esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries
### Setup Phase  
#### 4.1.1 Neighborhood Discovery  
At network initialization, each node undergoes a setup phase to discover neighborhood and routing parameters. Initially, nodes wake asynchronously and listen for beacon signals [53] from neighboring nodes. Because of beacon exchanges, this phase adds a small control overhead. Routing metrics can be initialized without further probing since each node sends and receives one setup packet and packets from its neighbors. Control overhead stays within reasonable bounds because the one-time neighborhood discovery usually finishes in one duty-cycle period (610 ms). The total setup-phase time of the network depends on the hop distance from any corner of the sensor field to the sink node. For our smooth simulation purpose, we have used a starting packet delay for 10 s. Which means the first packet of the network will be generated after 10 s, so that the setup control packet does not collide with the actual data packets.  
Figure 4: Workflow diagram of our proposed protocol, FCM-OR.  
The beaconing mechanism employed in this protocol aligns with the BoX-MAC protocol [53], as BoX-MAC serves as the underlying MAC layer protocol. The sink node exclusively initiates the setup phase by broadcasting a setup control packet, which contains essential node information. Upon receiving the initial setup packet, neighboring nodes update their routing metrics and rebroadcast the information to propagate network-wide topology awareness. When a node in the vicinity of the sink receives this setup packet, it computes its distance to the sink using the Euclidean distance formula, leveraging the location data embedded in the setup packet along with its predefined positional coordinates, which are assumed to be known at deployment. The receiving node then appends its own position information to the setup packet and forwards it to other nodes farther from the sink. This process continues recursively, ensuring that all nodes in the network acquire one-hop neighbor information, including their respective positions. Following the completion of the first step of the setup phase, each node independently calculates and updates its forwarding rank (FR) based on the collected topological and positional data. Each time a data packet is transmitted, it carries an updated routing metric, enabling neighboring nodes to dynamically refine their routing tables. Furthermore, whenever a node detects a beacon or overhears a data packet, it immediately updates its neighbor information table. Over a predefined interval, nodes accumulate sufficient neighborhood data, facilitating optimized routing decisions based on local topology variations.
# Improvement of Light Extraction in Deep Ultraviolet GaN Light Emitting Diodes with Mesh P-Contacts
###### Abstract  
One of the main reasons that the emission efficiency of GaN-based light-emitting diodes (LEDs) decreases significantly as the emission wavelength shorter than 300 nm is the low light extraction efficiency (LEE). Especially in deep ultra-violet (DUV) LEDs, light propagating outside the escape cone and being reflected back to the semiconductor or substrate layer is absorbed not only by active layers but also by p-type layers with narrower bandgaps and electrodes that are neither transparent nor reflective of the DUV wavelength. In this report, we propose a DUV LED structure with mesh p-GaN/indium-tin-oxide (ITO) contacts and a Ti/Al/Ni/Au layer as a reflective layer to improve LEE. The mesh p-GaN/ITO DUV LED showed an output power of 12% higher than that from the conventional DUV LED due to the lower light absorption at 280 nm.  
LED; DUV; LEE; ITO
# Antimicrobial and Odour Qualities of Alkylpyrazines Occurring in Chocolate and Cocoa Products
## 6 Conclusions  
Following a thorough analysis of the literature data gathered for this research, it can be concluded that the alkylpyrazines found in cocoa and chocolate may be potential antibacterial, antifungal and antioomycete agents for plants, food and possibly even as an additional therapy for _Mycobacteria_-caused tuberculosis. Alkylpyrazines also have strong odour-beneficial effects since they stimulate olfactory receptors and link to emotionally compelling experiences. Through neuroplastic alterations in the olfactory pathways to the limbic system and other regions of the brain related to hedonic sensation, human reactions to chemosensory and olfactory effects can become constitutional. Through associative learning, such occurrences can encourage the consumption of pyrazine-containing goods and medications. That can also serve as cues for the heightened hedonic valence of stimuli, increasing the desire of consumption. Therefore, integrating odour-beneficial, hedonistic and antibacterial properties with one another may allow scholars to address several pressing issues in the food and medical industries. Firstly, the Flavor and Extract Manufacturers Association (FEMA) and the European Union's list of approved flavouring agents have both recognized the use of pyrazines in food and plants for human consumption to be safe food. The flavour profile of products will be significantly improved by adding alkylpyrazines to produced food and still-growing plants, as well as protection from bacterial, fungal and oomycete-induced illnesses. Secondly, by inducing pleasant olfactory system signals, adding pyrazines as an extra therapy to medication therapy may positively influence the course of the disease and also address the issue of patients' antipathy to drugs and refusal to take them. The _t_-test demonstrated a substantial correlation between the odour threshold and the probability (Pa) of antibacterial activity. It is necessary to further examine alkylpyrazines' antimicrobial and odour activities due to the many promising benefits they own that have already been presented in this paper.  
