esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Acoustic Flow Field Characteristics of Key Components of High-Speed Train Bogies
## 3 Analysis of the Results
### Influence of Train Speed on the Sound Field Distribution
#### 3.1.3 Distribution of Bogie Axle Box Clouds  
Figure 11 illustrates the sound field distribution in the bogie area at a speed of 30 km/h. The sound pressure level distribution at a frequency of 100 Hz was mainly spread outwards in a circle with the sound source as the center. As the frequency increases, the longitudinal variation of the cloud distribution became significantly greater than the lateral variation.  
Figure 10: Line chart of sound pressure level of bogie gearboxes change, with different frequencies.  
Figure 9: Sound field distribution of bogie gearboxes at a speed of 30 km/h, with different frequencies.  
In terms of numerical values, at a speed of 30 km/h, the maximum value of the sound pressure level around the bogie increased with increasing frequency, from 120.9 dB at 100 Hz to 122.1 dB at 250 Hz, resulting in an increase of 1.2 dB. Meanwhile, the minimum value of the sound pressure level continuously decreased, from 89.7 dB to 64.5 dB, representing a total decrease of approximately 25.2 dB.  
In order to more intuitively observe the influence of vehicle speed on the sound field distribution of the bogie, the corresponding sound pressure level magnitude at each speed is plotted as a line graph, as shown in Figure 12. From the graph, it can be concluded that when the sound source frequency is between 50 Hz and 200 Hz, the gradient of the minimum value of the sound pressure level changes almost equally with the increase in the vehicle speed, and the change is not significant. Therefore, it can be inferred that the flow field generated by the vehicle speed exerts little influence on the sound field distribution around the bogie in this frequency range.  
However, after 200 Hz, the values start to change significantly, especially when the frequency reaches 350 Hz, the gradient of the minimum value of the sound pressure level reaches a maximum of 19 dB.  
By comparing the distribution of the sound pressure level cloud diagrams for the sound source located in the motor, gearbox, and axle box, it can be concluded that the maximum value of the sound pressure value for all three is located below the sound source, and that the lateral change in the cloud diagram is significantly larger than the longitudinal change, which may be due to the introduction of the flow field, which leads to the change in the propagation of the sound wave in the direction of the flow field. When the frequency  
Figure 11: Sound field distribution of bogie axle box at a speed of 30 km/h, with different frequencies.  
magnitude is the same, in regard to changing the vehicle speed, the frequency bands mainly affected by the motor and the axle box are after 200 Hz, and for the motor, after 150 Hz. In addition, the sound pressure level at the axle box is significantly lower than that at the motor and gearbox.
# Robust Visual Tracking via Patch Descriptor and Structural Local Sparse Representation
## 2 Related Work
### Patch-Based Tracking Methods  
Instead of learning a holistic appearance model, many patch-based tracking methods have been developed, in which the target is modeled as a small number of rectangular blocks. If the target is partially occluded and deformed, its remaining visible patches can still represent the target and provide reliable cues for tracking. In [27], [PERSON] et al. divided the target into multiple image patches to handle partial occlusion, and the integral image is used for feature extraction. [PERSON] and [PERSON] [28]presented a local patch-based appearance model to address tracking of a target whose geometric appearance is drastically changing over time. An adaptive structural local sparse appearance model was proposed in [9], which handles occlusion by pooling across the local patches. [PERSON] et al. [29] matched local patches from multiple frames jointly by considering their low-rank and sparse structure information, which can effectively handle patch appearance variations due to occlusion or noise. In order to effectively handle deformation and occlusion, [PERSON] et al. [30] designed a dynamic graph based tracker. They oversegment the target into several parts and then model the interactions between neighboring parts. Both the appearances of local parts and their relations are incorporated into a dynamic undirected graph. In [31], a patch-based tracking method with cascaded regression was proposed, which exploits the spatial constraints between patches by implicitly learning the shape and deformation parameters of the object in an online fashion. [PERSON] et al. [32] proposed a fragment-based tracking method with consideration of both temporal continuity and discontinuity information, which exploits both foreground and background information to detect the possible occlusion. With the kernelized CFs, [PERSON] et al. [33] proposed a patch-based tracking method to handle challenging situations, where they employ the reliable patch particles to represent the visual target. A similar method can be found in [34].  
Motivated by the aforementioned patch-based tracking methods, our approach also adopts a patch-based strategy to deal with challenging situations. However, different from existing methods, we design the patch descriptor to reflect the degree, to which each patch is contaminated with noise caused by appearance changes. The final tracked result is jointly determined by the patch descriptor and patch sparse coefficients.
