esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment
### Experimental Analysis
#### 3.3.1 Experimental Results Analysis  
Table 2 reports the results regarding EA. The results indicate that the proposed method outperforms the other models, achieving the best performance. Specifically, the proposed method improved Hits@1 by over 20% compared to the baseline methods. When incorporating visual information, the proposed method achieved a 4-15% improvement in Hits@1 over the other methods. This suggests that combining visual representations can effectively enhance cross-lingual entity representations to infer their correspondences.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**DBP15K\({}_{\text{ZH}-EN}\)**} & \multicolumn{2}{c}{**DBP15K\({}_{\text{ZH}-EN}\)**} \\  & **Hits@1** & **Hits@10** & **MRR** & **Hits@10** & **Hits@10** & **MRR** & **Hits@1** & **Hits@10** & **MRR** \\ \hline MTransE & 30.83 & 61.41 & 0.364 & 27.86 & 57.45 & 0.349 & 24.41 & 55.55 & 0.335 \\ JAPE & 41.18 & 74.46 & 0.490 & 36.25 & 68.50 & 0.476 & 32.39 & 66.68 & 0.430 \\ FVA & 59.44 & 83.44 & 0.680 & 63.12 & 85.85 & 0.712 & 66.52 & 88.40 & 0.747 \\ DNCN & 72.10 & 87.90 & 0.775 & 72.13 & 88.58 & 0.781 & 74.84 & 88.53 & 0.790 \\ MDSEA & 76.81 & 90.35 & 0.814 & 76.92 & 94.63 & 0.832 & 76.51 & 94.67 & 0.834 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cross-language EA results on DBP15K.  
Compared to the multimodal methods, the proposed method, by introducing multimodal data supervision, enables the model to learn more generalized and robust representations, aiding in addressing noise and variations, thus achieving good entity alignment performance even in complex data scenarios.
# YOLO-PWSL-Enhanced Robotic Fish: An Integrated Object Detection System for Underwater Monitoring
## 5 Conclusions  
This study proposes a novel underwater monitoring and detection system: the YOLO-PWSL-enhanced robotic fish. We first designed a biomimetic robotic fish equipped with multiple sensors and a folding robotic arm for dynamic water quality monitoring. Second, we designed an underwater hovering method that combines a PID controller with a depth sensor to provide a dynamically stable environment for the system. Finally, the we propose a target detection model, YOLO-PWSL, based on an attention mechanism fusion. The experimental results showed that the improved YOLOv5 target detection algorithm achieved a detection accuracy of 96.1% ([EMAIL_ADDRESS]), enabling precise identification in complex environments. In practical applications, optimization for occluded scenes can reduce missed detections in dense fish schools, providing an alternative to traditional manual inspections. The experimental results validated the feasibility of this system platform. This work provides a valuable foundation for the future development of more efficient and intelligent sustainable water quality monitoring and ecological protection, offering new solutions for intelligent aquaculture in the aquaculture industry.  
Conceptualization, L.L. and W.Z.; data curation, L.L. and H.H.; supervision, L.L. and Y.T.; writing--original draft, L.L. and Q.T.; writing--review and editing, L.L. and Y.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National College Students Innovation and Entrepreneurship Training Program (202410616037).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest. All images are from the public domain or have been authorized and do not infringe on privacy or copyright. The dataset is for academic research purposes only.
## 4 Multiplexing
### Multiple-Input Single-Output  
[PERSON] et al. [115], propose a system that consists of dual-hop utilizing MISO RF and also a SIMO FSO relaying system. The system has Alamouti-type space-time block coding and transmitter antenna selection (TAS) schemes between the source and the relay. The relay is an AF relay with fixed and variable gain protocols. The relay will be fixed on a mountain or on the top of a building; since supplying energy is insufficient; therefore, the relay must be constructed with less complexity in both hardware and software by using a single receiving antenna and single transmitting aperture. The authors calculate OP, outage and channel capacity, and ASER to measure the system's performance under different conditions. The presented mixed MISO RF/SIMO FSO relaying system (MSM-RS) has superior performance to the mixed MISO RF/SISO FSO relaying system (MSS-RS) as it accomplishes lower ASER and OP under strong turbulence. In other words, MSM-RS is stronger than MSS-RS when the weather worsens. In addition, an AF relay with a fixed gain is much better than a variable gain concerning outage capacity.
