esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
## 2 Vehicle Modeling
### Navigation
#### 3.1.2 Attitude Determination Algorithm
\[\left[\overrightarrow{i_{B_{i}}},\overrightarrow{j_{B_{i}}},\overrightarrow {k_{B_{i}}}\right]=DCM_{B,NED_{i}}\left[\overrightarrow{i_{NED_{i}}}, \overrightarrow{j_{NED_{i}}},\overrightarrow{k_{NED_{i}}}\right]\forall i \in\{tf,fl\} \tag{15}\]  
The DCM matrix can be solved from Equation (15) as it is shown in Equation (16). Employing an orthonormal base simplifies the calculation of the inverse matrix as it is the transposed matrix. \[DCM_{B,NED_{i}}=\left[\overrightarrow{i_{B_{i}}},\overrightarrow{j_{B_{i}}}, \overrightarrow{k_{B_{i}}}\right]\left[\overrightarrow{i_{NED_{fl}}}, \overrightarrow{j_{NED_{fl}}},\overrightarrow{k_{NED_{fl}}}\right]^{T}\forall i \in\{tf,fl\} \tag{16}\]  
After obtaining the two different director cosine matrices, which will be essentially similar matrices, the rotation is characterized. The most suitable method to express this rotation is through quaternions, as they avoid any possible singularities on the poles of rotation. It is widely known that quaternions themselves are enough to express rotations without singularities, but it is also known that conceptually they are difficult to be visualized. An easier manner to define these rotations is by means of Euler angles. Concretely, the most common aeronautical rotation is defined by roll (\(\phi_{i}\)), pitch (\(\theta_{i}\)), and yaw (\(\psi_{i}\)) angles for \(i\in\{tf,fl\}\). A method to obtain a quaternion solution is explainedin [2]. Note that two different values for each quaternion are obtained, \(i\in\{tf,fl\}\). This fact requires an hybridization between them in order to only obtain one value for each quaternion. Hybridization Algorithm  
(17)  
\[\kappa\big{|}_{n}=\Gamma\cdot[\Gamma+\Lambda]^{-1}, \tag{18}\]  
where \(\overrightarrow{Eul}\) are the Euler angles \((\phi,\theta,\psi)\), and \(\Gamma\) and \(\Lambda\) are the error covariance matrices for \(i=tf\) and for \(i=fl\) measurements, which are set to \(1.3\cdot 10^{-6}\) and \(0.95\cdot 10^{-3}\), respectively. Those values were determined empirically. The Euler angles obtained in (17) may be used to characterize rotations and angular speeds in navigation, guidance, and control algorithms. This basically means that \(\overrightarrow{\omega_{b}}\) is now known. Furthermore, from these Euler angles, the hybridized director cosine matrix \((DCM_{B,NED})\) may be calculated.
## 3 Road Surface Grade Identification
### Network Training  
The Long Short-Term Memory(LSTM) network is a sophisticated variation of the Recurrent Neural Network. It was specifically engineered to address the classic pitfalls of vanishing and exploding gradients that tend to plague regular RNNs, particularly when dealing with extended sequences [20]. This paper applies an LSTM network to the road surface classification task to accurately identify the road surface.  
The road surface grade identification model consists of an input layer, an LSTM layer, an activation layer, a fully connected layer, and a classification layer, as shown in Figure 4. First, the vehicle's vibrational response data are used as the input to the model and are processed by the LSTM layer to learn and extract temporal features, capturing the time dependencies in the data. Then, the feature vector output by the LSTM layer is passed to the fully connected layer for further integration and processing of the features' information. Finally, in the classification layer, the Softmax activation function is applied to classify the road surface grades, and the final road surface grade classification results are output.  
