esa-sceva/satcom-chunk-collection huggingface dataset:
# On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
## 4 NB-IoT Downlink Channel Estimation
### SVD with LMMSE Estimation  
After the application of SVD, the channel autocorrelation matrix \(\boldsymbol{\Phi}_{\boldsymbol{\Psi}\boldsymbol{\Psi}}\) of Equation (16) becomes  
\[\boldsymbol{\Phi}_{\boldsymbol{\Psi}\boldsymbol{\Psi}}=\mathbf{U}\boldsymbol{ \sum}\mathbf{U}^{H} \tag{17}\]  
where \(N\) singular value of the diagonal matrix \(\Sigma\) is expressed by \(\alpha(0)\geq\alpha(1)\geq\ldots\geq\alpha(N-1)\geq 0\) at its diagonal, and \(U\) is denoting the unitary matrix. Thereafter, by exploiting the unitary matrix property and placing the value of Equation (17) into Equation (16), we get  
\[\boldsymbol{\Psi}_{LMMSE} =\mathbf{U}\boldsymbol{\sum}\mathbf{U}^{H}{(\mathbf{U}\boldsymbol {\sum}\mathbf{U}^{H}+\sigma^{2}\mathbf{U}\mathbf{U}^{H})}^{-1}\boldsymbol{\Psi }_{LS}\] \[=\mathbf{U}\boldsymbol{\sum}{(\mathbf{U}\boldsymbol{\sum} \mathbf{U}^{H}+\sigma^{2}\mathbf{U}\mathbf{U}^{H})}^{-1}\mathbf{U}^{H} \boldsymbol{\Psi}_{LS}\] \[\boldsymbol{\Psi}_{LMMSE}=\mathbf{U}(\boldsymbol{\lambda},\ldots, \boldsymbol{0})\mathbf{U}^{H}\boldsymbol{\Psi}_{LS} \tag{18}\]The optimal rank \(q\) of the SVD matrix can be obtained as in [30]:  
\[\begin{split}\mathbf{\
abla}_{q}=\mathbf{U}\mathbf{\Delta}_{q}\mathbf{U}^{H} \mathbf{\
abla}_{LS}\\ \mathbf{\
abla}_{q}=\mathbf{U}\left[\begin{array}{cc}\mathbf{\Delta}_{q} &0\\ 0&0\end{array}\right]\mathbf{U}^{H}\mathbf{\
abla}_{LS}\end{split} \tag{19}\]  
where \(\mathbf{\Delta}_{q}\) is the \(q\times q\) upper left corner of \(\mathbf{\Delta}\). The values of the diagonal matrix \(\mathbf{\Delta}\) are defined as [30; 31]  
\[\Delta=diag(\frac{\alpha(0)}{\alpha(0)+\sigma^{2}},\frac{\alpha(1)}{\alpha(1)+ \sigma^{2}},\dots,\frac{\alpha(N-1)}{\alpha(N-1)+\sigma^{2}})\]  
where \(\alpha(N)\) is the channel energy (variance) of the received information after \(\mathbf{\
abla}_{LS}\) is transformed by the \(\mathbf{U}^{H}\). Since \(\mathbf{U}\) is a unitary matrix, we can consider this transformation as the rotation of the vector \(\mathbf{\
abla}_{LS}\), and hence all the components are uncorrelated [39]. The desired rank in the low-rank estimator is determined by the space dimension of the corresponding time and frequency related signal. According to [40], this dimension is \(2 BT+1\), where \(T\) is the interval of time and \(B\) is the one-sided bandwidth. Similarly, the singular values magnitude of \(\mathbf{\Phi}_{\mathbf{\
abla}\mathbf{\
abla}}\) will be smaller after about \(L+1\) values, where \(L\) indicates the length of cyclic prefix (\(2B=1/T_{s}\), \(T=LT_{s}\), and \(2 BT+1=L+1\)).
