esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Enhancement of the BST Content  
Based on the experimental data presented in previous subsections, the optimal processing parameters for achieving the highest ZT were determined as follows: temperature of 80 \({}^{\circ}\)C, pressure of 2 tons (250 MPa) and processing time of 75 min. In order to investigate whether a further increase in BST content could result in a higher ZT value, additional specimens were prepared with a 40 wt.% and 45 wt.% BST content by applying the above-mentioned processing parameters. Figure 9 summarizes the thermoelectric properties (i.e., S, k and \(\sigma\)), along with the figure of merit ZT for all samples (in which the BST content varied from 0 to 45% wt), as a function of the measurement temperature. As expected, the Seebeck coefficient, S, shows a clear increase with the increase in BST content and measurement temperature. The electrical conductivity presents similar trends for all samples, reaching a maximum value for temperatures between 363 and 383 K, whereas a distinctive decrease can be observed at higher temperatures. In terms of electrical conductivity, this appears to increase within the range of 383-403 K upon increasing the BST content up to 30 wt.%, whereas for higher loadings (i.e., 40 wt.% and 45 wt.%), a decrease in \(\sigma\) can be observed. This phenomenon could be related to the quality of the BST particle dispersion and, consequently, the existing contact area between the organic and inorganic counterparts at higher BST loadings. The thermal conductivity remains relatively unaffected with respect to the BST content, while the figure of merit, ZT, increases upon increasing the BST content and the measurement temperature, exhibiting a maximum value of 3.24 \(\times\) 10\({}^{-3}\) at \(\sim\)400 K for 45 wt.% BST loading. In conclusion, although the Seebeck coefficient is improved by further increasing the BST content, due to the decrease in electrical conductivity, the overall thermoelectric performance shows no significant improvement.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **S (\%/K)** & **\(\sigma\) (S/cm)** & **\(\kappa\) (W/Km)** & **ZT (\(\times\)10\({}^{-3}\))** \\ \hline
30-T80-P2-D75 & 39.67 & 12.937 & 0.2800 & 2.930 \\ \hline
30-T80-P2-D15 & 38.48 & 11.276 & 0.2671 & 2.520 \\ \hline
20-T100-P2-D15 & 29.19 & 11.875 & 0.2896 & 1.410 \\ \hline
20-T100-P2-D75 & 28.02 & 10.324 & 0.2715 & 1.200 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimum thermoelectric properties recorded at 130 \({}^{\circ}\)C for the PANI-BST composites (20 and 30% wt. BST content).
# Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform
## 5 Experimental Evaluation and Results
### Doppler Shift Measurements  
#### 5.2.1 Ground Experiment  
The experiment was done on the 6 November 2020 in Montreal, Canada. During the experiment, one Orbcomm (Orbcomm FM-113) and one Iridium-NEXT (Iridium 141) satellite were visible in the receiver's sight. The TLE files of the observed satellites were downloaded from the American NORAD website on the day of the experiment. To detect the overhead satellites, we implemented a real-time power spectral density (PSD) viewer on MATLAB for both RTL-SDR and BladeRF software-defined radios. Our latest experiments showed that in each time at least one Iridium satellite was visible above the Montreal city and its vicinity. As a result of that, we started the experiment when an Orbcomm satellite was detected on the PSD viewer. Figure 6 shows a geographical view of the overhead satellite as well as the location of the airport. It also presents the estimated Doppler shifts of the Iridium satellite in three different channels (quaternary, primary, and ring alert). Figure 7 demonstrates the PSD analysis of the Orbcomm channel. The receiver detected two frequency peaks with magnitudes of more than \(-38\) dB. These frequencies were \(137.6626\) and \(137.80\) MHz, which are the dual transmitted frequencies of the Orbcomm space vehicle (SV) FM-113. Later, the Doppler shift of each satellite was tracked using the MC-SDR for the entire trajectory. Although the Doppler shift of the Orbcomm on its two frequencies was obtained, just that of the higher frequency (\(137.80\) MHz) was considered for this study. It should be mentioned that the receiver showed the same Doppler behaviors for both carrier frequencies as they were propagated from the same satellite origin. For the Iridium channels, the Doppler shifts tracked in all the three channels had the same behavior. However, it can be seen that the Doppler shifts given by the quaternary messaging channel showed better quality with more dense measurements, which is more suitable for the EKF-based alignment method.  