This study employs a single-layer LSTM network with 16 hidden units. Each input sample consists of a single feature vector representing one time step. Consequently, the LSTM processes individual time steps. At each time step, the output from the LSTM layer is funneled into the following layers. A ReLU activation layer follows this, and the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **5** & **10** & **15** & **20** & **25** & **30** \\ \hline RMS & 1 & 0 & 1 & 1 & 1 & 1 \\ \(\kappa\) & 0 & 1 & 1 & 1 & 1 & 0 \\ \(\mu\) & 0 & 1 & 1 & 0 & 0 & 0 \\ \(\sigma\) & 1 & 1 & 1 & 1 & 1 & 1 \\ \(f_{do}\) & 1 & 1 & 0 & 1 & 0 & 0 \\ \(E_{sp}\) & 1 & 1 & 1 & 0 & 1 & 1 \\ BER & 1 & 0 & 1 & 1 & 1 & 1 \\ Max PSD & 1 & 1 & 1 & 1 & 1 & 1 \\ WER & 1 & 1 & 0 & 1 & 1 & 1 \\ IFM & 1 & 1 & 1 & 1 & 1 & 1 \\ SE & 1 & 1 & 1 & 1 & 1 & 1 \\ H & 1 & 0 & 1 & 0 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Feature selection results.  
final classification is achieved through an output layer that employs the Softmax activation function. The Adam optimizer trains the model, starting with an initial learning rate of 0.01. The learning rate dives to 10% of its original value after every 80 epochs. The training wraps up after a maximum of 100 epochs, with each batch containing 128 samples. The training data are shuffled at the start of each epoch to improve the model's ability to generalize.
# Inference of Drawing Elements and Space Usage on Architectural Drawings Using Semantic Segmentation
## 3 Model Structure of Semantic Segmentation
### DeeplabV3+  
The resolution of the feature map gained from the encoder is 16 times lower than that of the original image. (Output Stride: 16). In the decoding for an effective recovery of image details, low-level features are included. 16 was selected as it was a good trade-off point of velocity and accuracy based on the experimental results. The 'rate' in the ASPP structure means the dilation rate of the atrous convolution. The \(1\times 1\) Conv is applied to the low-level features to reduce the output from the encoder and channels. Given 48 channels were experimentally proved to deliver the best result, 48 channels are applied to the low-level features. The \(3\times 3\) Conv is lastly performed in the decoder to bring a sharper result to the edge of the object. In the last part of the encoder, the \(1\times 1\) convolution is performed for upsampling followed by the dimensionality reduction and then concat. This is similar to the U-Net structure [8; 28; 30; 31].  
Figure 1: Structure of encoder-decoder.  
The backbone networks used in DeepLab v3+ are Xception and ResNet-101. In this study, despite not much difference in performance found between the two networks, Xception performed marginally better when its structure was slightly altered. To apply the atrous separable convolution, all pooling was replaced with the depth-wise separable convolution, and following each 3 \(\times\) 3 depth-wise convolution, batch normalization and ReLU function were added. DeepLab V3 uses the ResNet pre-trained on ImageNet as the default feature extractor. In the final block of ResNet, the atrous convolution with multiple dilation rates is used to extract different sizes of features. Also, the atrous spatial pyramid pooling (ASPP), which was introduced in the earlier Deeplab version, is used. It is not a new idea but a model where the attributes of high-performance models are incorporated. The convolution uses kernels with diverse dilation rates in parallel. [8; 28; 30; 31]
# Evaluation of Unfilled Sheath in Concrete Structures Using Response Waveform in Time Domain
## 2 Difference Value Analysis (DVA)  
### Difference Value  
The conventional technique can be applied only to the evaluation of plate-like structures. In contrast, DVA can be applied to the evaluation regardless of the shape of the structures. In particular, in the case of a structure constructed using commercialized concrete members, especially, DVA is a suitable index for the evaluation since it is possible to measure the response waveform in sound conditions. The response waveform in sound condition can be used for the evaluation of the other structures using the same product since it is considered that the product has the same response waveform in sound condition. This method uses the Difference value calculated based on the response waveforms of a structure in sound and damaged conditions in the time domain as the index value. Figure 6 shows a conceptual diagram of DVA. The difference of the models is only the existence  
Figure 4: Schematic of the propagation path and frequency of elastic waves in the internal void.  