## 2 Related Works
Three components extracted by decomposition and integration are used for causal structure learning, respectively. Causal data mining has also been a research hotspot in recent years. In the research of data association, the focus of research has gradually shifted from correlation analysis to causality verification. In the real world, multivariate time series (MTS) data are common in various fields. Existing methods assume that the value to be predicted of a single variable is related to all other variables in MTS data. In [21], they proposed a solution by using causality information as a priori knowledge. Moreover, they proposed the framework that considers multivariate time series as a graph structure with causality. [PERSON] et al. [22] mentioned that concept of Granger causality is increasingly applied to the characterization of directional interaction in different applications. In order to explain all the available information in multivariable time series, a multivariable framework for estimating Granger causality is very important. In [23], Granger causality analysis based on neural network was used for root cause diagnosis, which effectively solved the problem that Granger causality analysis based on linear model can not process nonlinear data. In [24], a human behavior event analysis and calculation model integrating perceived causality was proposed. [PERSON] designed and established a causal rule representation method based on default logic. Research in [25] aimed to determine the causes of air pollutants in surrounding cities that affect air quality. They proposed a compressed sensing causality analysis method by combining Granger causality analysis and maximum correlation entropy criterion to effectively identify the causality of air pollutants between Beijing and surrounding cities. Studies on time series has lasted for a long time, and many methods have been used in urban computing, especially in traffic prediction. However, existing research has not deeply explored the time series, especially combined with causality test. In this paper, three components of cellular traffic data were extracted, respectively, causal structure learning for three components was applied, and the application scenario of each component was given. Finally, a prediction system based on decomposition integration and causal structure learning was proposed.
# Empirical Evaluation of TLS-Enhanced MQTT on IoT Devices for V2X Use Cases
## 5 Performance Evaluation
### Symmetric Encryption Evaluation
We observe that the combination of message count and individual message size significantly influences this metric, underscoring the importance of optimized message packaging strategies for efficient and reliable communication. Overall, Chacha20_Poly1305 produces better results than AES_256_GCM, making it a better choice for time-critical V2X applications. In that regard, among the 100 KB message packagings, a significant goodput surge is observed over WiFi from 1000 \(\times\) 100 B to 10 \(\times\) 10,000 B, where the latter's measurements are more than 40 times for client-to-broker communication and more than 50 times higher for broker-to-client communication for both encryption algorithms. These drops to 5 and 6 times higher, respectively, compared with 100 \(\times\) 1000 B. Additionally, for 1 MB and 100 MB, we infer that they have comparable results to 100 \(\times\) 1000 B and 10 \(\times\) 10,000 B, respectively. This is a strong indication that goodput is greatly influenced by the size and not the number of messages selected. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Role** & **Non-TLS** & **TLS** \\ \hline Publisher & 26.1 & 27.5 \\ Subscriber & 26.1 & 27.6 \\ Broker & 7.3 & 8.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Maximum RSS for data transmission (MB). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**ChaCha20**} & \multicolumn{3}{c}{**AES-GCM**} \\ \cline{3-6}  & **Role** & **100 KB** & **1 MB** & **100 MB** & **100 KB** & **1 MB** & **100 MB** \\  & **(1000 \(\times\) 100 B)** & **(1000 \(\times\) 1000 B)** & **(10,000 \(\times\) 10,000 B)** & **(1000 \(\times\) 100 B)** & **(1000 \(\times\) 1000 B)** & **(10,000 \(\times\) 10,000 B)** \\ \hline \multirow{3}{*}{**TLS**} & Publisher & 1.398 & 2.063 & 84.827 & 1.407 & 2.118 & 85.639 \\  & Subscriber & 1.395 & 2.061 & 84.817 & 1.405 & 2.116 & 85.632 \\  & Broker & 1.193 & 1.721 & 67.436 & 1.200 & 1.764 & 68.081 \\ \hline \multirow{3}{*}{**TLS**} & Publisher & 11.497 & 12.873 & 233.721 & 11.606 & 13.316 & 240.027 \\  & Subscriber & 11.483 & 12.859 & 233.690 & 11.592 & 13.302 & 239.996 \\  & Broker & 9.236 & 10.329 & 185.681 & 9.322 & 10.680 & 190.694 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Energy consumption (J) during TLS Phase 4 (data encryption): Chacha20 vs. AES-GCM for low- (100 KB), medium- (1 MB), and high-volume (100 MB) payload transmission over both Ethernet and WiFi. The findings presented in this section are of great importance in order to develop and maintain V2X solutions with package configurations, which primarily serve the applications needs and at the same time address the resource constrains encountered in the IoT environment.