## 3 The State-of-the-Art Review in Tunnel Blasting Evaluation
### 3D Reconstruction and Visualization
(2023) developed a novel SfMDLT method that integrates multiple data sources for detailed and accurate visualization of metro tunnels [26]. This holistic approach aids in operational planning and risk assessment during construction phases, ensuring that blasting operations are conducted safely and efficiently. In essence, the advancements in 3D reconstruction and visualization techniques have significantly improved our ability to assess the effects of tunnel blasting. These methodologies provide a detailed understanding of tunnel conditions, allowing for better decision-making and optimization of blasting practices. As technology continues to evolve, the integration of advanced imaging techniques with data analytics will undoubtedly enhance the precision and reliability of tunnel assessments, ultimately leading to safer and more efficient construction practices.
## 5 TB Vaccine Candidates in Clinical Trials
In addition, testing candidates in both uninfected and infected individuals in future trials could avoid risks of excluding potentially efficacious candidates in the pipeline against infection but which lack efficacy in IGRA-positive individuals [47]. New TB vaccine candidates today in clinical trials could be divided into whole cell vaccines and subunit vaccines. Whole cell vaccines include live mycobacterial vaccines derived from live attenuated _Mtb_ strains, _M. bovis_ BCG or recombinant BCG and killed mycobacterial vaccines that could be formulated from other saprophytic mycobacterial species or _Mtb_[48] (Figure 3). Subunit vaccines contain _Mtb_ antigens expressed as recombinant proteins that are formulated with different adjuvants or expressed by recombinant viral vectors that are used as vehicles for the administration of antigens [47]. Most of the current subunit vaccine candidates are vaccines with limited antigen diversity which are designed to enhance prior immunity mediated by T cells [49] (Figure 3). There is a total of 14 TB vaccine candidates in clinical trials today, seven are based on subunits and seven consist on whole-cell mycobacteria [47; 50] (Figure 3). Of the subunit candidates, four are mycobacterial fusion protein(s) in new adjuvant formulations (ID93: GLA-SE, H56.IC31, M72:ASO1E, GamTBVac) and three are based on recombinant live-attenuated or replication-deficient virus-vectored expressing one or more _Mtb_ proteins (Ad5 Ag85, ChadOx1.85/MVA85A, TB/FLU-04L). Of the whole-cell mycobacterial candidates, there are four candidates based on inactivated/extracts of mycobacteria (_M. vaccae_, MIP, DAR-901, RUTI) and three vaccine candidates are based on live attenuated mycobacteria, one is BCG revacination, since a positive signal has been seen in prevention studies of _Mtb_ infection [51], other is based on the use of recombinant BCG (VPM1002) and the third is derived from rationally attenuated _Mtb_ (MTBVAC).
# Vehicle Route Planning for Relief Item Distribution under Flood Uncertainty
## 2 Problem Formulation
### The Waiting Time  
When planning for disaster relief, minimizing distance or travel time is often prioritized. In emergencies such as flooding, the urgency to alleviate conditions makes waiting time a crucial dimension that cannot be overlooked. The longer individuals wait, the more stress and uncertainty they face, impacting their mental well-being and ability to recover from the situation. Figure 3 illustrates the difference between two routes of equal distance.  
Routes 1-2-3-4 have a total travel time of 45, the same as route 4-3-2-1. However, they are different when considering the total waiting time of both routes. Route 1-2-3-4 has a total waiting time of \(10+(10+15)+(10+15+20)=80\), while route 4-3-2-1 has a total waiting time of \(20+(20+15)+(20+15+10)=100\). Therefore, route 1-2-3-4 is the preferable choice.  
Figure 1: Example of delivery of items under the uncertain flooding situation.  
Figure 3: Difference in travel time and waiting time.  
Figure 2: An example of uncertain road conditions affecting vehicle speed.
## 3 Proposed Cumulative QoE Model
### Memory Effects
#### 3.2.4 Proposed Memory Weight  
As discussed in the previous sub-subsections, the effects of primacy, recency, forgetting behavior and repetition are significantly crucial for the evaluation of the cumulative QoE. Therefore, in the proposed cumulative QoE model, we introduce a novel _memory weight_ incorporating the effects of those factors to accurately assess the cumulative human perception during a streaming session. The proposed memory weight is represented by Equation (6). An example of time-varying memory weight is illustrated in Figure 5. In fact, Equation (6) is a linear combination effect of the above-mentioned memory factors obtained from Equations (3)-(5).  
\[w_{t}=\beta_{1}f_{P}(t)+\beta_{2}f_{R}(t)+\beta_{3}f_{RP}(t) \tag{6}\]  
where \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\), respectively, determine the contribution of primacy effect, recency effect and repetition to the memory weight.  
