esa-sceva/satcom-chunk-collection huggingface dataset:
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 5 Performance Evaluation
### Performance Metrics  
The effectiveness of the proposed PAUM system has been demonstrated using the following performance metrics:  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Cellular layout & Hexagonal Grid, 3 sectors per site \\ \hline Carrier & 900 MHz \\ \hline Inter-site distance & 500 m/1732 m \\ \hline UE deployment & Uniform random distribution \\ \hline Event generation at UEs & Uniform random distribution \\ \hline BS Transmit Power & 43 dBm \\ \hline UE transmit power & Maximum 23 dBm \\ \hline Inter-site correlation co-efficient & 0.5 and 0.75 \\ \hline \(\epsilon\) & 0.0005 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation Parameters.  
Average Utility:It measures how the high priority users with higher data rate receive transmission opportunities in the network. Utilities achieved by individual UEs, as calculated in Equation (4), are then averaged to plot the graph.  
Average Packet Delivery Ratio:It is defined as the ratio of the number of packets delivered at an eNB to the total number of packets sent from all source nodes under that eNB. The higher value shows improved performance.  
Radio Resource Access Fairness:This is used to determine whether users are receiving a fair share of system radio resources. Our system approaches [PERSON]'s index, which provides a fairness criterion that considers all the users of the system, not only those assigned minimal resources. The fairness index starts from 0, and a value close to 1 indicates a fair share of radio resource access.  
Average Packet Delivery Delay:It is the average time delay experienced by all data packets from UEs to reach at the corresponding eNB. The lower value indicates higher performance.  
Classification Accuracy:It is a measurement of how correctly events are identified as critical, relevant, and normal after applying Bayesian learning.
# Protection of Buried Pipelines from High-Speed Train Vibrations with Expanded Polystyrene Geofoam
## 3 Results
### Impact of EPS Layer Thickness on Pipeline Protection
DIN 4150-3 standard defines the \(PPV\) as follows [38]:  
\[PPV=\max\abs{\sqrt{v_{x}^{2}(\mathrm{t})+v_{y}^{2}(\mathrm{t})+v_{z}^{2}( \mathrm{t})}} \tag{5}\]  
where \(v_{x}\)(t), \(v_{y}\)(t), and \(v_{z}\)(t) are the longitudinal, transversal, and vertical velocity time-histories, respectively. As it is clearly illustrated in Figure 13, the most effective mitigation measure is the implementation of a thick layer (50 cm) with low-density EPS19 blocks. As the HST passing velocity increases, the \(PPV\) level is also increased. The same trend is observed for the thinner EPS19 layers. Figure 14 depicts the \(PPV\) levels at the top of the pipeline at an increasing distance from the HST passing axis. As was expected, the \(PPV\) values are (slightly) reduced at an increasing distance from the track both for the protected and the unprotected pipeline. Evidently, the \(PPV\) on the top of the pipeline is reduced rapidly for all the examined passing velocities when the EPS layer with a height of 50 cm is added for all EPS heights. For example, when the Thalys HST is passing with 240 km/h, the \(PPV\) of the unprotected pipeline is reduced from 3.7 mm/s to 1.4 mm/s at 6 m from the track. The corresponding values after the implementation of EPS19 were decreased to 2.6 mm/s and 1.4 mm/s (at 6 m). The same tends were noticed for an HST passing with 300 km/h and 360 km/h. Lastly, it is also obvious that the proposed mitigation measure does not affect the vibrations level at distances higher than 6 m from the rail axis.
# A Short-Term Forecasting Method of Inter-Frequency Clock Bias for GPS Block IIF Satellites
## 3 Results
### Extraction of GPS IFCB
When the time lag was 1, 9, 19 and 29 days, the peak-to-peak amplitudes of the residuals reached 1.13, 3.44, 6.86 and 11.25 cm, respectively. This phenomenon was because the periods of the IFCB time series on different days were not strictly consistent, namely, the IFCB on different days shows an approximate period characteristic for all Block IIF satellites, and this approximation gradually becomes smaller with the increase in lag time. Following [PERSON] et al. (2016), the IFCB periods of the Block IIF satellites roughly coincide with their orbital periods, which are close to the length of half a sidereal day [14]. Therefore, when the IFCB of one day is applied to other days, the periodic changes in the IFCB time series for different days must be considered.