To validate the obtained Doppler shifts, we used the LASESENA's Doppler simulator based on the simplified perturbation (SGP, SGP4, and SGP8) model. In the simulation, regarding the fact that the vehicle's velocity related to that of the satellite was a small value, the satellite's velocity was considered as a relative velocity between the satellite and the receiver. It could provide us with an estimation of the Doppler shift from each satellite during the experiment. The estimated Doppler shifts for the Orbcomm FM 113 and the Iridium 141 satellites were continuous and burst-based, respectively. Figure 8 shows the comparison of the simulated and the estimated Doppler-shift curves. It was obvious that the receiver could extract the Doppler shifts from the LEO satellites with high accuracy. The accuracy of the receiver for different channels and constellations were previously discussed in [6].  
Figure 5: The light aircraft and the equipment used in the flight experiment.
# Sound Radiation of Aerodynamically Excited Flat Plates into Cavities
## 3 Materials and Methods
### Experimental Setup
#### 3.1.1 Flow Field  
The measurements were performed in the aeroacoustic wind tunnel of the Chair of Sensor Technology at the Friedrich-Alexander University Erlangen-Nurnberg. The measuring section is located in an anechoic room with the dimensions \(9\,\mathrm{m}\times 6\,\mathrm{m}\times 3.6\,\mathrm{m}\). The general test setup is installed at a massive base frame built from aluminium profiles to ensure the structural stiffness. To determine the pressure coefficient at the plate surface, 64 holes with the diameter of \(0.3\,\mathrm{mm}\) are drilled on the flow-exposed surface of the rigid plate, whereas on the other side a treated hole is drilled and tapped to connect pressure hoses to 4 Pressure Systems 16 TC/DTC pressure scanners from the company Pressure Systems (Melbourne, Australia). The pressure holes are arranged in a straight streamwise line with a constant spacing from one another. The second measurement setup is shown in Figure 2. The replaced continuous rigid plate is equipped with a square opening in which a manufactured aluminium frame is inserted. The different flexible plates with the thickness \(t=1,3\) and \(5\,\mathrm{mm}\) are each bonded to the frame by an adhesive to avoid possible tensions in contrast to bolting. After installation of the frame, the plane surface of the flexible plate is arranged at the same height as the solid plate. The position of the flexible plate in relation to the nozzle outlet is set so that the entire surface of the plate structure is located in the core area of the free-stream at any time. This substructure ensures that the only way that sound is transferred into the cavity is through the flexible plate.  
Figure 2: Schematic sketch of the experimental setup.
## 3 Results and Discussion
### Measurement Results of the Classroom
#### 3.2.1 Results of the First Method  
Measurement results in seconds for the first method and for 72 measurements (500 Hz) made in the teaching room are described in Section 2.2 (Figure 11).  
The result together with the standard uncertainty for particular frequencies is \(T_{500}=2.19\pm 0.326\) s.
## 3 Results and Discussion
### Flow Characteristics Under Different Storage Pressures
#### 3.2.3 Analysis of BOG Generation  
Figure 11 illustrates the variation in the amount of boil-off gas (BOG) generated under different storage pressures. It is evident that under low-pressure conditions, the amount of BOG generated is relatively high, whereas under high-pressure conditions, the amount is significantly reduced. As the storage pressure increases, the amount of BOG generated gradually decreases, and the rate of BOG generation slows down noticeably beyond 1.5 MPa. This trend is closely related to the thermophysical properties of liquid carbon dioxide at different states. With increasing storage pressure, the boiling point of the fluid rises, requiring more heat for evaporation, which consequently reduces the amount of BOG generated. Additionally, as the pressure increases, both the flow and heat transfer performance of the fluid decline, resulting in poorer heat transfer efficiency and a corresponding reduction in BOG generation.  