Figure 5 shows that when a rebuffering event occurs near the end of the session, the recency effect has a stronger effect on human perception. Therefore, in this period of time, the end user's QoE will drop dramatically. In addition, the forgetting rate of a specific interruption is also smaller than those of previous ones, determining the characteristics of forgetting behavior and repetition. Therefore, the proposed memory weight potentially reflects the intensity of human memory over time during a streaming session.
# Whole Organ Engineering: Approaches, Challenges, and Future Directions
## 5 Recent Developments and Accomplishments
### Lungs
They found that incorporating 8 CPT-2 Me-cAMP, an Epac-selective cyclic adenosine monophosphate analog, into the recellularization process resulted in superior endothelial barrier function within the recellularized lungs. HUVECs treated with this Epac agonist demonstrated improved barrier functionality that could be maintained for at least three days, as opposed to the five-hour-long effect resulting from other molecules [PERSON] and colleagues tested. Epac agonist treatment resulted in reorganization of actin structure, as well as an increase in continuity of the junction proteins VE-cadherin and ZO1. When actin polymerization was prevented, the effects of the Epac agonist were removed, substantiating a strong actin-mediated effect. In addition, iPSC-derived endothelial colony forming cells (ECFCs) treated with the Epac agonist displayed an increase in barrier function that was maintained for >60 h. The iPSC-ECFCs were shown to maintain their phenotype after being cultured in lung scaffolds for 5 days, as evidenced by their expression of the proteins CD31, eNOS, vWF, and VE-Cadherin. [PERSON] and colleagues' work shows that Epac-selective 8 CPT-2 Me-cAMP is a potentially useful tool in improving the functionality of engineered endothelium in bioengineered whole lungs, which is a vital component of whole lungs [121]. [PERSON] et al., also utilized decellularization and recellularization of porcine lungs to approach developing a bioengineered lung on a clinically relevant scale [122]. Using hydrogels laden with growth factor containing nanoparticles, [PERSON] and colleagues were able to promote improved cell adhesion to decellularized whole porcine lungs. These scaffolds were developed into bioengineered lungs by seeding them with autologous cells and showed vascular perfusion from collateral circulation after less than 2 weeks post-transplantation. The transplanted lungs could be aerated and contained a microbiome similar to that of native lung. One pig displayed no symptoms of respiratory issues when euthanized an entire 2 months after transplant. This work is a considerable advance in the lung tissue engineering field and demonstrated the viability of tissue-engineered lungs in the realm of clinical applications.
# Game-Theory-Based Multimode Routing Protocol for Internet of Things
###### Abstract  
Various routing protocols have been proposed for ad hoc networks such as the Internet of Things (IoT). Most of the routing protocols introduced for IoT are specific to applications and networks. In the current literature, it is essential to configure all the network nodes with a single proposed protocol. Moreover, it is also possible for a single IoT network to consist of different kinds of nodes. Two or more IoT networks can also be connected to create a bigger heterogeneous network. Such networks may need various routing protocols with some gateway nodes installed. The role of gateway nodes should not be limited to the interconnection of different nodes. In this paper, a multi-mode hybrid routing mechanism is proposed that can be installed on all or a limited number of nodes in a heterogenous IoT network. The nodes configured with the proposed protocols are termed smart nodes. These nodes can be used to connect multiple IoT networks into one. Furthermore, a game-theory-based model is proposed that is used for intercommunication among the smart nodes to gain optimal efficiency. Various performance matrices are assessed under different network scenarios. The simulation results show that the proposed mechanism outperforms in broader heterogeneous IoT networks with diverse nodes.  
IoT; multimode routing ad hoc networks; heterogeneous networks; routing protocols +
Footnote †: journal: Electronics
# Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
## 3 Prototyping Methodology  
### Modeling and Sensors Overview  
The intended data acquisition tool (Weather-Gear) requires both hardware and software development. The hardware segment consists of a collection of environmental sensors connected to the microcontroller board. The microcontroller receives power from a 9 volt (V) direct current (DC) source. The microcontroller's embedded voltage regulator can supply 3.3 V, 5 V, and the source voltage (input voltage, 9 V) for the sensors. The microcontroller is programmed by a computer through the universal serial bus (USB). The sensor data can be logged into an SD card. The same data can be viewed and saved on the internet through the Wi-Fi module or the IDE terminal via USB interface. An outline of the model is illustrated in Figure 1.  