Figure 5: Schematic of the propagation path of multiple reflection: (**a**) Rectangular cross-section; (**b**) Complex shaped beam.  
of the void as shown in Figure 6a,b. Approximated propagation paths of the model are illustrated in Figure 6a,b as well, and Figure 6c shows the examples of response waveforms. The difference value is calculated as Equation (5), and the location and size of internal defects are evaluated on the basis of the difference value.  
\[D_{V}=\frac{1}{N}\sum_{i=1}^{N}{(f_{i}-f_{i}^{\prime})^{2}}, \tag{5}\]  
where \(D_{V}\) is the difference value,\(f_{i}\) is the amplitude of the response waveform at time step \(i\) without the void, \(f_{i}^{\prime}\) is the amplitude of the response waveform at time step \(i\) with the void, and \(N\) is the number of data that are used for the calculation of the difference value. DVA evaluates the soundness in the thickness direction, mainly in the range between the input point and the measurement point. Therefore, \(N\) is determined to cover the time in which the reflected elastic wave in the thickness direction returns to the measurement point. The measurement time \(t\) required for this response is determined by Equation (6):  
\[T=\frac{2D_{C}}{\beta V_{P}}, \tag{6}\]  
where \(D_{C}\) is the propagation distance in the thickness direction of the concrete structures, \(\beta\) is the correction factor and \(V_{P}\) is the primary elastic wave velocity. This correction factor \(\beta\) varies depending on the cross-sectional shape of the measurement target. Therefore, the minimum number of data required for calculation is determined by Equation (7) from the measurement time and sampling interval:  
\[N_{min}=\frac{t}{\Delta t}, \tag{7}\]  
where \(N_{min}\) is the minimum number of data required for calculation and \(\Delta t\) is the sampling interval.  
Figure 6: Concept of Difference value Analysis in Time Domain: (a) No void; (b) With void; (c) Example of response waveform comparison.
# On the Use of LoRaWAN for Mobile Internet of Things: The Impact of Mobility
## 3 Mobility Models and Methodology
### Mobility Models  
Even though LoRaWAN is typically used for static communication scenarios, there exists some usage of LoRaWAN for mobile scenarios. LoRaWAN can be used for mobile scenarios such as fleet monitoring, bike rentals, and wildlife and animal tracking. Therefore, mobility models should be considered in the field of LoRaWAN. However, testing LoRaWAN for mobile scenarios with real devices is costly and difficult due to area-specific restrictions in some scenarios, such as wildlife and animal tracking applications. Consequently, there is a need to evaluate the performance of LoRaWAN for these scenarios in simulated environments while using different mobility models. This study considers the use of two different mobility models--namely, the random waypoint mobility model, and the Gauss-Markov mobility model.
## 3 Results and Discussion
### Composites Characterization
#### 3.2.4 Differential Scanning Calorimetry (DSC)  
The DSC thermal behaviors (heating and cooling) of the neat PP and the BC/PP composite systems are presented in Figure 7a,b. The heat of melting (\(\Delta\)H\({}_{\text{m}}\)) and crystallization heat (\(\Delta\)H\({}_{\text{c}}\)) were determined from the areas of melting and crystallization peaks, and the results are summarized in Table 5. The melting temperature (T\({}_{\text{m}}\)) of all the samples was found to be \(\sim\)163 \({}^{\circ}\)C, corresponding to the typical melting of PP. The type of BC was observed to have negligible effect on the melting temperature of BC/PP composites. However, the crystallization temperature (T\({}_{\text{c}}\)) of neat PP appeared at 109 \({}^{\circ}\)C and was found to significantly increase with BC incorporation into BC/PP composites. The crystallization temperature of the composites was also found to increases proportionally with the BC pyrolysis temperature. This could be attributed to the increased number of BC particles and which in turn increases the number of nucleation sites available for crystallization. Furthermore, the overall degree of crystallinity was found to increase in BC/PP composites. This is attributed to the nucleation effect of the biochar particles in the PP matrix. It means the biochar particles acted as points from where crystal growth initiated. Similar nucleating effects through incorporation of other carbonaceous fillers into PP matrix has also been reported [35; 36].  