## 2 Related Works and Motivations
### Statistical En-routed Filtering Scheme (SEF)  
SEF was the first scheme to address false data injection attacks in the presence of compromised nodes and it focuses on the detection of false event reports, which are known as false positive attacks, injected by compromised nodes. In SEF, the base station maintains a global key pool, which is divided into multiple partitions and every node loads a small number of keys from a randomly selected partition in the global key pool before it is deployed.  
When real events occur, one of the detecting nodes is elected as the center-of-stimulus (CoS) node to generate a sensing report. The surrounding nodes, which detect the same event, produce MACs for the event, using their stored keys, and send them to the CoS which generates a sensing report using the collected MACs. This set of multiple MACs acts as the proof that a report is legitimate [9] after which points the CoS forwards the report toward the base station (BS) over multi hops. Each forwarding node verifies the correctness of the MACs carried in the report by using its keys. When the BS receives a report, it can verify all the MACs carried in the report because it has complete knowledge of the global key pool [9].
## 3 Results and Discussion
### Physical Characterization
For NDG/Fe\({}_{3}\)O\({}_{4}\) (Figure 4b), the peaks observed at 30.2\({}^{\circ}\), 35.5\({}^{\circ}\), 43.2\({}^{\circ}\), 57.1\({}^{\circ}\), 62.7\({}^{\circ}\), and 74.2\({}^{\circ}\) are related to the (220), (311), (400), (511), (440), and (533) planes of the Fe\({}_{3}\)O\({}_{4}\) cubic crystalline system (JCPDS 01-088-0315). The peaks observed  
Figure 3: Field Emission Scanning Electron Microscopy (FESEM) images of (**a**) NDG, (**b**) NDG/Fe\({}_{3}\)O\({}_{4}\), (**c**) NDG/Mn\({}_{3}\)O\({}_{4}\), and (**d**) NDG/Mn\({}_{3}\)O\({}_{4}\)/Fe\({}_{3}\)O\({}_{4}\). at 18.0\({}^{\circ}\), 32.5\({}^{\circ}\), 36.2\({}^{\circ}\), 38.1\({}^{\circ}\), 44.6\({}^{\circ}\), 58.8\({}^{\circ}\), and 60.0\({}^{\circ}\) in the XRD pattern of NDG/Mn\({}_{3}\)O\({}_{4}\) (Figure 4c) corresponds to the (101), (103), (211), (004), (220), (321), and (224) planes of tetragonal crystallinity in Mn\({}_{3}\)O\({}_{4}\) (JCPDS 00-001-1127). For NDG/Mn\({}_{3}\)O\({}_{4}\)/Fe\({}_{3}\)O\({}_{4}\), almost all the characteristic peaks for Fe\({}_{3}\)O\({}_{4}\) and Mn\({}_{3}\)O\({}_{4}\) can be observed (Figure 4d), confirming that both Fe\({}_{3}\)O\({}_{4}\) and Mn\({}_{3}\)O\({}_{4}\) exist in the composite. The diffraction peaks of Fe\({}_{3}\)O\({}_{4}\) in NDG/Mn\({}_{3}\)O\({}_{4}\)/Fe\({}_{3}\)O\({}_{4}\) shift to a lower angle, as compared to NDG/Fe\({}_{3}\)O\({}_{4}\), indicating a partial change of the lattice structure of Fe\({}_{3}\)O\({}_{4}\) after the addition of Mn\({}_{3}\)O\({}_{4}\)[7].
# An Improved Low-Density Parity-Check Decoder and Its Field-Programmable Fate Array Implementation
## 2 LDPC Code Structure and Decoding Algorithm
### Structure in the IEEE 802.16e Standard  
The IEEE 802.16e standard specifies a maximum code length of 672. In this paper, the focus is on the (672,336) LDPC code. This code starts with 336 bits of original information and generates 336 parity bits through encoding. The code is transmitted over the channel and then decoded iteratively. The IEEE 802.16e protocol includes six different base parity-check matrices, with the base matrix \(H_{\mathbf{b}}\) (at a code rate of 1/2) illustrated in the following Figure 2.  
The parity-check matrix H is derived by expanding the base matrix \(N(i)\lor j\). Different code lengths correspond to different expansion factors z. Depending on these factors, the base parity-check matrix \(H_{\mathbf{b}}\) requires updating. Let \(q(i,j)\) denote the element at the \(i\)-th row and \(j\)-th column in the base parity-check matrix. The update to \(q(i,j)\) is as follows:  
\[q(i,j)=\left\{\begin{array}{l}q(i,j),q(i,j)\leq 0\\ \left[q(i,j)*z/96\right],q(i,j)>0\end{array}\right. \tag{13}\]  
Here, \(\lfloor x\rfloor\) represents the floor operation on x. According to the IEEE 802.16e standard, the expansion factor z corresponding to the 672-length LDPC code is 28. Substituting this value, the base parity-check matrix for the 672-length LDPC code remains as depicted in Figure 3.  