# Radial Structure of OAM-Carrying Fundamental X-Waves
## 2 Results
### Fundamental X Waves with OAM
Here, only the positive wing is shown, for simplicity), leaving a region of zero intensity around \(R=0\), corresponding to the transverse doughnut structure seen in Figure 2. Moreover, the onset of different rings in the transverse structure of the fundamental X-wave is mimicked by the appearance of several secondary structures in the radial direction, which corresponds to the rings in the transverse structure, as seen in Figure 2. For these plots, the \(\rho-\) and \(\zeta-\)axes are dimensionless, i.e., \(\rho\) and \(\zeta\) are expressed in units of \(\alpha\). Figure 2: Normalized intensity distribution of fundamental X-waves carrying \(m=1\) units of orbital angular momentum (OAM) in the plane \(\zeta=0\), for different values of the spectral index \(n\), namely \(n=0\) (**a**), \(n=20\) (**b**), and \(n=50\) (**c**). As can be seen, as \(n\) grows, several rings start to appear in the paraxial region of the pulse, i.e., near its center. For these plots, the \(x-\) and \(y-\)axes are dimensionless, i.e., they are measured in units of \(\alpha\). As can be seen, the number of rings grows linearly by one unit each time \(n\) increases by 10 units, starting from \(n=15\). For values \(n<15\), there are no extra rings appearing in the transverse intensity structure of the X-wave (the OAM ring does not count towards the total number of extra rings, and therefore the zero number of rings corresponding to \(n<15\) means, in reality, that only the OAM ring is present). The functional relation between the number of extra rings and the spectral index \(n\) can be then written in a compact way by introducing the function:  
\[N(p)=\sum_{k=0}^{p}\Theta(n-(15+10k)), \tag{8}\]  
where \(\Theta(x)\) is the Heaviside step function and \(p\in\mathbb{N}\) is an integer used to truncate the sum to the desired maximum value of \(n\). For example, a value of \(p=10\) will allow \(N(p)\) to only consider the appearance of ring structures up to \(n=15+10\times 10=115\). Defined in this way, the function \(N(p)\) then returns the number of rings possesses by the X-wave for a specific value (or interval of values) of \(n\), essentially represented by \(p\).
# BICM-ID with Physical Layer Network Coding in TWR Free Space Optical Communication Links +
## 2 System Description
### System Model
#### 2.3.1 Transmitter  
The information bit \([\mathbf{b}_{k}]_{n=0}^{N-1}\in GF(2)\) of length \(n\) is encoded by non-systematic convolutional (NSC) encoder to produce codeword bits \(\mathbf{c}_{k}\) of length \(n/R_{c}\), where \(R_{c}\) is the code rate. The sequence of code bit \(\mathbf{c}_{k}\) is permuted by a bit-level interleaver, \(\Pi\), to obtain \(\mathbf{I}_{k}\) of length \(n/R_{c}\). The \(M\)-ary mapping scheme groups \(\log_{2}M\) bits into each constellation point. Quadrature phase shift keying (QPSK) is adopted in this work, in which case we have four symbols representing the \(\mathbf{s}_{k}\) sequence. To ensure that the optical modulated signals are all positive, a direct current (DC) bias level \(b_{0}\) is added to the QPSK signal prior to intensity modulation (IM) of the light source, which can be expressed as:  
\[\mathbf{x}_{k}=P_{t}[1+\psi\,\mathbf{s}_{k}], \tag{14}\]  
where \(P_{t}\) is the average transmit optical power and \(\psi\) is the modulation index satisfying the condition \(-1\leq\psi\,\mathbf{s}_{k}\leq+1\) in order to avoid clipping due to the over-modulation.  