The environmental sensors include air temperature, relative humidity, globe temperature, wind speed, and global solar radiation. A GPS module is used to attain real-world coordinates. The SD card sloth and real-time clock (RTC) are connected to the microcontroller for data logging. The microcontroller's built-in USB port is used for primary communication. A Wi-Fi module is connected for effortless user-to-device interaction. The main challenges for the hardware are accuracy and reaction time of the sensors and precision of the RTC. The secondary challenge is the enhancement of functionality relevant to the user's demand. For this purpose, particulate matter and noise pollution sensors are added. Additional data will be extracted from GPS, such as mobility speed, number of satellites in view, and altitude. The implemented components and sensors over the prototyping process are listed in Table 1.  
The software side of the project includes microcontroller programming with the Arduino IDE. Thereafter, real-time commands can be sent to the microcontroller through IDE terminal, web browsers, and smartphone applications.  
Figure 1: The schematic hardware connection of the model.
# Flexible Phased Antenna Arrays: A Review
## 1 Introduction  
During the past few decades, Phased Arrays (PAs) have been prioritized over traditional arrays due to specific features such as fast and reliable beamforming, the ability to generate multiple beams with minimum interference, the ability to rapidly steer beams electronically, coverage of large areas without mechanically moving parts, and the ability to dynamically adapt to different operational needs [1].
# Analysis and Design of Functional Device for Vehicular Cloud Computing
## 3 System Model
### V2V Communication
#### 3.2.2 Indirect Communication
This implies that the relay choice gain \(A\) in Equation (7) can omit the noise, \(N_{0}\), and can be re-written as  
\[A=\frac{1}{h_{1}}. \tag{11}\]  
To get the PDF of \(\gamma_{AF}\), we need to recall the definition of the harmonic mean and of an exponential variation in Reference [34; 35]. We notice that  
\[\gamma_{AF}=\frac{1}{2}\times\mu(\gamma_{S,R_{i}},\gamma_{R_{i}D}), \tag{12}\]  
where \(\mu(\gamma_{1},\gamma_{2})\) is defined as the reciprocal of the arithmetic mean of the reciprocals of channel \(S\to R_{i}\) and \(R_{i}\to D\). From Equation (8), we can get key results on the statistics of the harmonic mean of two independent exponential RVs (Random Variables). If we assume that  
\[\left\{ \begin{array}{lcl}\gamma_{1}&=&1/\overline{\gamma_{1}},\\ \gamma_{2}&=&1/\overline{\gamma_{2}},\end{array} \right. \tag{13}\]  
then we can get the PDF of \(\gamma_{AF}\) in Equation (10) by using the following transformation of variables  
\[p_{\tau}(\gamma)=2p(2\gamma). \tag{14}\]The PDF \(\gamma_{AF}\) is now written as \(p_{\tau}(\gamma)\) by substituting Equation (8) into Equation (14) and in light of the PDF of the Harmonic Mean of Two Exponential RVs in Reference [34] and after two modifications. Hence,  
\[p_{\tau}(\gamma)=\frac{2\gamma e^{-\gamma(\frac{1}{7S_{k}}+\frac{1}{7R_{k},D})}}{ 1/(7S_{k}\cdot T_{k}\cdot T_{k},D)}\left[\left(\frac{7S_{k}+\gamma T_{k},D}{ \sqrt{\gamma_{S_{k}}\cdot T_{k},D}}\right)K_{1}\left(\frac{2\gamma}{\sqrt{ \gamma_{S_{k}}\cdot T_{k},D}}\right)+2K_{0}\left(\frac{2\gamma}{\sqrt{\gamma_{S _{k}}\cdot T_{k},D}}\right)\right]. \tag{15}\]
## 5 Experiments and Results
### Lossy Compression  
Lossy compression is a common signal processing tool in multimedia; for most applications, lossy compression is a requirement as storage space and communications bandwidth must be optimized. The proposed data-hiding scheme was evaluated under Advanced Audio Coding (AAC) audio compression. AAC is used for experimentation, as it has shown better performance in perceptual transparency and compression rates, as compared with Motion Picture Expert Group MPEG-1 and MPEG-2 Audio Layer 3 (MP3) [33]. The experiment was carried out using the multiplicative strategy, as it has been shown (above) to be the best option. The bitrate for compressor was set to \(256\) kilobits per second (kbps), as this quality is standard for online music distribution.  
Figure 15 shows the BER results and the BER distribution after AAC compression.  
