esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems
## 4 Performance Evaluation
### Field Tests and Results  
To further evaluate the proposed approach, the experimental data provided by the vehicle in Figure 10 is treated as the navigation data of a vehicle on the surface of the water. The experimental  
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline \multirow{2}{*}{**Malfunctions Length**} & \multirow{2}{*}{**Solution**} & \multicolumn{2}{c}{**Absolute Maximum Velocity Error (m/s)**} & \multicolumn{2}{c}{**Absolute Maximum Position Error (m)**} \\ \cline{3-6}  & & **East** & **North** & **Latitude** & **Longitude** \\ \hline \multirow{4}{*}{60 s} & Insulate DVL & 0.6869 & 2.438 & 61.03 & 12.92 \\  & PLRS & 0.2366 & 0.1744 & 2.792 & 1.705 \\  & RBF & 0.1907 & 0.2032 & 4.045 & 1.693 \\  & PLRS-SVR & 0.0793 & 0.0544 & 0.8216 & 1.224 \\ \hline \multirow{4}{*}{120 s} & Insulate DVL & 3.63 & 6.679 & 251.6 & 280.2 \\  & PLRS & 0.4306 & 0.2311 & 18.69 & 25.49 \\ \cline{1-1}  & RBF & 0.1672 & 0.189 & 12.04 & 20.08 \\ \cline{1-1}  & PLRS-SVR & 0.1261 & 0.1059 & 6.1113 & 8.783 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Error statistics of velocity and position during DVL malfunctions.  
Figure 9: Navigation errors when there is a DVL malfunction for a length of 120s: (**a**) Velocity errors; (**b**) Position errors.  
GPS that provides velocity information can pretend to be a DVL. To simulate a DVL malfunction for a length of 120 s, the velocity outputs of GPS are set to be zero during the period 150-270 s. Since GPS provides both velocity and position information, the position information is also employed to revise the SINS calculating results when GPS is available. In the case of malfunction, the integrated navigation system is processed by two different solutions: (1) only SINS; (2) use PLSR-SVR hybrid predictor. Figure 12 shows the navigation errors of the two solutions when there is a malfunction for a length of 120 s.  
Figure 12 shows that, the velocity and position errors of the system with the first solution increase during the malfunction period. Specifically, the maximum errors of east and north velocity reach to 0.546 m/s and 0.399 m/s respectively. In addition, the maximum errors of latitude and longitude reach to 18.78 m and 39.23 m respectively. By contrast, the system with the PLSR-SVR hybrid predictor acquires better accuracy on both velocity and position. Since the predictor provides the predictions of the velocity measurements, the accumulated SINS velocity errors are corrected by the prediction information. When the malfunction is removed, the position errors are corrected quickly due to the auxiliary position information offered by GPS. Above all, the field test results are consistent with the simulative results.  
The proposed approach is validated by both simulation and field test results. Whenever a short-term DVL malfunction occurs, the hybrid predictor can immediately provide velocity information to restrain the increase of navigation errors. Therefore, the accuracy and reliability of the underwater integrated navigation system can be substantially improved.
## 3 System Design
### Telemetry Decoding  
When tracking very weak Galileo E1b/c signals, the receiver may not be able to correctly demodulate the telemetry messages due to the presence of bit errors [36; 37]. However, demodulating the received telemetry messages is needed to obtain the TOW of the received messages. The TOW is used to compute the GNSS basic measurements and the navigation solutions. For this reason, the GNSS-SDR software telemetry decoder was upgraded to estimate the TOW of the received Galileo E1b/c messages using the Time of Arrival (TOA) of the telemetry synchronization patterns and the receiver's local clock.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Signal** & **Configuration** \\ \hline GPS L1 C/A & E, P, L \\ \hline Galileo E1b/c & VE, E, P, L, VL (Pilot Component) \\  & P (Data Component) \\ \hline GPS L5 & E, P, L (Pilot Component) \\  & P (Data Component) \\ \hline Galileo E5a & E, P, L (Pilot Component) \\  & P (Data Component) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Tracking multicorrelator configuration.  