Figure 2: Block diagram for bit interleaved coded modulation with iterative decoding (BICM-ID) TWR-free space optical (FSO) with PNC. NSC, non-systematic convolutional; APP, a posteriori probability.
## 4 Statistical Quality Assessment of GNSS Signal
### Frequency Domain Features
Additionally, features such as the Root Mean Square (RMS), 75 th percentile, inter-percentile range, entropy, and skewness exhibit a reduction in values compared to other types of interference. The kurtosis value for pulse-type interference varies significantly, whereas it remains constant for other types of interference. The next step was to decide on the best choice among two signal domain attributes, primarily depending on how well each set of features could distinguish between the different signal types. The statistical analysis has been made for both time and frequency domain attributes of various GNSS signal categories. We conducted a \(p\)-test to evaluate the normality of the signals. The null hypothesis of the \(p\)-test of the residuals follows a normal distribution. Usually, the hypothesis is rejected if a \(p\)-value is less than 0.05. To visualize the results, a box-and-whisker plot of the \(p\)-values is plotted in Figure 5 for 25.6 s of data for each signal. \[NSK=\frac{\sum_{k=f_{1}}^{f_{2}}\left(f_{k}-S_{c}\right)^{4}P_{k}}{\sum_{k=f_{1 }}^{f_{2}}p_{k}s^{4}},\]1 (**a**) Frequency domain features at different jamming power levels, (**b**) Time domain features under various jamming power levels. The black line of the box-and-whisker plot represents the median \(p\)-value that indicates the degree of normality. Based on the level of significance, the \(p\)-value is set to 0.05, denoted by a red dashed line. This confirms a high likelihood chance that the distribution is not normal below this value. The \(p\)-value is greater than 0.05, revealing that the residuals tend to follow a normal distribution. It is observed, based on the analysis, that the time domain attributes generally behave as normal distributions, and it is very difficult to find significant deviations. Therefore, the time domain attribute may not be an ideal choice for analyzing GNSS signal disruption using ML techniques. On the other hand, the \(p\)-values in the frequency domain for most GNSS signal categories were below 0.05 except for pulse, indicating deviations from normality and providing more promising information for analysis.
## 2 MAC Design Issues in CRBANs
### Optimized Spectrum Decision  
SUs dynamically choose the best available channels and transmission parameters. CR-aware MAC protocols should be able to choose the best spectrum using minimum time and energy. Determining thespectrum is among the main design issues in CRBANs. Several techniques and methods can be applied for the learning and decision making processes, such as evolutionary computation, fuzzy logic and the Markov decision process [14]. Nonetheless, more advanced schemes are needed for optimized spectrum decisions.
###### Abstract  
In recent years, the research on bitumen has reached the nanometer level, but there are still some problems in the study of the relationship between the atomic force microscope (AFM) nano-morphology and ageing of bitumen. The purpose of this paper is to find out the effect of thermal oxygen ageing on the nano-morphology of bitumen. Atomic force microscope (AFM) test sample of bitumen was prepared in this paper. Area ratio of bee-like structure, roughness, and maximum amplitude were selected as the nano-morphological parameters of the bitumen. The calculation method of nano-parameters was also proposed. Bitumen with different ageing degrees was prepared by thin film oven test (TFOT). Nano-morphological parameters of bitumen with different ageing degrees were obtained by AFM and analyzed with NanoScope Analysis, and the relationships between nano-morphological parameters and bitumen technical indexes after ageing, such as penetration, ductility, softening point, and viscosity were analyzed. The test results show that the wave crests and wave troughs of the bee-like structure on the surface of the bitumen alternate, while the other areas are relatively flat. The bee-like structure continues to develop as the ageing time prolongs, and the number of bee-like structures decreases, but the volume and the undulation degree increase. With the ageing time prolonging, the nano-parameters of bitumen, such as the area ratio of bee-like structure, roughness, and maximum amplitude, show a trend of increasing gradually, but the increasing rates become smaller and smaller. During the ageing process of bitumen, with the increase of nano-morphological parameters (area ratio of bee-like structure, roughness, and maximum amplitude), penetration at 25 \({}^{\circ}\)C and ductility at 15 \({}^{\circ}\)C of the bitumen decrease, and softening point and viscosity at 60 \({}^{\circ}\)C increase. AFM testing technology and image analysis method in this paper can be used for a reference in the nano-scale study of bitumen.  