The matrix H, expanded from the base parity-check matrix \(H_{\mathbf{b}}\), is defined as follows:  
\[H=\left[\begin{array}{cccccc}I_{0,0}&I_{0,1}&I_{0,2}&\cdots&I_{0,n_{\mathbf{ g}-2}}&I_{0,n_{\mathbf{g}-1}}\\ I_{1,0}&I_{1,1}&I_{1,2}&\cdots&I_{1,n_{\mathbf{g}-2}}&I_{1,n_{\mathbf{g}-1}}\\ I_{2,0}&I_{2,1}&I_{2,2}&\cdots&I_{2,n_{\mathbf{g}-2}}&I_{2,n_{\mathbf{g}-1}}\\ \cdots&\cdots&\cdots&\cdots&\cdots&\cdots\\ I_{m_{\mathbf{g}-1},0}&I_{m_{\mathbf{g}-1},1}&I_{m_{\mathbf{g}-1},2}&\cdots&I_ {m_{\mathbf{g}-1},n_{\mathbf{g}-2}}&I_{m_{\mathbf{g}-1},n_{\mathbf{g}-1}}\\ \end{array}\right] \tag{14}\]  
Figure 3: Base parity-check matrix for IEEE 802.16e standard LDPC code with a code length of 672.  
Figure 2: Base parity-check matrix for IEEE 802.16e standard LDPC code at 1/2 code rate.  
In this structure, \(I_{i,j}\) represents a z-by-z zero matrix or a permutation matrix. If the value \(q\)(i,j) at the corresponding position in the base parity-check matrix \(H_{\text{b}}\) is less than zero, the corresponding position \(I_{i,j}\) in the parity-check matrix H will be a zero matrix. Otherwise, \(I_{i,j}\) represents a permutation matrix derived by cyclically right-shifting an identity matrix through \(q\)(i,j) positions.
## 2 Related Work
### Qat  
Quantization-Aware Training (QAT) represents a significant evolution in the application of quantization techniques within the training process of deep neural networks. Unlike post-training quantization, where the model is quantized after being fully trained, QAT integrates quantization into the training loop itself, allowing the model to learn how to cope with the reduced precision from the outset [3; 10; 23; 24; 25]. This approach ensures that the final model is better adapted to low-precision hardware, potentially leading to smaller and faster models that do not sacrifice accuracy [26].  
The core idea behind QAT is to simulate the effects of quantization during training by quantizing the weights and activations in each forward pass while maintaining full precision during the backward pass [27]. This process allows the network to learn to be robust to the quantization process, effectively \"pre-conditioning\" it for deployment on low-precision hardware. The pioneering work demonstrated that by integrating quantization into the training process, models could be optimized for efficient integer arithmetic during inference, significantly reducing the computational overhead [4].  
Recent advancements in QAT have focused on enhancing its flexibility and robustness. Techniques such as stochastic rounding have been introduced to mitigate the impact of quantization noise by randomly rounding the weights and activations, which helps in maintaining the gradient flow during training. Additionally, researchers have developed methods to adaptively adjust the quantization parameters, such as the bit-widths, during training based on the model's performance [6; 28]. This dynamic adjustment allows for a more fine-grained control over the trade-off between model size and accuracy, making QAT applicable to a wider range of models and tasks [29].  
Moreover, QAT has been extended to support mixed-precision training, where different layers of the network are trained at different precision levels based on their sensitivity to quantization [30; 31; 32]. This approach allows for more efficient use of the available bit-widths, enabling the training of larger models on the same hardware [33].
# Recycling of Low-Quality Carbon Black Produced by Tire Pyrolysis
## 4 Conclusions  
When waste tire pyrolysis occurs under non-optimized conditions, the resulting raw carbon black poses health concerns due to the elevated concentrations of PAHs, limiting its practical applications. Additionally, the presence of significant bituminous substances leads to a reduced specific surface area and particle agglomeration.  
Employing gravitational dry or wet grinding effectively breaks down these agglomerates, with wet grinding proving particularly effective in reducing harmful contaminants and preventing re-agglomeration. This processed material is suitable for use as a pigment in applications such as mortars and concrete mixtures, exhibiting excellent coloring properties and resistance to basic environments.  