The receiver performs TOW estimation in three steps: sync frame detection, sync frame confirmation, and TOW estimation. These steps are explained below.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 5 Simulation Results  
In this section, we evaluate the performance of our proposed 3D-SM with two existing protocols: DDRP-4 MS and AUV-PN. The simulation parameters are given in Table 3, where the field dimensions are 500 m \(\times\) 500 m \(\times\) 1000 m with 300 nodes that are randomly deployed in the field. The initial energy of each node is 70 J. The transmission radius of the MS is 70 m in each scheme. For a fair comparison, besides the working principles, in three protocols, we consider the same network dimensions, number of nodes, initial energy and the transmission range of nodes. DDRP has multi-hop transmissions for data delivery to an MS, which is collecting data from the nodes and has a random trajectory. In 3D-SM, the MS collects data from nodes directly, and it moves on a straight trajectory in the field, as shown in Figure 3. In those regions where the MS is far away from the nodes, CNs are playing the role of helping to avoid multi-hop communication, and they collect data from the nodes and send them to the MS at the minimum distance. We use the Linprog solver to solve the linear programming model in Section 4.1.  
Definitions of the performance parameters are given below:  
1. _NLT_: The definition of the network lifetime varies; the authors in [21] state it as the duration till the death of the first node, and the authors in [4] define it as the duration till all nodes die. Similarly, other definitions of network lifetime exist that indicate different percentages of dead nodes while calculating the network lifetime. We stick with the definition of the authors in [21], because in balanced data transmissions, effective communication is only possible till the death of the first node.
2. _Dropped packets_: This is the number of data packets dropped during the transmissions from the node to the sink due to a bad link status. Its unit is the number of packets per second.
3. _Throughput_: The throughput of the network is defined as the number of successfully received packets at the sink. It is also measured as the number of packets per second.
4. _End-to-end delay_: This is defined as the average time taken by a data packet to reach the sink from the source node. Its unit is also seconds.
5. _TL_: This is the accumulative decrease in the intensity of waveform energy as a wave propagates from the source to the destination. In the underwater environment, TL occurs due to attenuation and spreading of the data packet from the source node to the sink. It is measured in dB.
6. _Path loss_: The signal attenuation between a transmit and a sink antenna as a function of the propagation distance is defined as path loss. It is also measured in dB.
# Prediction of Phytochemical Constituents in Cayenne Pepper Using MIR and NIR Spectroscopy
## 3 Results
### NIR and MIR Spectra  
Figure 1 shows the unprocessed NIR spectra of the chili samples. Peaks were located at approximately 1450, 1190, and 970 nm, similar to that reported by [PERSON] et al. [20]. These absorption bands can be attributed primarily to the OH 1 st overtone, CH 2 nd overtone stretch, and OH 2 nd overtone, respectively [34; 35].  
The MIR spectra of the dried chili powder showed a greater number of peaks (Figure 2), with major peaks corresponding to OH from moisture (approx. 3300 cm\({}^{-1}\)), CH\({}_{3}\), and CH\({}_{2}\) groups such as those found in fatty acids (3000-2800 cm\({}^{-1}\)), C=O (1750 cm\({}^{-1}\)), amide and cellulose groups (1000 cm\({}^{-1}\)) [36; 37].  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **DM Content (\%)** & \begin{tabular}{c} **TPC** \\ **(mg GAE/100 g)** \\ \end{tabular} & \
\begin{tabular}{c} **FRAP** \\ **(mg TE/100 g)** \\ \end{tabular} &
\begin{tabular}{c} **CUPRAC** \\ **(mg TE/100 g)** \\ \end{tabular} \\ \hline Minimum & 8.39 & 898 & 168 & 1256 \\ Maximum & 12.64 & 2617 & 615 & 3138 \\ Mean & 10.52 & 1300 & 296 & 1917 \\ SD & 0.86 & 267 & 70 & 379 \\ CV (\%) & 4.82 & 20.54 & 23.65 & 19.77 \\ \hline \end{tabular}  
SD = Standard deviation; CV = coefficient of variation.  