bitumen; thermo-oxidative ageing; nano-morphology parameters; bee-like structure; technical indices +
Footnote †: journal: Applied Soft. **2019**, \(9\), 3027; doi:10.3390/app9153027
# The Development of Current Collection in Micro-Tubular Solid Oxide Fuel Cells--A Review
## 5 Materials for Current Collection
### Micro-Tubular SOFC Geometry-Specific Current Collector Material Trend
Electroless plating achieves a more homogenous coating and finer control over layer thickness (proportional to exposure time for a given plating chemistry) than electroplating, particularly for intricate parts of differing thicknesses where an applied potential results in varying current densities, and thus differing rates of localised coating application [85; 86; 87]. A study by [PERSON], [PERSON] and [PERSON] [47] looked at cathode side current collection methods from a materials perspective. The experiment determined whether a perovskite-coated nickel-based steel (Nimonic 90) could show comparable performance to the more commonly used silver wire. The coating suppressed chromium migration, but the electrical performance could not match that of the silver wires. They further discussed the limitations of using silver due to its high evaporation rates at typical SOFC operating temperatures, meaning unpredicted temperature spikes could inflict irreparable damage to the silver. Wire and strips of the silver were very close in performance, while a comparison of silver conductive ink covering the entire cathode versus a two-strip configuration suggested the ink interference in cathode performance was negligible for this ink composition and cell geometry. The properties of typical SOFC interconnect materials are given Table 2.
## 3 Methodology
* On a separating hyperplane: \(\mathbf{x}\mathbf{w}+b=0\), where
* \(w\) normal to the hyperplane;
* \(\dfrac{\|b\|}{\mathbf{w}\|}\) is the distance to origin;
* \(\|\mathbf{w}\|\) Euclidean norm of \(\mathbf{w}\). * \(d_{+}\), \(d_{-}\) shortest distances from labeled points to hyperplane. * Define margin \(m=d_{+}+d_{-}\). * Task: find the separating hyperplane that maximizes \(m\). Figure 3: Flow chart showing the proposed architecture. Key point: Maximizing the margin minimizes the VC dimension. * For the separating plane: \[\mathbf{x}_{i}\mathbf{w}+b\geq+1,\ \ \ y_{i}=+1\] (1) \[\mathbf{x}_{i}\mathbf{w}+b\leq-1,\ \ \ y_{i}=-1\] (2) \[\equiv\] (3) \[y_{i}(\mathbf{x}_{i}\mathbf{w}+b)-1\geq 0,\qquad\forall i.\] (4)
* For the closest points the equalities are satisfied, so: \[d_{+}+d_{-}=\frac{|1-b|}{\|w\|}+\frac{|-1-b|}{\|w\|}=\frac{2}{\|w\|}.\] (5)
* One coefficient per training sample. * The constraints are easier to handle. * Training data appear only in dot products. * Great for applying the kernel trick later on. * Minimize \[L_{P}=\frac{\|w\|^{2}}{2}-\sum_{i=1}^{l}\alpha_{i}y_{i}(\mathbf{x}_{i}\mathbf{ w}+b)+\sum_{i=1}^{l}\alpha_{i}.\] (6)
* Convex quadratic programming problem with the dual: maximize \[L_{D}=\sum_{i=1}^{l}\alpha_{i}-\frac{1}{2}\sum_{i,j=1}^{l}\alpha_{i}\alpha_{j }y_{i}y_{j}(\mathbf{x}_{i}\mathbf{x}_{j}).\] (7)
* Those points having \(\alpha_{i}>0\) represent the support vectors. * Solution is mainly dependent on them. * The points with \(\alpha_{i}=0\) can be removed, or moved away arbitrarily from the hyperplane. * Once the hyperplane is found: Figure 3.0 (8)  
The optimal hyperplane is determined by solving the constrained optimization problem \(min(\frac{1}{2}w^{T}w)\), subject to \(y_{n}(w^{T}x_{n}+b)\geq 1\) for \(n=1,2,3,\ldots,N,w\in\mathbb{R}^{d},b\in\mathbb{R}\). A way to find the non-linear classifiers' kernel trick is applied to maximum-margin hyperplanes. In the resulting algorithm, a non-linear kernel function replaces every dot product, which enables it to locate the max-margin hyperplane in a new (mostly higher dimensional) feature space, essentially the transformed feature space. It can be a non-linear transformation and the new dimension can be of a higher dimension. The classifier in this space is linear, however, in the original input space, it can be non-linear. Although the generalization error increases in high-dimensional feature space, the algorithm still yields better results.