Incinerating raw carbon black from tire pyrolysis is challenging due to the high SOx content in flue gases, overloading the conventional DeSOx equipment (Babcock Power Environmental Inc., Worcester, MA, USA). However, the developed method provides a solution, enabling the utilization of low-quality recycled carbon black in high-calorific-value solid fuel compositions. This innovative approach enhances the viability and versatility of carbon black from waste tire pyrolysis.  
Conceptualization and methodology by J.L.; experimental and investigation by E.R.; chemical analyses and data curation by U.M.; writing--original draft preparation by J.L. and E.R.; writing--review and editing by U.M.; supervision J.L.; project administration, by E.R.; funding acquisition by U.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Estonian Business and Innovation Agency, project 2014-2020.4.04.21-1755.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Engineer [PERSON] is acknowledged for his contribution to assembly and tuning of test equipment.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**rCB to Peat Ratio**} & \multicolumn{1}{c}{**Unit**} & \multicolumn{1}{c}{**1 + 9**} & \multicolumn{1}{c}{**1 + 4**} & \multicolumn{1}{c}{**1 + 1**} \\
**Parameter** & & & & & \\ \hline Moisture content, & wt\% & 49.3 & 32.8 & 23.4 \\ Ash content of dry matter & wt\% & 11 & 13 & 18 \\ Calorific value & MJ/kg & 20.975 & 21.108 & 21.783 \\ CO\({}_{2}\) emission & kg/kg & 1.9 & 1.98 & 2.08 \\ SO\({}_{2}\) emission & kg/kg & 20 & 32 & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the briquettes made of different rCB/peat mixtures.
### Upper Limb Motion Reconstruction  
In reliable WSN-based rehabilitation systems, multiple accelerometers, gyroscopes and magnetometers collect raw data related to linear accelerations, angular rates and magnetic field data of the patient body movements. This data and the corresponding timestamps are transmitted to a base station for accurate estimation of the body segments movements in a global coordinate reference system [27]. More accurate estimations of the segments' orientations can be obtained by taking into account restrictions related to the feasible orientation directions and ranges of the joint angles. At the next step, the reconstruction of the posture of the subject takes place, adopting the Denavit-Hartenberg (DH) configuration [17] or a Quaternion-based [14] approach.  
In the final processing stage, using kinematic analysis, the limb posture and orientation is visualized in a 3-Dimensional (3D) digital representation. Using this representation,the extraction of several distinct characteristics, such as the movement amplitude, segments' range of motion, movement velocity, is accomplished. This could give useful information during each exercise within a rehabilitation session. Furthermore, in some cases, the extraction of motion patterns is achieved via Principal Component Analysis (PCA) [28] or Machine Learning algorithms and techniques [19].
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 3 Applications  
LoRaWAN aims at becoming the de facto wireless connectivity standard for the Internet of Things (IoT) applications. Providing services that span areas ranging kilometers and to be implementable in devices that operate requiring very low energy is promising among industry and academia.  
In our previous work [9] have introduced how e-Health application can be adopted with LoRaWAN and evaluated the performance of the time critic data transmissions. Since the protocol released in 2015 [7], a high number of IoT application has been powered by the LoRaWAN protocol. In this section we will cover a variety of LoRaWAN based applications and Network Servers available today.
# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
## 2 Vehicle Modeling
### Control System  
The control law presented in [29] is employed in the current research. Two control conditions are presented in the actuation device: the modulus and the angle for the control force. Control is computed by a double loop feedback system. The inner loop aims at augmenting the stability of the vehicle. Equation (22) characterizes modulus (\(\tau_{c}\)) and angle (\(\phi_{c}\)) of the control force. Its inputs are pitch (\(\theta_{dem}\)) and yaw (\(\psi_{dem}\)) errors. \(K_{i}\), \(K_{d}\) and \(K_{p}\) are the integral, derivative and proportional constants of the controller, \(K_{mod}\) is a constant to adjust force module and \(L1\) and \(L2\) are experimental gains. The procedure to decide these constant values, which appear in Table 5, is clarified in [1]. Note that the acceleration, without accounting for gravity, of the vehicle in body axes is defined by \([acc_{xb},acc_{vb},acc_{zb}]\). Euler angles as introduced before are represented by \([\phi,\theta,\psi]\).  
