients with small negative value. Several cyclic shifts were performed on the SPIHT bit stream and reshaped into small blocks (\(2\times 2\)), packed to form large blocks (\(8\times 8\)) and combined into a large matrix (\(512\times 512\)). Then, this matrix was subjected to K\({}^{2}\)-tree for further compression with confusion and diffusion operations. At different levels of K\({}^{2}\)-tree, we can achieve permutations of sub-level matrices with the help of a pseudo-random permutation sequence (which is obtained via Matlab function randperm with rng(seed) with a specified seed value). In a modified K\({}^{2}\)-tree, we achieved compression of homogeneous regions of zeros and ones at multiple levels. The matrix vocabulary was used to achieve diffusion at lower levels of the conceptual tree.
## 3 Methodologies  
The overall workflow of the framework is shown in Figure 1. The workflow is composed of three modules. In the Dataset Preparation module, raw CCTV footage is preprocessed in preparation for the integration of spatial information and subsequent extraction of crowd congestion data. The Trajectory Generation module then sequentially employs a pedestrian detector, tracker, and spatial mapper to generate a spatiotemporal mapping of each occupant. Lastly, the CMGraph for Efficient Congestion Monitoring module enables the representation of spatiotemporal characteristics as graph data, facilitating real-time monitoring and prediction of future crowd congestions.  
Figure 1: Workflow of the congestion monitoring framework.
## 2 Basic Theoretical Analysis
### Frequency Estimation
```
1: The signal sequence \(s[n]\), sequence length \(N\), and sampling rate \(f_{s}\);
2: Population size \(M\) of PSO, iteration times \(T\), inertial weight \(\omega\), \(c_{1}\), and \(c_{2}\);
3: The signal frequency estimation value \(\hat{f}\);
4: calculate the spectrum \(S[k]=DFT\{s[n]\}\) (\(k=0,1,2,\cdots,N-1\));
5:\(\hat{p}_{k}=argmax(|S[k]|^{2})\), \((k=0,1,2,\cdots,\frac{N}{2}-1)\);
6: calculate the search range \([f_{min},f_{max}]\), where \(f_{min}=(\hat{p}_{k}-0.5)f_{s}/N\), and \(f_{max}=(\hat{p}_{k}+0.5)f_{s}/N\);
7: randomly initialize M particles' position uniformly distributed in range \([f_{min},f_{max}]\);
8: randomly initialize M particles' velocity;
9: initialize gbest=zeros(1,1) and pbest=zeros(M,1)
10:for\(t<T\)do
11:for each particle \(i\)do
12: calculate and store particle \(i\)'s fitness value by Equation (15) ;
13:endfor
14: update gbest and pbest;
15: calculate inertial weight \(\omega_{i}\); by Equation (17);
16:for each particle \(i\)do
17: update velocity \(V_{i}\) using Equation (16a);
18: update position \(X_{i}\) using Equation (16b);
19: check position vector \(X_{i}\) in the search range \([f_{min},f_{max}]\);
20:endfor
21:\(t=t+1\);
22:endfor
```  
**Algorithm 1** Pseudo-code of the PSO-based frequency estimation method.
## 4 Evaluation of Quality and Efficiency
### Optimisation of the FE Meshing
determined by copying the result variables from the Gauss points to the element nodes (_blue curve_). The extrapolation has no significant influence. Alternatively, for comparison the strain ranges can be calculated from the stress from FEA using Equation (6), shown as the red curve. Due to the extrapolation, the elastic-plastic strain range \(\Delta\epsilon\) converges faster than it does for copying, but the strain ranges are clearly overestimated with a coarse mesh. * By using shell elements on the surface and copying the FEA results (_green line_) the same behaviour occurs for elastic and elastic-plastic material behaviour: the strain ranges converge towards a constant value even with a coarse mesh. * When using shell elements on the surface and the additional application of extrapolation (_grey line_), the strain range converges more slowly, especially with elastic-plastic material behaviour. With elastic-plastic material behaviour, the strains are calculated from the stress as before. The tendencies shown are also reflected in a consistent manner in the comparison of the service lives at constant amplitude loading in Figure 18B, which are calculated from the results of the simulation with elastic-plastic material behaviour. In the standard procedure in Ansys Workbench involving copying the results at the Gauss points to the element nodes, a mesh with \(n_{\text{E}}>70\) is needed in this case to determine the service life with an error of less than 1%. By using extrapolation, a mesh with \(n_{\text{E}}=30\) is sufficient to fulfil this condition. A great advantage can be gained for the meshing results from the use of shell elements on the surface. Together with extrapolation, a mesh with \(n_{\text{E}}=15\) is sufficient. On the other hand, it is recommended to copy the results at the Gauss points, as this means that a mesh with \(n_{\text{E}}=5\) is already sufficient, even if the service life thereby approaches the converged solution from the unsafe side. Figure 18: Influence of the mesh in elastic and elastic-plastic material behaviour on the: (**A**) strain range; (**B**): service life. The use of shell elements on the surface along with copying of the results at the integration nodes to the element nodes offers no great advantage in calculations with elastic material behaviour. With elastic-plastic material behaviour, however, a converged result with a significantly coarser mesh can be determined. The resulting time saving increases the efficiency in determining the load-notch-strain curve immensely.