In practical terms, low-pressure storage conditions are easier to achieve; however, they impose higher demands on BOG risk management due to the increased generation of BOG. Conversely, high-pressure storage conditions generate less BOG but require more stringent equipment specifications to withstand the elevated pressures.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 DBM 3D Building Mode  
6-DOF Six Degrees of Freedom  
ASF Alternate Sequential Filter  
CDF Cumulative Distribution Function  
DGPS Differential Global Positioning System  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Estimator** & **2D rms Error (m)** & **2D Maximum Error (m)** \\ \hline
**GNSS-KF** & 39.8 & 113.2 \\
**LC GNSS/vision** & 56.3 & 402.7 \\
**TC GNSS/vision** & 14.5 & 61.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the considered trajectory.
## 2 System Model  
This section introduces the system models used throughout the paper. It first presents a single link scenario (with a direct channel between the transmitter and receiver) describing the channel, modulation, ECC, and HARQ schemes. Then it extends this scenario to the dual-hop relay system depicted in Figure 1, where the base station (BS) can only reach the end user through an intermediate relay station (RS), and to a multi-user scenario where a single transmitter (possibly the relay) is communicating with multiple recipients. All the links in the relay and multi-user scenarios follow the same model as that in the single link scenario.
###### Abstract
These three dimensions correspond to the specific aspects of the whole document, each sentence, and the entity for sentiment polarity classification. The challenges of sentiment analysis of e-commerce product reviews lie in dimension mapping, disambiguation of sentiment words, and polysemy of words. The dimension mapping problem is the problem of mapping the review text to the correct dimension. The sentiment word disambiguation problem refers to the situation where the sentiment word in the review text is connected with two or more dimensions. In the word polysemy problem, for example, the word \"Xiaomi\" in appliances is a brand name, but as a food, it means the grain. In order to solve the above problems and to improve the effectiveness of the sentiment analysis model in Chinese e-commerce product review texts, this paper combines the ERNIE word embedding model and Bidirectional Long Short-Term Memory (BiLSTM) and adds an attention mechanism (Att) [10] to the network structure. First, the ERNIE word embedding model is used to obtain the representation of the dynamic word vector of the review text. Then, BiLSTM is used combined with the attention mechanism to construct a sentiment analysis model called ERNIE-BiLSTM-Att (EBLA). The main contributions of this paper are as follows:  
1. We target sentiment analysis in the field of Chinese e-commerce product reviews. Based on the ERNIE word embedding model, BiLSTM model, and attention mechanism, we proposed a Chinese e-commerce review sentiment analysis model EBLA, which was tested on the real crawled Jingdong appliance review dataset and achieved good results, the model outperforms many classic deep learning models proposed by other researchers, can provide more accurate reference for consumers and merchants. It has strong practicability in the field of sentiment analysis of Chinese e-commerce reviews. 2. Dynamic word embedding model ERNIE was used to obtain the representation of the dynamic word vector of the review text, which effectively solves the polysemy problem in Chinese text. In addition, it addresses the challenge of dimension mapping in the sentiment analysis of e-commerce product reviews. 3. Since BiLSTM can fully capture contextual semantic information from the front and rear directions, it was applied to the sentiment analysis problem to solve the sentiment word disambiguation. The rest of this paper is as follows: Section 2 introduces related work of this research; Section 3 introduces the composition of the EBLA model; Section 4 describes the experimental process and analysis, and concludes. Section 5 discusses and summarizes these experiments.
## 3 Experimental Results  
### Experimental Setup  
To analyze the effectiveness of the system, the tilt angle evaluation method presented in [13] is analyzed by attaching the manipulator to a single pivot point, as in Figure 14. The tilt angle corresponds to the CoG shift, as the CoG is directly under the apparatus pivot point [13]. During the manipulator's operation, an MPU-6050 gyro and accelerometer sensor is used to measure the system's tilt angle [24]. All data is collected directly through the Arduino microcontroller serial port.  
To validate the effectiveness of the CoG compensation system, the microcontroller is programmed to run through a series of fixed points, utilizing the inverse kinematic control  
Figure 14: Experimental setup: cable aerial manipulator with a CoG compensation mechanism.  
mode. The experiment is repeated twice, once with and once without the battery slider active in the CoG compensation system.  