The BER median and median absolute deviation (MAD) were calculated as \(0.0312\) and \(0.0312\), respectively.  
Figure 14: Payload results for the proposed data-hiding scheme. (**a**) Payload results. (**b**) Payload distribution.
## 3 Results
### Electrochemical Properties
These powders simplify the growth of dense bodies even if the sintering temperature is 1000 \({}^{\circ}\)C. As a consequence, very dense sintered electrolytes with high electrochemical properties can be fabricated [22; 41; 42] avoiding many drawbacks [43; 44]. \begin{table}
\begin{tabular}{l c c c c} \hline  & **GDC** & **\({}^{\bf 0.5}\)Cu** & **\({}^{\bf 1}\)Cu** & **\({}^{\bf 2}\)Cu** \\ \hline E\({}_{\rm a}\)(T \(\leq\) 450 \({}^{\circ}\)C) (eV) & 0.85 \(\pm\) 0.031 & 0.99 \(\pm\) 0.051 & 0.98 \(\pm\) 0.029 & 0.88 \(\pm\) 0.140 \\ E\({}_{\rm a}\)(T \(\geq\) 450 \({}^{\circ}\)C) (eV) & 0.85 \(\pm\) 0.048 & 0.78 \(\pm\) 0.066 & 0.78 \(\pm\) 0.041 & 0.78 \(\pm\) 0.166 \\ \(\sigma_{\rm(450\,^{\circ}\rm C)}\) (S-cm\({}^{-1}\)) & 4.23\(\cdot\)10\({}^{-4}\) & 1.78\(\cdot\)10\({}^{-3}\) & 2.05\(\cdot\)10\({}^{-3}\) & 2.23\(\cdot\)10\({}^{-3}\) \\ \(\sigma_{\rm(500\,^{\circ}\rm C)}\) (S-cm\({}^{-1}\)) & 9.21\(\cdot\)10\({}^{-3}\) & 4.66\(\cdot\)10\({}^{-3}\) & 4.98\(\cdot\)10\({}^{-3}\) & 5.34\(\cdot\)10\({}^{-3}\) \\ \(\sigma_{\rm(550\,^{\circ}\rm C)}\) (S-cm\({}^{-1}\)) & 1.92\(\cdot\)10\({}^{-3}\) & 9.18\(\cdot\)10\({}^{-3}\) & 9.31\(\cdot\)10\({}^{-3}\) & 1.04\(\cdot\)10\({}^{-2}\) \\ \(\sigma_{\rm(600\,^{\circ}\rm C)}\) (S-cm\({}^{-1}\)) & 2.29\(\cdot\)10\({}^{-3}\) & 1.61\(\cdot\)10\({}^{-2}\) & 1.73\(\cdot\)10\({}^{-2}\) & 1.87\(\cdot\)10\({}^{-2}\) \\ \(\sigma_{\rm(650\,^{\circ}\rm C)}\) (S-cm\({}^{-1}\)) & 5.03\(\cdot\)10\({}^{-3}\) & 2.59\(\cdot\)10\({}^{-2}\) & 2.89\(\cdot\)10\({}^{-2}\) & 3.05\(\cdot\)10\({}^{-2}\) \\ \(\sigma_{\rm(700\,^{\circ}\rm C)}\) (S-cm\({}^{-1}\)) & 7.68\(\cdot\)10\({}^{-3}\) & 3.95\(\cdot\)10\({}^{-2}\) & 4.36\(\cdot\)10\({}^{-2}\) & 4.73\(\cdot\)10\({}^{-2}\) \\ \(\sigma_{\rm(700\,^{\circ}\rm C)}\) (S-cm\({}^{-1}\)) & 1.16\(\cdot\)10\({}^{-2}\) & 5.5710\({}^{-2}\) & 6.47\(\cdot\)10\({}^{-2}\) & 6.90\(\cdot\)10\({}^{-2}\) \\ \(\sigma_{\rm(800\,^{\circ}\rm C)}\) (S-cm\({}^{-1}\)) & 1.58\(\cdot\)10\({}^{-2}\) & 7.55\(\cdot\)10\({}^{-2}\) & 8.68\(\cdot\)10\({}^{-2}\) & 9.19\(\cdot\)10\({}^{-2}\) \\ \hline \end{tabular}
\end{table} 
Table 4: Total conductivities and activation energies. Figure 7: (**a**) Nyquist plots at several temperatures, (**b**) Arrhenius plot of total conductivity, (**c**) Arrhenius plot of grain boundary conductivity, and (**d**) comparison with literature.