## 3 Results and Discussion  
The calculated equilibrium lattice constants, scalar relativistic band gap \(E_{g}^{SP}\), and fully relativistic band gap \(E_{g}^{SOC}\) (eV) of the CsPbBr\({}_{x}\)I\({}_{3-x}\) perovskites are listed in Table 1. For the CsPbBr\({}_{x}\)I\({}_{3-x}\) perovskites were increased due to the larger Wigner-Seitz radius of iodine. The fully relativistic band gap was significantly lower than the scalar relativistic band gap of the CsPbBr\({}_{x}\)I\({}_{3-x}\) perovskites due to the strong SOC effect. When SOC is considered, the band gaps of the CsPbBr\({}_{x}\)I\({}_{3-x}\) perovskites lead to a substantial underestimation of calculated band gaps. The calculated band gaps of the CsPbBr\({}_{x}\)I\({}_{3-x}\) perovskites induced by SOC were 0.67, 0.49, 0.39, and 0.33 eV for the CsPbBr\({}_{x}\)I\({}_{3-x}\)CsPbBr\({}_{2}\)I\({}_{3-x}\)CsPbBr\({}_{2}\), and CsPbI\({}_{3}\), respectively. This underestimation of the band gap can be corrected by applying, for example, the \({\rm{{}^{G}}}{\rm{{}^{W}}}{\rm{{}^{0}}}\) or PBE0 approximations, but the tendency of the band gap to decrease under strain will remain unchanged. The reduction in the band gap caused by the SOC effect commonly appears in not only bulk TIs but also two-dimensional transition metal dichalcogenides (TMDCs) [33]. A useful and practical empirical formula of TMDCs in terms of the PBE band gap and \({\rm{{}^{G}}}{\rm{{}^{W}}}{\rm{{}^{0}}}\) band gap was introduced by [PERSON] et al. [34] who introduced the form \(E_{g}^{G{{{{{W}}}}{\rm{{}^{0}}}}}=1.358\times E_{g}^{PBE}+0.904\), where the \({\rm{{}^{G}}}{\rm{{}^{W}}}{\rm{{}^{0}}}\) is an approximation with Hartree-Fock (HF) Green function G and the screened interaction W without further iterations. Here we adopt the \({\rm{{}^{G}}}{\rm{{}^{W}}}{\rm{{}^{0}}}\) approximation because it is the most physically grounded method to accurately predict the energy band gap, as presented in Table 1 and Figure 2. To compare our calculated band gaps, we also employed the PBE0 hybrid functional which combines the PBE exchange energy and HF exchange energy in a set 3:1 ratio, along with the full PBE correlation energy, as also summarized in brackets in Table 1. The CsPbBr\({}_{x}\)I\({}_{3-x}\) perovskite obtained from the \({\rm{{}^{G}}}{\rm{{}^{W}}}{\rm{{}^{0}}}\) empirical approach has the largest band gap. The PBE0 band gaps with SOC are between the PBE and \({\rm{{}^{G}}}{\rm{{}^{W}}}{\rm{{}^{0}}}\) band gaps. For the experimental results of CsPbBr\({}_{x}\)I\({}_{3-x}\) perovskites, the \({\rm{{}^{G}}}{\rm{{}^{W}}}{\rm{{}^{0}}}\) band gaps  
Figure 1: Schematic of the CsPb\(X_{3}\) (\(X=\) Br or l) crystal structure (**a**), one Pb\(X_{2}\)- and Cs\(X\)- (**b**) and two Pb\(X_{2}\)- (**c**) terminated slabs. The atoms are represented by spheres: Cs (green, large), Pb (dark blue, medium), and \(X\) (purple, small).  
with SOC are underestimated by approximately 23% and 22%, respectively. Analysis of the fully relativistic band gaps suggests that more external pressure may be required to produce a normal TI phase transition under the empirical correction with a constant of 0.904 eV.