The discrete motion controller with sample time \(T=1\;s\) is implemented on the microcontroller. The motor input is the duty cycle, which is discretized for sample \(k\) by  
\[\begin{split} u[\text{kT}]&=K_{p}e[\text{kT}]+K_{i} \sum_{j=0}^{k}e[jT]+K_{d}(\Delta x_{d}-\Delta x[kT]),\\ e[kT]&=x_{d}-x[kT],\Delta x[kT]=x[kT]-x[(k-1)T]\;, \;\Delta x_{d}=0\end{split} \tag{12}\]  
The controller gains are obtained by manual tuning, with values \(K_{p}=8,\;K_{d}=0.01\), and \(K_{i}=1.5\).  
This experimental setup is intended to emulate a hovering scenario with moving arms, in which the UAV position is assumed to be stable.
## 3 Results
### Structure Prediction, Refinement and Validation
#### 3.5.2 Ab Initio Structure Prediction Using I-TASSER, RoseTTAFold, and AlphaFold  
After that, the tertiary structure of VLP was generated using two online servers: iTASSER and RoseTTAFold. VLP 3D structure prediction result from the I-TASSER server shows four predicted models. The models were predicted based on C-score, Exp. TM-Score, Exp. RMSD, No. of decoys, and Cluster density. The best score model is shown in Figure 1c. VLP structure prediction results from the ROBETTA Baker server showed five predicted models using RoseTTAFold, and model 1 (Figure 1e) was prioritized based on its highest C-score. The AlphaFold-predicted structure of VLP in _N. tabacum_ (Identifier: AF-A0A1S4 CXB2-F1) is shown in Figure 1g.  
These three structures, obtained from the I-TASSER server, ROBETTA Baker server, and AlphaFold, were subjected to structure validation using a Ramachandran plot to determine the best-predicted structure. The iTASSER model exhibited 54.5% of residues in the most favored regions (Figure 1d). In contrast, the RoseTTAFold model displayed superior structural quality with 83.2% of residues in these regions (Figure 1f). In comparison, the AlphaFold model showed 50.0% of residues in the most favored areas of the Ramachandran plots (Figure 1h).
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 3 Adaptive Bat Algorithm Optimizes Observation Matrix
### The Flow Chart of Adaptive Bat Algorithm Optimal Observation Matrix  
In the previous section, we proposed an adaptive bat algorithm to optimize the observation matrix. Firstly, the algorithm parameters and reconstructed signal are initialized. Secondly, the compression projection is carried out based on the relevant principles of compressed sensing, and signal reconstruction is realized. Thirdly, according to the search method of our adaptive bat algorithm, the optimization radius and new position are constantly updated. Finally, the optimal global solution is obtained. In our model, the observation matrix is optimized and the reconstruction error value is updated by generating the optimal position and the optimal fitness value. The process of the adaptive bat algorithm optimal observation matrix is shown in Figure 2.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 2 Background
### Indoor Search and Rescue (SAR) Operations for Swarms of Drones
#### 2.1.1 Swarm (Multi-Agent) SAR Operations  
Of course, the use of drones is not restricted to disasters, but as such events often result in partial or total loss of existing communication and data-collection infrastructure and cause substantial damage to buildings, drones have been deployed to provide situational awareness (SA) [18], deliver (medical) supplies [12], and monitor human personnel in the field [14], e.g., fire fighters [12]. For example, the AceCore NEO drone, shown in Figure 1 (center, right) is being used in trials for systems intended for use by the Dutch police forces.  
More recently, the drop in cost for units, as well as the operation thereof, has made the operation of multiple drones, acting as a single larger collective (often called a _stwarm_[23]), a realistic scenario for, e.g., aerial tracking [17]. The authors of [28] investigated SAR operations with ground based robot swarms using actual devices, but while the topic of performing SAR operations using autonomous agents has been an area of widespread research [20], most of this work is still rather general. Due to this, there is still room for improvement in terms of optimizing the search algorithm for specific metrics, such as the speeding up of the rescue. Due to the large potential for unknowns and uncertainties, traditional deterministic approaches are at a disadvantage against heuristics.  
Considering temporal and spatial uncertainties and ambiguities in a disaster struck search space (and not the least because of the likelihood of continuing changes in the environment), heuristic techniques, rather than classic, deterministic ones are worth exploring in expectation of a better performance in such unstable environments [29].
# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 3 System Design
### Tree Construction Algorithm
#### 3.5.3 Joining Process and Path Delay Estimation  
The JOIN message includes an LS Table to record link information, storing the network addresses (NAs) of nodes N\({}_{\text{i}}\) probed by the JOIN operation, along with the corresponding accumulated path cost (N\({}_{\text{i}}\)_Cost_) and delay (N\({}_{\text{i}}\)_Delay_) from each N\({}_{\text{i}}\) to the Receiver Node.  
Upon receiving a JOIN message, nodes in the network calculate the accumulated delay and cost from their node to the Receiver Node using the LS Table, subsequently appending this information to the table. The specific construction process of the LS Table is depicted in Figure 7. Upon receiving the JOIN message forwarded by node N\({}_{\text{1}}\), the non-tree node N\({}_{\text{2}}\) parses the LS Table to retrieve the table length (\(L\)) and information about the tail node (N\({}_{\text{1}}\)). Subsequently, N\({}_{\text{2}}\) looks up N\({}_{\text{1}}\) in the NSIB and calculates the measured values \(D_{\textit{meas}}\) and \(C_{\textit{meas}}\) for delay and cost using Equations (4) and (5):  
\[D_{\textit{meas}}=\begin{cases}NSIB[\text{N}_{\text{1}}].Delay,&L=0\\ NSIB[\text{N}_{\text{1}}].Delay+\text{N}_{\text{1}}.Delay,&L>0\end{cases} \tag{4}\]  
\[C_{\textit{meas}}=\begin{cases}NSIB[\text{N}_{\text{1}}].Cost,&L=0\\ NSIB[\text{N}_{\text{1}}].Cost+\text{N}_{\text{1}}.Cost,&L>0\end{cases} \tag{5}\]  
If N\({}_{\text{2}}\) is a non-tree node, append its own NA, \(D_{\textit{meas}}\), and \(C_{\textit{meas}}\) to the LS Table. Query the LCT based on the recorded NA\({}_{\text{Branch}}\) in the Branch Node field. Then, forward the configured JOIN message to LCT[NA\({}_{\text{Branch}}\)]. Gateway according to the routing entry in the LCT.  
Figure 7: Header format of multicast signaling messages.  
If TNspec is RN, the complete link from RN to R must be detected during the JOIN message propagation process, as illustrated in Figure 8. Otherwise, only the link between TN and R needs to be probed.  
If N2 is a tree node, calculate the minimum possible delay using Equation (6), where _LDT_[i]._Delay_ represents the delay recorded in _LDT_ for reaching node i:  
\[Delay_{min}=D_{meas}+LDT[\text{N}_{2}].Delay \tag{6}\]  
If _Delay\({}_{min}\)_ exceeds the delay required by QoS, the path constructed from this node does not meet the delay constraint. Thus, the JOIN message must be forwarded to the predecessor node recorded in the local MFT[MID]. On the other hand, if the delay is within the constraint, the grafting process can proceed accordingly.
## 4 Performance Evaluation and Discussion
### Secrecy Capacity
\[I(X;Z)=1+P_{ez}\log_{2}(P_{ez})+(1-P_{ez})\log_{2}(1-P_{ez})=C_{z} \tag{14}\]  
If the information from the sender is completely received, \(P_{ey}=0\). By substituting \(P_{ey}=0\) into Equation (13) the mutual information \(I(X;Y)\) becomes 1. If the information from the sender cannot be received at all and \(X\) and \(Y\) are independent with \(P(x_{1})=P(x_{2})=1/2\) and \(P(y_{1})=P(y_{2})=1/2\), \(P_{ey}=0.5\). By substituting \(P_{ey}=0.5\) into Equation (13) the mutual information \(I(X;Y)\) becomes 0. \(I(X;Z)\) can be calculated in the same way as \(I(X;Y)\). From Equations (13) and (14), it can be proven that when the information from the sender can be completely received, the mutual information becomes 1, and when the information cannot be received at all, the mutual information becomes 0. We measured the bit error ratio (BER) by simulation under the above assumption.