\end{table}
Table 2: Range, mean standard deviation, and coefficient of variation for the DM, TP, FRAP, and CUPRAC datasets.  
Figure 1: The NIR spectra of the chili samples, between 908–1676 nm were obtained using the handheld microNIR probe.
# Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm
## 3 Cost-Sensitive Random Forest Algorithm
### Algorithmic Enhancement  
In the face of class imbalance within intrusion-detection datasets, traditional Random Forest algorithms employ decision trees as base classifiers, with node splits based on randomly selected attributes. Typically, the chosen splitting criterion involves calculating the minimal impurity of child nodes after the split. For imbalanced datasets, the class distribution tends to concentrate within the category with lower impurity, leading to misclassification of the minority class. In the realm of intrusion detection, malicious attacks represent the minority class, while normal behavior constitutes the majority class. In the context of network traffic detection, conventional Random Forest models tend to detect normal behavior while overlooking malicious attacks. The inability to effectively detect malicious behavior in the minority class could pose significant security threats to computer systems.  
Therefore, improving the generation process of each base classifier is pivotal in classification work. By considering the cost associated with different classes and embedding cost-sensitive thinking into the training process of each base classifier, we utilize class weights to calculate the weighted minimum Gini coefficient for selecting the optimal split point. Such a cost-sensitive approach better accounts for cost disparities between different classes, resulting in more accurate classification outcomes. This transformation of the expression ensures a focus on the integration of cost sensitivity into the training process of each base classifier, ultimately leading to improved classification accuracy. The expression of \(Gini(t)\) is transformed into the following:  
\[Gini_{\cos t}(t)=1-\sum\limits_{i=1}^{c}\left[p(i|t)\cot s(i,j)\right]^{2}\]  
where, \(i\) and \(j\) represent categories; \(C\) represents the number of categories.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Mimo
#### 3.4.4 Adaptive ANN Equalizer  
Spatial complexity is a major obstacle for machine learning in VLC MIMO applications. In SR-MIMO-VLC, crosstalk between channels will lead to a significant increase in the size of the data-driven neural network [73]. The traditional MIMO-LMS can obtain the channel matrix more efficiently, although it cannot take into account the nonlinear impairment. Two adaptive ANN (AANN) equalizers are proposed to combine ANN and MIMO-LMS with an adaptive parameter [76]. The spatial complexity of the AANN can be less than 10% of MIMO-MBNN.  
An adaptive algorithm determines different algorithmic processes by the power ratio of two transmitted signals. When the power ratio is close to one, the SISO ANN can be used to equalize the two signals. However, when the power ratio is out of balance, it is necessary to use MIMO-ANN algorithms, such as L-DBMLP-L (combination of MIMO-LMS, DBMLP, and MIMO-LMS) or one hidden layer MBNN (OHL MBNN). This constitutes two AANN algorithms, namely ADP L/DBMLP-L and ADP MIMO ANN. The proposed AANN can achieve the same transmission performance, but with lower spatial complexity [76].
## 3 Research Results and Discussion
### Tensile Fracture Surfaces  
Figure 9 displays the tensile fracture surface images of the AZ91 Mg and AA6063 Al specimens and the SEM images of the Cr/CrN/TiCrN/TiCrCN multilayer nitride coating after the tensile test fracture damage. Tensile test SEM damage images show that the tensile crack initiation starts from the coating surface in the nitride-coated AZ91 alloy. Tensile crack propagation can be characterized by more randomly oriented line-like features combined with tear ridges and secondary cracks along the interfaces. In AA6063, cracks started from the coating surface, and damage occurred with their spread toward the center of the Al layer after the coating was finished. It is thought that the reason for this is that the adhesion between the coating and the AA6063 substrate is low, and the coating peels off from the surface very quickly, initiating and accelerating the crack.  