\[\left[\begin{array}{c}\phi_{c}\\ \tau_{c}\end{array}\right]=\left[\begin{array}{c}K_{p}\left(E1-E2\right)+K_ {i}\int\left(E1-E2\right)dt+K_{d}\frac{d}{dt}\left(E1-E2\right)+E1\\ K_{mod}\sqrt{(\theta_{dem}-L_{1}\theta)^{2}+(L_{2}(\psi_{dem}-L_{1}\psi))^{2}} \end{array}\right] \tag{22}\]  
\[\text{where }\left\{\begin{array}{c}E1=atan\frac{\theta_{dem}-L_{1}\theta}{L_{2}( \psi_{dem}-L_{1}\psi)}\\ E2=atan\frac{acc_{zb}}{acc_{zb}}\end{array}\right.\]  
Summarizing, the control law works as follows. The controller determines the required pointing angle of the aerodynamic force. This is calculated obtaining the arc-tangent of the quotient of the pitch and yaw error, which provides an angle, in the \(y_{b}-z_{b}\) plane, at which the aerodynamic force should point to reach the objective. However, due to gyroscopic effects, the response of the vehicle is hard to govern, i.e., pointing the control force upwards may not make the vehicle to react upwards. Consequently, knowing the acceleration of the vehicle, without accounting for gravity, is a must. Similarly, the difference between \(\phi_{c}\) and the angle the projection of the aerodynamic force in the \(y_{b}-z_{b}\) plane forms with \(y_{b}\) needs to be determined [1].  
The aforementioned parameters of control are transformed into canard surface deflections, i.e., \(\delta_{1},\delta_{2},\delta_{3}\) and \(\delta_{4}\), which are ruled by two different actuators, as it is shown in Equation (23).  
\[\left[\begin{array}{c}\delta_{1}\\ \delta_{2}\\ \delta_{3}\\ \delta_{4}\end{array}\right]=\tau_{c}\left[\begin{array}{c}sin\phi_{c}\\ cos\phi_{c}\\ sin\phi_{c}\\ cos\phi_{c}\end{array}\right] \tag{23}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Phase of FlightConstant** & \(C_{1}\) & \(C_{2}\) & \(K_{i}\) & \(K_{p}\) & \(K_{d}\) & \(K_{mod}\) & \(L_{1}\) & \(L_{2}\) \\ \hline Intermediate phases & \(-7.5\) deg & \(-21\) deg & \(0\) & \(0.5\) & \(0\) & \(0.08\) & \(0.01\) & \(100\) \\ \hline Terminal
## Abbreviations  
AFRB  
Autonomous Field Robotics Laboratory  
AHRS  
Attitude and Heading Reference System  
AUV  
Autonomous Underwater Vehicle  
BITAN  
Beijing university of aeronautics and astronautics Inertial Terrain-Aided Navigation  
Brandler and Kohout  
CRNN  
Convolution Recurrent Neural Network  
DR  
Dead-Reckoning  
DSO  
Direct Sparse Odometry  
DT  
Distance Traveled  
DVL  
Doppler Velocity Logger  
EKF  
Extended Kalman Filter  
ELC  
Extended Loosely Coupled  
FLS  
Forward-Looking SONAR  
FITPS  
Fitting of Two Point Sets  
GBNN  
Glasius Bio-inspired Neural Network  
GN  
Geophysical Navigation  
GPS  
Global Positioning System  
HSV  
Hue Saturation Value  
I-AUV  
Intervention AUV  
IMU  
Inertial Measurement Unit  
INS  
Inertial Navigation Systems  
KF  
Kalman Filter  
LBL  
Long Baseline  
LC  
Loosely Coupled  
LCI  
Language-Centered Intelligence  
MEMS  
Micro-Electron-Mechanical System  
NRT  
Near-Real-Time  
NN  
Neural Networks  
PF  
Particle Filter  
PL-SLAM  
Point and Line SLAM  
PMF  
Point Mass Filter  
PS  
Pressure Sensor  
PTAM  
Parallel Tracking And Mapping  
RMSE  
Root-Mean-Square Error  
RNN  
Recurrent Neural Network  
ROS  
Robot Operating System  
SBL  
Short Baseline  
SINS  
Strapdown Inertial Navigation System  
SITAN  
Sandia Inertial Terrain Aided Navigation
## 2 Performance Support System
### Definitions, Principles, and Theoretical Foundations
#### 2.1.4 Procedures for Making a Call  
From the moment the subscriber initiates a call to when the traffic channel is successfully assigned to him or her, various steps are performed in the network. In Figure 3, the different stages are illustrated, along with the indicators used to measure the statistical status of the network. These indicators are fully explained in the following part of this section.