# Study on Thermal Radiation Characteristics and the Multi-Point Source Model of Hydrogen Jet Fire
## 1 Introduction
The current research on the thermal radiation prediction of hydrogen jet fires by multi-point source models is limited. Extensive literature indicates that the precision of thermal radiation prediction models for hydrogen jet fires under complex conditions still requires enhancement. Determining the optimal weight coefficients remains a critical challenge that must be addressed. This study aimed to further optimize the weighted coefficients in prediction models for hydrogen jet fires under complex conditions. Experimental investigations were conducted within a leakage pressure range of 1 MPa and leakage diameters of 1.2 mm, 1.5 mm, and 2.0 mm. A series of experiments were conducted to analyze the size and thermal radiation characteristics of jet flames at various flame heights and radial distances. Computational fluid dynamics (CFD) simulations were used to study the temperature and thermal radiation distribution of jet fires under high-pressure conditions. Furthermore, based on the temperature distribution along the flame axis, discrete weighted coefficients in the multi-point source radiation model were optimized, and the optimized model was validated with experimental data.
# Time Slotted Channel Hopping and ContikiMAC for IPv6 Multicast-Enabled Wireless Sensor Networks
## 3 Background  
The protocols under study in this paper were IPv6-compatible RPL, BMRF, TSCH, and ContikiMAC. Figure 1 illustrates the relationship between the protocols under discussion in this section.  
Figure 1: Relationship between the protocols under discussion. DODAG: destination-oriented directed acyclic graph; BMRF: Bidirectional Multicast Routing Protocol for Low Power and Lossy Networks (RPL) Forwarding; LL: link layer; TSCH: time slotted channel hopping.  
RPL [12], a distance routing protocol devised by the Internet Engineering Task Force (IETF) for low-power and lossy networks (LLNs), constructs a network topology by forming a DODAG. In a DODAG, a node can have multiple parents. Choosing only the link with the preferred one results in a RPL tree. In this tree, each node directly reaches the root or through intermediate links. BMRF uses the RPL tree and group management for organizing IPv6 multicast routing. BMRF supports both LL unicast and LL broadcast forwarding. TSCH, the MAC layer protocol, together with the Orchestra scheduler, assigns cells for each node in the network to forward messages via LL unicast or LL broadcast. Orchestra uses information gathered beforehand by RPL to build the schedule for the nodes in the network.
## 2 Frequency Reconfigurable Microstrip Patch Antenna Array
### Simulation and Measurement Results
The gains of the single patch antenna and the patch antenna array are shown in Figure 7. The gain of the single patch antenna is 4.4 dBi. Also, the gain of the single patch antenna array is 7.8 dBi. Therefore, the gain is improved by using the array. As a result, we updated the gains of the frequency reconfigurable antenna array. Figure 6: Radiation gain patterns of the antenna array in the xz plane and yz plane at different frequencies. The gains of the antenna array at different frequencies between the S-band are shown in Figure 8a. Also, the simulated and measured gains are illustrated in Figure 8b when one smaller side patch is connected to the larger main patch. Simulated results have shown that the gain is above 7 dBi. Measurement results have shown that the gain of the antenna array is above 6 dBi at different frequencies. There was an error rate of about 6% between the simulation results and the measurement results. Measured gains were slightly lower than the simulated gains due to measurement errors and the fabrication tolerance. In short, the gains of the antenna array are relatively stable within the range of the frequency. The gains of the frequency reconfigurable antenna array are slightly lower than the gains of the patch antenna array without any smaller side patches. Also, these gains are reduced as the capacitance of the varactor increases due to increases in internal resistance. In short, the radiation patterns and the gains of the antenna array are relatively stable within the range of the frequency.