## 3 Results
### Other Genetic Alterations and Psychosis
#### 3.3.1 COMT Gene  
The COMT gene is located on chromosome 22 in the q11.2 region and encodes the enzyme that degrades dopamine in the PFC [26]. The COMT rs4680 genetic polymorphism involves replacing guanine with adenine, which leads to valine (Val) being replaced by methionine (Met) at position 158 associated with the membrane, or at position 108 of the soluble form of gene [31; 33; 39; 40].  
The interaction established between the COMT Val 158 Met genotypes with the use of cannabis leads to an increase in the production of psychotic symptoms. This is because the Val enzymes increase the degradation activity, that is, they are associated with sensitivity to cannabis, while Met enzymes decrease activity, that is, they are associated with protection as they are less efficient [28; 34; 39]. Thus, people who are homozygous for the Val allele have lower levels of dopamine in the PFC, increasing the risk of psychosis tenfold and presenting poorer cognitive performance [31]. In contrast, people homozygous for the Met allele have higher dopamine levels and people heterozygous for the Val/Met allele have intermediate levels of dopamine in the PFC [30; 33; 40; 45].  
Therefore, the COMT gene polymorphism interferes not only with cognitive performance, but also with structural brain abnormalities when cannabis is consumed [29]. In addition, the association with a history of childhood abuse increases the chances of positive psychotic symptoms appearing along with negative, especially in those with Val alleles [31; 45].
# Special Issue on \"Recent Advances in Indoor Localization Systems and Technologies\"
###### Abstract
In [33], a NLOS tracer method is proposed based on the improved Modified Probabilistic Data Association and Interacting Multiple Model algorithms. In [34], a Gaussian model is proposed to identify NLOS signals, while [35] applies machine learning methods to identify not only LOS and NLOS, but also multipath situations. **Unorthodox Positioning and Sensing methods:** Some of the papers proposed new or unorthodox approaches both for sensing and positioning. In [36], a novel inexpensive sensing method is proposed using a Circular Photodiode Array (for sensor) along with modulated light sources (for anchor nodes), to provide an ADoA-based positioning system. Paper [37] proposes a single-camera trilateration method, which is able to estimate the 3D pose of a camera from a single image, using landmarks at known positions. The systems of [38; 39] offer help for the visually impaired, using portable cameras. In [38], a marker-based localization and navigation system is proposed, using convolutional neural networks to identify markers on the camera stream. In [39], a system is proposed, which helps to identify objects on the camera image, using a novel multi-label convolutional support vector machine. This research received no external funding. Not applicable. The authors declare no conflict of interest.
# Numerical Study on Aerodynamic Noise Reduction of Pantograph
## 4 Results and Discussion
### Influence of Wavy Rods on the Aerodynamic Noise  
According to the previous research [15], the boundary layer separation of the wavy cylinder is markedly delayed and the noise intensity can be suppressed. In order to study the influence of wavy rods on the aerodynamic noise of the pantograph, the surface pressure level is firstly compared in Figure 11. Through modifying the cross bar surface, the value of the pressure time derivative is obviously decreased, as marked by an ellipse. Applying the wavy rods can also reduce the acoustic source in the middle area, but the reduction magnitude is relatively small, such as in the lower arm frame.  
Next, the values of OASPL at the standard observer are quantitatively compared in Figure 12. The components of the bottom area remain the same, so the corresponding noise intensity is not displayed. As shown in Figure 12, the noise generated by the middle area of the wavy rod model is slightly weaker than that of the original model, and the difference is about 0.6 dB. Then, we focus on the noise radiated from the panhead area of the two models. The difference between the two models is 2.4 dB, and the results suggest that applying the wavy rods can diminish the panhead noise intensity. Furthermore, the change of A-weighted SPL at different frequency ranges is studied. The data in Figure 13 reveals that the panhead noise suppression is mainly concentrated around the peak frequency, from 560 Hz to 3600 Hz. Nevertheless, the noise intensity in the higher frequency range (\(f>3600\) Hz) is strengthened.  
Figure 10: A-weighted sound pressure level spectra of noise radiated from different parts of the pantograph.  
Figure 11: Contour of the surface pressure level at \(f_{c}=2\) kHz: (**a**) original model; (**b**) wavy rod model.  
Figure 12: Comparison of overall sound pressure level between the original model and wavy rod model.