# Routing Selection Algorithm for Mobile Ad Hoc Networks Based on Neighbor Node Density
## 5 Simulations and Results
### Simulations
#### 5.1.2 Metrics  
We will evaluate the performance of the proposed algorithm based on the network's throughput, delay, and packet delivery ratio [26; 27]. These metrics will help us assess the effectiveness and efficiency of the algorithm in comparison to other approaches.  
Throughput: The total number of received data packets in a unit of time.  
\[\text{Throughput}=\sum\frac{\text{Receive bytes}\times 8}{\text{Simulation Time}\times 1024} \tag{9}\]  
Delay: Also known as average delay, it represents the time taken from the source node sending a data packet to the destination node receiving it.  
\[\text{Average Delay}=\sum\frac{\text{Time}_{\text{packetReceive}}-\text{ Time}_{\text{packetSent}}}{\text{Total Number of Packet Received}} \tag{10}\]  
Packet delivery ratio: The ratio of the number of data packets received at the destination node to the number of data packets sent by the source node.  
\[\text{Packet Delivery Ratio}=\frac{\sum\text{Packet Received}}{\sum\text{Packet Sent}}\times 100\% \tag{11}\]
## 3 Results
### Twice Prediction Length  
To investigate how well both methods perform with increasing prediction length, the prediction length was increased twice (256 ms: 128 samples). The prediction performance usually decays with time [44]. However, the LMS-based AR model showed better prediction performance when the length was increased, as shown in Figure 5.  
Representative PLVrz values as a function of predicted time points are shown for three channels (O1, O2, and Oz) in Figure 6. Both parts of the figure show a decay with respect to increasing prediction length and crossing the significance line. For this particular subject, the AR model shows the 378 ms (189 th sample) for crossing, while the LMS-based AR model shows the 416 ms (208 th sample) for crossing the significant line.  
Figure 5: Representative prediction data of the AR model and the LMS-based AR model for double the prediction length (256 ms). Original data segment is shown as the red signal, while the predicted signal is shown in blue. (**A**) AR model and its respective instantaneous phase and amplitude; (**B**) LMS-based AR model and its corresponding instantaneous phase and amplitude. Asterisk (*) shows the prediction starting point.  
Figure 6: [PERSON]’s Z value (PLVrz) for twice the prediction length (256 ms). (**A**) shows the AR model; (**B**) shows the LMS-based AR model. The red line indicates the significance level with a value of 2.9957. The box shows the time point (X-axis) at which the significant line is crossed with its respective PLVrz value (Y-axis).  
Both methods were compared using a paired \(t\)-test for three channels: O1, O2, and Oz. At 128 ms and the 256 ms, the LMS-based AR model showed higher PLVrz than the AR model. Although the performance of both methods declined with increasing prediction length, the LMS-based AR model outperformed the AR model when the length was increased two times, as shown by their mean PLV Rayleigh \(Z\) values (Figure 7).