Figure 8: Specimens’ tensile curves.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multicolumn{3}{c}{**AA6063 Base**} & \multicolumn{3}{c}{**AA6063 Coated**} & \multicolumn{3}{c}{**AZ91 Base**} & \multicolumn{3}{c}{**AZ91 Coated**} \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Average \\ Deviation \\ \end{tabular} } & **Stress** & **Strain** & **Stress** & **Strain** & **Stress** & **Strain** & **Stress** & **Strain** \\  & **(Mpa)** & **(\%)** & **(Mpa)** & **(\%)** & **(Mpa)** & **(\%)** & **(Mpa)** & **(\%)** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Average \\ Deviation \\ \end{tabular} } & 129.35 & 8.18 & 118.16 & 7.50 & 137.89 & 12.83 & 139.65 & 12.74 \\  & 5.12 & 0.35 & 3.52 & 0.14 & 4.41 & 0.45 & 4.04 & 0.36 \\ \hline \end{tabular}
\end{table}
Table 3: Summary table of the specimens’ tensile curves.
## 1 Introduction
### Empirical Prediction Models  
Because of the importance of assessing the magnitude of the air blast wave, a lot of prediction models to determine explosion parameters, mainly overpressure, have been developed. These can be based on empirical (or analytical), semiempirical or numerical methods. Empirical methods are essentially correlations with experimental data. Most of these approaches are limited by the range of experiments carried out. The accuracy of all empirical correlations decreases with distance to the source of the explosion.  
The use of empirical laws has been extensively studied and has been applied in various recommendations, mostly proposed by military authorities. After the first attempt due to [PERSON] [14], several methods were proposed [3; 4; 5; 6; 7], and due to the relevance of the topic recently, works about this topic have been published [15; 16; 17].  
In the field of mining and civil engineering, several empirical models have also been proposed to estimate the magnitude of the air blast overpressure as for example [18; 19; 20].  
In many cases, the air blast wave is given as a function of the scaled distance \(Z\) (in m/kg\({}^{1/3}\)):  
\[Z=\frac{R}{W^{1/3}} \tag{1}\]  
\(R\) (m) is the distance from the explosion to the measurement point, and \(W\) (kg) is the amount of explosive detonated.  
In order to be able to characterize the wave generated by any explosive substance and to be able to compare them with each other to assess their harmful effects after a detonation in the open air, it was important to establish a base explosive. The selected explosive was the Trinitrotoluene (TNT), which has well-known explosive properties. The TNT-equivalent mass is the mass of Trinitrotoluene (TNT) that would release an amount of energy equal to the explosive charge in question. If there is a mass \(W\) of a given explosive with an explosion heat \(Q\), the equivalent TNT mass \(W_{eq}\) is:  
\[W_{eq}=W\ \frac{Q}{Q_{eq}} \tag{2}\]  
where \(Q_{eq}\) is the explosion heat of TNT \(Q_{eq}=4520\) kJ/kg.  
The relationship (2) is widely accepted for blast-resistant design. It is proposed in documents taken as a reference or guides, such as UFC 3-340-02 [21] or EUR 2645 EN [22], which allow to determine the incident and reflected overpressures and impulses of a spherical or hemispherical TNT explosion.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 5 Discussion
### Ephemeral Identifiers Improve Inter-Flow Privacy
These defences aim to protect _location_ privacy (or hide the topological location), and do not try to protect _identity_ privacy, so should not be used to do so. Also, NAT, CGNAT, VPNs, and Tor all rely on additional entities in the network being trusted and being sufficiently protected from compromise. Finally, as eNID values are pseudo-random values, their use in ILNP has a number of advantages over cryptographic techniques (such as tunnelling) to prevent flow correlation based on source address values:  
* they have a much lower computational overhead than cryptographic techniques, so can be used on a range of devices and systems;
* they are not subject to national restrictions that might exist for cryptographic algorithms, so implementations can be used globally, including sharing of code;
* the eNID values can be generated independently on the node to confine the scope of trust, but there is the potential for further security benefits if a wider scope of trust is possible (see Sections 5.5 and 5.6).