Figure 7: \(Cont.\)
# An Improved Low-Density Parity-Check Decoder and Its Field-Programmable Fate Array Implementation
## 3 Improved Decoding Algorithm
### TNMS Decoding Algorithm
In summary, the improved decoding algorithm requires n additions and (4 \(\times\) n + 2 \(\times\) m)/3 shifts per iteration to achieve the correction factor \(\beta\) of 1.25. As shown in Table 2, for LDPC decoding with a code length n, check bit length m and a code rate R = (n - m)/n:  
When the code rate R = 2/3, the optimal \(\alpha\) values for the NMS algorithm are 0.73 and 0.8. Transitioning to the TNMS algorithm can reduce computational overhead. When the code rate R = 1/2, the optimal \(\alpha\) values for the NMS algorithm are 0.73, 0.74, 0.79, 0.8, 0.84, and 0.85. Transitioning to the TNMS algorithm can reduce computational overhead. When the code rate R = 1/3, the optimal \(\alpha\) values for the NMS algorithm are 0.72, 0.73, 0.74, 0.78, 0.79, 0.8, 0.83, 0.84, and 0.85. Transitioning to the TNMS algorithm can reduce computational overhead. When the code rate R = 1/4, the optimal \(\alpha\) values for the NMS algorithm are 0.7, 0.71, 0.72, 0.73, 0.74, 0.78, 0.79, 0.8, 0.83, 0.84, and 0.85. Transitioning to the TNMS algorithm can reduce computational overhead. When the code rate R = 1/5, the optimal \(\alpha\) values for the NMS algorithm are 0.7, 0.71, 0.72, 0.73, 0.74, 0.77, 0.78, 0.79, 0.8, 0.82, 0.83, 0.84, and 0.85. Transitioning to the TNMS algorithm can reduce computational overhead. From this data, it is evident that the lower the code rate, the broader the applicability of the improved decoding algorithm.
# Numerical Integration-Based Performance Analysis of Cross-Eye Jamming Algorithm
## 5 Conclusions
In practical implementation of cross-eye jamming, in the viewpoint of modeling of perturbations in amplitude ratio and phase difference as Gaussian random variables, two extensions can be considered. At first, in the proposed scheme, the amplitude ratio and phase difference are modeled as zero-mean Gaussian random variables, which are \(\delta_{a}\) and \(\delta_{\phi}\). In practice, due to systematic bias in jammer system, \(\delta_{a}\) and \(\delta_{\phi}\) can be non-zero mean Gaussian random variables. The formulation for non-zero mean Gaussian random variable can be derived by extending the formulation propose in this paper. In addition, \(\delta_{a}\) and \(\delta_{\phi}\) can be correlated. Since the amplitude ratio and phase difference are obtained from the magnitude and angle of a complex number, they can be correlated. Although taking into account the fact that the amplitude ratio and phase difference can be correlated makes the formulation more complicated, the scheme proposed in this paper, where the amplitude ratio and phase difference are assumed to be uncorrelated, can be extended to the case where the amplitude ratio and phase difference are correlated. [PERSON] made a MATLAB simulation and wrote the initial draft. [PERSON] and [PERSON] derived the mathematical formulation of the proposed scheme. In addition, [PERSON] checked the numerical results and corrected the manuscript. All authors have read and agreed to the published version of the manuscript. The authors gratefully acknowledge the support from the Electronic Warfare Research Center at Gwangju Institute of Science and Technology (GIST), originally funded by the Defense Acquisition Program Administration (DAPA) and Agency for Defense Development (ADD). The authors declare no conflict of interest.
## 2 Identifying and Defining the Components and Drivers of Transformation
### Technology Perspective  
Blockchain has been labeled both as a revolution [4] and as \"one of the most overhyped technologies ever\" [13]. While blockchain enthusiasts never tire in praising one potential use case after another, critics frequently point out that centralized systems often produce comparable results with less complexity. A lot of confusion in the ongoing discussion is caused by unclear terminology and failure to recognize that there is no such thing as a uniform blockchain system. Instead, numerous deployments of distributed ledgers vary substantially in their implementation details, such that some of them do not even exhibit a chain-like structure.  