## 6 Conclusions and Future Work
We defined oracle and baseline cases for a fair comparison with our solution and elaborated the method to calculate the RSSI using the channel matrices given in the DeepMIMO simulation tool. Our evaluation results showed that our localization is (i) codeword-independent and (ii) robust against changes in the number of codewords, testing area size, and additive white Gaussian noise. The experiments also showed that complex-value codeword discriminators performed better than real-value codeword discriminators. Although we used a far larger number of the codewords compared to the current RIS prototypes [66; 67; 68] and achieved performances close to those of the oracle cases, there is still a chance that our model fails to deal with more codewords when the resolution is extremely low. If it happens, then we may have to require the codewords for location inference. Nevertheless, considering the fact that most currently available prototypes only support \(C=2/4\)[66; 67], we are confident that our solution has strong practicality in modern wireless communication systems. For our evaluation, we fully relied on simulations using the DeepMIMO dataset, as the RIS hardware is currently still under development, even though some prototypes exist. Hence, in our future works, if RIS hardware becomes available, we will design experiments, collect real-world data, and perform the evaluation. [PERSON] defined the problem, proposed the solution, and designed experiments for evaluation; [PERSON] supervised [PERSON] in this research project. Both authors contributed to the paper's writing. All authors have read and agreed to the published version of the manuscript. This work was partially done in the context of the DAIS project, which received funding from Key Digital Technologies Joint Undertaking (KDT JU) under grant agreement No. 101007273. Not applicable. Data Availability StatementData were generated using the open-source DeepMIMO [38]. The authors declare no conflict of interest.
## 2 Materials and Methods
### Instrumental Equipment
#### 2.2.2 Measurement with Modular Raman Spectrometer  
The measurement of reference compounds and tissue samples with a mobile i-Raman Plus spectrometer (\(\lambda_{\text{ex}}\) 785 nm) was provided in macroscopic mode using fiber optic probe.  
The spectra were recorded in the region of 175-3110 cm\({}^{-1}\) with spectral resolution of 4.5 cm\({}^{-1}\). Each spectrum was generated through the accumulation of 3-10 scans with a laser exposure time of 60-90 s per scan. The laser output power was \(\sim\)90 mW.
# State of the Art in LP-WAN Solutions for Industrial IoT Services
## 3 LP-WAN Solutions for IIoT Services
### Sigfox  
This is the platform in the most advanced deployment state in Europe. By means of agreements with local cellular network operators, Sigfox [20] claims to have covered most of the territory of France, Russia, and Spain, among others. Technically speaking, this solution is quite different from the LoRaWAN approach. Instead of using bidirectional spread spectrum channels, Sigfox employs proprietary ultra-narrow band modulation (Differential Binary Phase Shift Keying, DBPSK) with a heavily limited uplink connection. Using this modulation, a maximum data rate of 100 bps can be achieved by transmitting messages with a maximum payload length of 12 bytes. Meanwhile, using this low bitrate permits large ranges of 10 km and beyond with very low transmission power, which allows saving energy at edge-nodes. Sigfox's technical sheets claim a typical stand-by time of 20 years with a 2.5 Ah battery.  
Sigfox's star topology is similar to a cellular architecture, with a wide deployment of base stations aimed at covering entire countries by employing ISM bands. This base station structure permits edge-nodes to upload the gathered data directly to Sigfox servers, which makes it accessible to subscribers through a web-based API (Application Programming Interface). The use of ISM bands together with Sigfox's medium access strategy, namely without collision-avoidance techniques, leads to a stringent bandwidth-occupancy limitation suffered by edge-nodes. For example, a duty cycle of 1% is established in the Europe regulations; hence, a maximum of 140 messages per edge-node per day are allowed. In the case of the USA regulations, Sigfox's limited data rate of 100 bps showsthat transmitting single messages usually takes 2-3 s, which is outside the FCC's maximum message transmission time in ISM bands of 0.4 s. Although originally designed as a unidirectional system, Sigfox has lately included a limited downlink window (four messages of eight bytes per edge-node per day) similar to the strategy adopted by LoRaWAN's Class A devices (please see previous sub-section).  
Regarding security issues, Sigfox implements frequency-hopping and anti-replay mechanisms in their servers, but no encryption techniques are used between end-nodes and base stations. Additionally, the payload format is undefined. Therefore, Sigfox's security strategy relies on the fact that an intercepted message cannot be interpreted unless the attacker is able to understand the particular subscriber's system.