## 3 Novel Rotating Motion Mechanism
### Universal joints Drive
#### 3.7.3 New Type of Fully Compliant Universal Joint
In terms of material selection, developing new high-strength, high toughness, and self-healing materials can effectively improve the load-bearing capacity, fatigue resistance, and service life of universal joints; In terms of design methods, the use of topology optimisation techniques can further optimise the structure of universal joints, reduce stress concentration, and improve transmission efficiency and accuracy; In terms of manufacturing processes, high precision additive manufacturing technology and multi material composite manufacturing technology will continue to develop, achieving more complex and precise structural manufacturing to meet the needs of miniaturisation and integration; The integration of intelligent technology, such as sensors and smart control algorithms, can achieve real-time monitoring and thoughtful regulation of the operating status of universal joints, improving their adaptability and reliability in complex working conditions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types** & **Advantages** & **Disadvantages** & **Applications** \\ \hline Steel flexible universal joint [142] & Balancing elasticity and strength, no need for lubrication, lightweight and compact, long lifespan & Elastic deformation may lead to a decrease in transmission accuracy & Compact transmission, high reliability joint \\ Anti-buckling flexible universal joint [143] & High stability, high precision of dual-axis rotation, no need for lubrication & Complex structure, high cost, and limited load-bearing capacity by tension plates & Optical positioning platform/surgical robot joint, continuum robot flexible arm segment \\ Fully compliant universal joint [144] & A single-piece structure does not require assembly, has an approximate constant transmission speed, and is flexible. & High torque/large angle can easily cause stress concentration, and the polypropylene material is weak & Small robot/drone joints, lightweight transmission of medical equipment, and noise-sensitive scenes \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Comparison of universal joints.
# Damage Identification Based on Adding Mass for Liquid-Solid Coupling Structures
## 5 Damage Identification Experiment of a Liquid Storage Tank
### Experimental Model, Devices and Process  
The experimental model of tank is shown in Figure 15: diameter is 0.4 m, height is 0.8 m and thickness is 6 mm. The tank wall is cut from a cylindrical steel tube. The tank wall and bottom plate are welded together, whereas the bottom plate and the base are anchored by bolts, as shown in Figure 16. In each scenario, the same area of the sidewall is selected as the area to be identified. The area to be  
Figure 14: Multiple damage recognition results at different immersion depths.  
Figure 13: Experimental and computed values of the first four natural frequencies of the cantilever beam with double damage.  
identified is divided into 21 substructures (see Figure 15) and recorded by the serial number \(S_{V,H}\), where V and H represent the vertical and horizontal numbers of the substructure, respectively.  
The experimental devices and steel are the same as those of the submerged cantilever.  
The experimental process is divided into four parts. Firstly, add water to a certain height in the tank; secondly, install the acceleration sensor on the outside of the tank, taking substructure \(S_{4,1}\) as an example (see Figure 17); thirdly, add mass to the substructure \(S_{1,1}\) by a magnet and knock the tank to obtain acceleration signals; Finally, move the mass to other substructures and repeat step 3. The experiment is not completed until all substructures are added with mass.
# Probabilistic Mixture Model-Based Spectral Unmixing
## 4 Results
Figure 4 presents a three-way comparison of the reconstruction error for endmember coordinates under HMC, VI and N-FINDR. As is evident in the figures, HMC and N-FINDR generate the highest- and lowest-quality reconstructions, respectively. Further, mean field VI leads to reconstructions that lie between HMC and N-FINDR. However, VI provides an approximately 100-fold advantage in computation time over HMC, on average. Figure 2: Comparison of endmember coordinates extracted using N-FINDR vs. through sampling of the model posterior using HMC and the no-U turn (NUTS) sampler in Pyro [23]. The panels (**a,b**) on the top row represent the results of endmember extraction on datasets generated under different conditions of the concentration hyperparameter \(\alpha_{1}\): small and large, respectively, coupled with a small noise scale parameter (\(\sigma\)). The bottom row panels (**c,d**) depict inference results for large noise scale parameter (\(\sigma\)), again contrasting between small and large \(\alpha_{1}\).