In this context, it makes sense to first clarify the underlying terminology. The International Organization for Standardization [14] provides a comprehensive list of relevant definitions including \"distributed ledger technology\" (i.e., a \"ledger that is shared across a set of DLT nodes and synchronized between the DLT nodes using a consensus mechanism\") and \"blockchain\" (i.e., a \"distributed ledger with confirmed blocks organized in an append-only, sequential chain using cryptographic links\"). Therefore, DLTs also include blockchains but, to make things simple, in the remainder of this paper, we will only refer to blockchain. In academia, alternative definitions for blockchain tend to also include their intended goals (e.g., \"blockchain is a digital, decentralized and distributed ledger in which transactions are logged and added in chronological order with the goal of creating permanent and tamperproof records\" ([PERSON], 2018, [15] p. 547)).  
Most importantly, it is the features of a system that ultimately yield benefits and not blockchain \"per se\". In this regard, [PERSON] et al. [16] identify six relevant properties of distributed ledgers, namely: flexibility (the degrees of freedom associated with deploying applications and customizing a DLT), opaqueness (the level of untraceability), performance (the accomplishment of tasks), policy (the ability to guide and verify correct operations), practicality (the achievement of goals with respect to social and socio-technical constraints), and security (the uncompromised functioning of the ledger and the integrity of its stored data). They further unravel those properties into 40 characteristics to describe distributed ledgers on a very granular level. Most of the characteristics are not binary (i.e., existent/non-existent) but rather reflect the current status of development in comparative terms. For example, characteristics such as availability, durability, or fault tolerance can never be perfect but need to be measured on a continuous scale, measuring the level of target attainment. Presumably, it will mostly be researchers with a technical interest who will investigate the development of one or more specific characteristics in close detail.
## 2 Description of the Research Task and Measuring Instruments
More detailed information about the sensors and the method of their installation in pipelines of different configurations can be found in the article [34]. This article presents data obtained with the help of sensors located at a distance of 150 mm from the turbulator (see also Figure 1). Signals from all sensors were fed to the LCard (Moscow, Russia) analog-to-digital converter (ADC) in real time, after which the digitized signals were transferred to specialized LGraph2 (version 3.25.20) software (Russia) for storage and processing. The response time of the measuring system based on a H-WA and ADC was from 3 to 6 ms. This response time is sufficient for studying pulsating flows with a frequency of up to 100 Hz. The relative uncertainty in determining \(w_{x}\) was 5.3%; the relative uncertainty in determining \(\alpha_{x}\) was 10.9%; the relative uncertainty in measuring the flow temperature was 1.0%. The following errors of measuring instruments were taken into account to determine the relative uncertainty of the experiment: barometric pressure (barometer, 0.1%), pressure drop in the flow (micromanometer, 2.2%), air temperature (thermocouple, 1.0%), linear dimensions of the pipe (caliper, 0.6%), and the thermophysical properties of substances (handbook, 2.0%). The relative uncertainty was also calculated considering the conversion of analog signals from sensors into digital data (1.1%). Thus, a gas-dynamic system with an automated measuring base was developed, which made it possible to study the gas dynamics and heat exchange of stationary and pulsating air flows with different pulsation frequencies and different turbulence intensities. The experimental stand has the possibility of modernization for the study of other system configurations and initial conditions of tests.
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## Preliminaries and Definitions
### Bilinear Pairing  
Let \(\mathbb{G}\) and \(\mathbb{G}_{\mathbb{T}}\) be two multiplicative cyclic groups of prime order \(p\). let \(\mathrm{g}\) be a generator of \(\mathbb{G}\) and \(e\) be a bilinear map, \(e:\mathbb{G}\times\mathbb{G}\rightarrow\mathbb{G}_{T}\). The bilinear map \(e\) has the follow properties:  
Bilinearity: for all \(u\), \(v\in\mathbb{G}\) and \(\mathrm{a}\), \(\mathrm{b}\in Z_{\mathbb{P}}\), we have \(e(u,v)^{\mathrm{ab}}\ =\ e(u,v)^{\mathrm{ab}}\).  
Non-degeneracy: \(e(g,g)\
eq 1\).  
Computability: to compute the pairing \(\mathrm{e}\) is efficient.