###### Abstract  
This paper addresses a formation tracking problem of multiple low-cost underwater drones by implementing distributed adaptive neural network control (DANNC). It is based on a leader-follower architecture to operate in hazardous environments. First, unknown parameters of underwater vehicle dynamics, which are important requirements for real-world applications, are approximated by a neural network using a radial basis function. More specifically, those parameters are only calculated by local information, which can be obtained by an on-board camera without using an external positioning system. Secondly, a potential function is employed to ensure there is no collision between the underwater drones. We then propose a desired configuration of a group of unmanned underwater vehicles (UUVs) as a time-variant function so that they can quickly change their shape between them to facilitate the crossing in a narrow area. Finally, three UUVs, based on a robot operating system (ROS) platform, are used to emphasize the realistic low-cost aspect of underwater drones. The proposed approach is validated by evaluating in different experimental scenarios.  
low-cost underwater robotics; distributed adaptive neural network control; collision and obstacle avoidance; robot operating system (ROS); Gazebo
## 3 Results and Discussion
### Photophysical Properties
Glass transition temperature (T\({}_{\rm g}\)) of TP-AFF-TP and TP-ASF were measured by differential scanning calorimetry (DSC), which showed 204 and 190 \({}^{\circ}\)C, respectively, as shown in Figure S1. Under the joule heating, which comes from the device operation, this high thermal stability is able to stabilize the operation of the device. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Solution \({}^{\rm a}\)**} & \multicolumn{3}{c}{**Film \({}^{\rm b}\)**} & \\ \cline{2-10}  & \(\lambda_{\rm Abs}\) & \(\lambda_{\rm PL}\) & **FWHM \({}^{\rm c}\)** & \(\lambda_{\rm Abs}\) & \(\lambda_{\rm PL}\) & **FWHM \({}^{\rm c}\)** & **HOMO \({}^{\rm d}\)** & **LUMO \({}^{\rm d}\)** & **Band Gap** & **T\({}_{\rm d}\)** \\  & **(nm)** & **(nm)** & **(nm)** & **(nm)** & **(nm)** & **(nm)** & **(nm)** & **(eV)** & **(eV)** & **(eV)** & **(\({}^{\circ}\)C)** \\ \hline TP-AFF-TP & 388,410 & 426, 447 & 55 & 393,416 & 477 & 96 & \(-5.80\) & \(-2.79\) & 3.01 & 481 \\ TP-ASF & 375, & 396,419 & 439, 456 & 57 & 381, 401,425 & 467 & 68 & \(-5.72\) & \(-2.80\) & 2.92 & 407 \\ \hline \end{tabular}  
* Chloroform solution (1.00 \(\times\) 10\({}^{-5}\) M). \({}^{\rm b}\) Film with a thickness of 50 nm on glass. \({}^{\rm c}\) FWHM of PL peak. \({}^{\rm d}\) HOMO values derived from ultraviolet photoelectron spectra (Riken-Keik, AC-2).  
\end{table} 
Table 1: Photophysical properties of the synthesized materials. Figure 2: UV-visible absorption and photoluminescence analysis of synthesized substances (**a**) in the solution state (chloroform, 1 \(\times\) 10\({}^{-5}\) M) and (**b**) in the vacuum-deposited film (thickness: 50 nm).
# Faudistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability
###### Abstract  
Cooperative intelligent transportation systems (C-ITSs) such as platooning rely on a robust and timely network that may not always be available in sufficient quality. Out of the box hybrid networks only partly eliminate shortcomings: mutual interference avoidance, data load balancing, and data dissemination must be sophisticated. Lacking network quality may lead to safety bottlenecks that require that the distance between the following vehicles be increased. However, increasing gaps result in efficiency loss and additionally compromise safety as the platoon is split into smaller parts by traffic: maneuvers, e.g., cut-in maneuvers bear safety risks, and consequently lower efficiency even further. However, platoons, especially if they are very long, can negatively affect the flow of traffic. This mainly applies on entry or exit lanes, on narrow lanes, or in intersection areas: automated and non-automated vehicles in traffic do affect each other and are interdependent. To account for varying network quality and enable the coexistence of non-automated and platooned traffic, we present in this paper a new concept of platooning that unites ad hoc--in form of IEEE 802.11p--and cellular communication: feudalistic platooning. Platooned vehicles are divided into smaller groups, inseparable by surrounding traffic, and are assigned roles that determine the communication flow between vehicles, other groups and platoons, and infrastructure. Critical vehicle data are redundantly sent while the ad hoc network is only used for this purpose. The remaining data are sent--relying on cellular infrastructure once it is available--directly between vehicles with or without the use of network involvement for scheduling. The presented approach was tested in simulations using Omnet++ and Simulation of Urban Mobility (SUMO).  
C-ITS; platooning; platoon management; IEEE 802.11p; C-V2X; Omnet++; SUMO +
Footnote †: journal: Sensors
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 3 Physiological Roles
### Virulence/Symbiosis and Metabolism  
Many pathogens, as well as symbiotic bacteria carry multiple types of TA systems that are implicated in host cell entry and in adaptation to intracellular environments. FitB toxin of FitAB from _Neisseria gonorrhoeae_, a structural homologue of VapC, is crucial for pathogens to efficiently cross theepithelial barrier to enter host cells [151]. The _fitAB_ deletion mutant exhibits much slower rates of crossing polarized monolayer membranes than the wildtype stain [151].  
Genome sequence comparison of _M. tuberculosis_ and its non-pathogenic relative, regarding MazF-mt3 and MazF-mt7 cleavage sites, revealed a potential connection between these toxins and the pathogenicity of _M. tuberculosis_[21; 45]. Several proline-proline-glutamic acid (PPE) genes in the _M. tuberculosis_ genome show an underrepresented frequency of MazF-mt3 and MazF-mt7 cleavage sites [45]. The PPE family consists of proteins with unknown functions that localize on cell walls or cell surfaces [152; 153]. While non-pathogenic _Mycobacterium_ strains lack PPE genes, pathogenic _M. tuberculosis_ has multiple copies of PPE genes. This evidence suggest their involvement in host-pathogen interactions and the pathogenicity of _M. tuberculosis_. A biological basis behind the lack of MazF-mt3 and MazF-mt7 cleavage sites in PPE genes and their roles in immunopathogenicity await experimental verification.  
In _Mycobacteria_, VapBC plays a role in coupling cell growth and carbon metabolism [48]. When _vapBC_ is deleted, cells consume glycerol at a faster rate than the wildtype, without increasing cell growth. Additionally, the expression of genes in sugar metabolism and transport is specifically altered when VapC is overexpressed.  
VapC homologue NtrR in _Sinorhizobium meliloti_ is implicated in maintaining the metabolic balance and adaptation to metabolism inside of the symbiotic host [154]. The _ntrR_ mutant exhibits a loss of the ability to effectively down-regulate the genes involved in symbiosis and metabolism under microoxic conditions. In particular, the transcription of _nod_ (modulation) and _nif_ (nitrognase) genes is elevated in the _ntrR_ knockout mutant. As a result, the _ntrR_ deletion strain shows an increase in nitrogen concentration and biomass, as compared with the wildtype strain [154; 155].
## 3 Control Strategy Description  
### Description of the Feedback System  
In order to make the mathematical derivation process of the whole article clearer and clearer, rewrite the q-axis state equation in Equation (1) into the following form:  
\[\begin{array}{c}\dot{x}_{1}=ax_{1}+bx_{2}+d(t)\\ \dot{x}_{2}=cx_{1}+dx_{2}+gu\\ y=x_{1}\end{array} \tag{2}\]  
Among them,  
\[a=-\frac{B}{j};\;b=\frac{3P\psi}{2j};\;c=-\frac{P\psi}{L};\;d=-\frac{R}{L};\;g= \frac{1}{L};\;x_{1}=\omega;\;x_{2}=i_{q}\]  
and \(d(t)\) is the aggregate disturbances of the mismatched channels of the PMSM system.  
The research goal of this paper is to realize the speed tracking control of PMSM. Therefore, an error tracking control signal (\(e\)) and an error control signal of disturbance (\(e_{d(t)}\)) are defined in this section. The specific forms are as follows:  
\[e=x_{1}-x_{1d} \tag{3}\]  
\[e_{d(t)}=d(t)-\hat{d}(t) \tag{4}\]  
where \(\hat{d}(t)\) is the observer estimate value of \(d(t)\) and \(x_{1d}\) represents the desired value of \(x\). The significance of defining Equations (3) and (4) is that it can ensure that the speed of the PMSM servo control system converges to the desired value in a finite time, and the error estimate converges to the true value. Equation (5) is the state equation of the speed tracking control signal.  
\[\begin{array}{rl}\dot{e}&=\dot{x}_{1}-\dot{x}_{1d}=ax_{1}+bx_{2}+d(t)\\ &=a(e+\;x_{1d})\;+bx_{2}+d(t)\\ &=ae+ax_{1d}+bx_{2}+d(t)\end{array} \tag{5}\]  
Equations (3) and (5) can be solved simultaneously to obtain the \(q\)-axis state equation of the PMSM servo speed control system, as shown in Equation (6).  
\[\begin{array}{c}\dot{e}\\ \dot{x}_{2}\end{array}=\begin{bmatrix}a&b\\ c&d\end{bmatrix}\begin{bmatrix}e\\ x_{2}\end{bmatrix}+\begin{bmatrix}0\\ g\end{bmatrix}u+\begin{bmatrix}1\\ 0\end{bmatrix}d(t)+\begin{bmatrix}a\\ c\end{bmatrix}x_{1d} \tag{6}\]
## 5 Latency Mitigation Strategies
### Method Correlation with CAVs  
There are very few studies on delay mitigation methods for CAVs. Nevertheless, it is feasible to adapt methodologies employed in other domains to fill this gap, provided the distinctive requirements of CAVs are taken into consideration. However, it is essential to note that the developed methods should be optimised and minimise any additional latency they introduce in the teleoperation control loop [8]. A potential solution is using predictive feedback methods, such as the predictive model-free and online approaches, which are well suited for addressing both control and perception delays, respectively. The developed methods should focus on dynamically adjusting their sensor retrieval and driving behaviour based on the network conditions. One can achieve safe and reliable teleoperation even while predicting the short-term or immediate future [12], i.e., during latency, it is sufficient to predict approximately the number of frames based on the visual update rates (the cameras FPS). Further, to improve the operator situational awareness, it is also recommended to use multi-sensory feedback (e.g., cameras along with lidars, radars, audio, and haptic). However, appropriate sensor choice is critical for the sensor fusion to be safe and reliable, since camera-based sensors may require higher computational power and are sensitive to lighting conditions. Meanwhile, active sensors such as lidar and radar can be affected by interference from other sensors [173].  
Moreover, most studies use simulations to experiment and test the teleoperation environments [36]. This is a cost-effective solution, yet it may not always provide realistic results due to simplistic assumptions and constrained conditions. Again, real-world experiments also have their downsides, including being expensive to build and, most importantly, not reproducible for further research.
## 3 Results and Discussion  
### Analysis of Gasification Product Components  
Figure 2 illustrates the effects of gasification temperatures and two heating methods on the mass yields of syngas, tar, and biochar derived from oak and corn stalk gasification. As the gasification temperature increases from 600 to 1000 \({}^{\circ}\)C, the syngas yield increases while the tar and biochar yields decrease. The increase in the gasification temperature enhances the pyrolysis and oxidation reactions during the process. This leads to higher consumption of solid raw materials converted into volatile gases. Furthermore, higher temperatures promote the processes of tar cracking and reforming.  
Figure 2 shows that MG produces less biochar at the same temperature as EG, but it is more efficient in producing syngas. Some studies have obtained similar results [34; 36; 37; 38]. Since the biochar could absorb the microwave energy, which would cause a higher temperature of biochar, promoting the MG process. The self-gasification reaction between biochar and CO\({}_{2}\) during MG could decrease the biochar yield. Additionally, microwave heating is more efficient and uniform [27; 40]. The occurrence of hot spots (sparks) enhance the production of gas-phase products [51]. EG generates higher tar yields than MG at lower temperatures, while MG becomes more favorable for tar production at higher temperatures. MG is influenced by the dielectric properties of the material being heated. Biomass samples  
Figure 2: Yield of gasified products from oak (**a**) and corn stalk (**b**) via MG and EG.  
could generate gaseous tar during MG, which subsequently undergoes volatilization from the biomass. This volatilization creates a temperature gradient between the material surface and the heating zone. Consequently, a greater amount of tar condenses within the quartz tube compared to electric heating methods.