Conceptualization, L.Z. and A.P.; methodology, A.P.; software, A.C.; validation, L.Z., A.P. and A.C.; formal analysis, A.P.; investigation, A.C.; resources, L.Z.; data curation, L.Z.; writing--original draft preparation, A.C.; writing--review and editing, A.P. and L.Z.; visualization, A.C.; supervision, L.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Figure 4: Scatter plot of odour threshold (OT) and Pa values of the antibacterial activity of pyrazines. A sign “_” - means multiply.
# Accurate Constant Phase Elements Dedicated for Audio Signal Processing
## 4 Applications of CPE
### Application of Audio CPEs in Subsonic Two-Port Phase Keeper
\begin{array}{c}\omega_{Z1}=27\frac{\mathrm{rad}}{\mathrm{s}},\ \omega_{Z2}=110\frac{\mathrm{rad}}{\mathrm{s}},\ \omega_{Z3}=499\frac{\mathrm{rad}}{\mathrm{s}},\omega_{Z4}=2.26\frac{\mathrm{ rad}}{\mathrm{s}},\\ \omega_{Z5}=10.54\frac{\mathrm{rad}}{\mathrm{s}},\ \omega_{Z6}=47.08\frac{\mathrm{rad}}{ \mathrm{s}},\omega_{Z7}=220.5\frac{\mathrm{rad}}{\mathrm{s}},\ \omega_{Z8}=2.47\frac{\mathrm{rad}}{\mathrm{s}},\end{array}  \tag{25}\]  
and  
\[ \begin{array}{c}\omega_{P1}=40\frac{\mathrm{rad}}{\mathrm{s}},\ \omega_{P2}=154\frac{\mathrm{rad}}{\mathrm{s}},\omega_{P3}=698\frac{\mathrm{ rad}}{\mathrm{s}},\omega_{P4}=3.15\frac{\mathrm{rad}}{\mathrm{s}},\\ \omega_{P5}=14.9\frac{\mathrm{rad}}{\mathrm{s}},\omega_{P6}=65\frac{\mathrm{ rad}}{\mathrm{s}},\ \omega_{P7}=312.5\frac{\mathrm{rad}}{\mathrm{s}}.\end{array}  \tag{26}\]  
Now, to move CPE approximation to a subsonic frequency band, each angular frequency should be multiplied by a norm \(\gamma=10^{-5}\). As already mentioned, the CPE will be designed as two-port by a cascade connection of general bilinear filters as depicted in Figure 15. Thus, numerical values of resistors and capacitors can be obtained as  
\[ \begin{array}{c}2.7\cdot 10^{-4}=\frac{1}{R_{Z1}C_{Z1}},\ 1.1\cdot 10^{-3}= \frac{1}{R_{Z2}C_{Z2}},5\cdot 10^{-3}=\frac{1}{R_{Z3}C_{Z3}},0.023=\frac{1}{R_{Z4}C_{Z4}},\ 0.105= \frac{1}{R_{Z5}C_{Z5}},\\ 0.471=\frac{1}{R_{Z6}C_{Z6}},\ 2.2=\frac{1}{R_{Z7}C_{Z7}},24.7=\frac{1}{R_{Z8}C_{Z8}},\ 4 \cdot 10^{-4}=\frac{1}{R_{P1}C_{P1}},\ 1.54\cdot 10^{-3}=\frac{1}{R_{P2}C_{P2}},\\ 0.007=\frac{1}{R_{P3}C_{P3}},\ 0.032=\frac{1}{R_{P4}C_{P4}},0.149=\frac{1}{R_{P5}C_{P5}},\ 0.651= \frac{1}{R_{P6}C_{P6}},\ 3.125=\frac{1}{R_{P7}C_{P7}}.\end{array}  \tag{27}\]  
Orcad Pspice verification of this CPE in frequency and time domain is demonstrated in Figure 16. The simulation profile was set as final time \(10^{4}\) s and time step \(100\) ms.  
Of course, values (23) and (24) can be obtained if the capacitors taken from Table 8 are divided by the frequency norm \(\gamma\) then substituted into (1) through algebraic manipulations, or using Mathcad's _collect_ item from the _Symbolic_ toolbox, transformed into semi-symbolic Fraction (5), and polynomials of this fraction are used for root calculations (in Mathcad, the keyword _polyroots_ can be used). Both mentioned approaches can also be applied on the second parallel-series structure of RC two-terminal CPEs.
# A Review of Digital Twin Technology for Electric and Autonomous Vehicles
## 3 Digital Twin Technologies for Electromobility
### Digital Twin for Internet of Vehicles  
By guaranteeing greenhouse gas reduction and fuel efficiency, EVs are attracting a greater share of the private automobile market [31]. Nevertheless, the charging problem is still a barrier to the EV business growth with the present battery technology. Thus, it is essential to have a wide charging infrastructure area that holds fast charging poles, battery swapping stations, and individual charging points for faster EV battery charging. In this scenario, the EV model is simulated with the DT to accurately replicate the EV in the real world. Introducing a DT model makes it easy to simulate mobility behaviors and interactions to study the efficiency of the charging pole and EVs from the demand-side and supply-side.  
DT for IoV creates virtual representations of vehicles and traffic systems, connecting the virtual world and physical world spaces. This allows for real-time vehicle and traffic system performance monitoring, improved situational awareness, feasibility forecasting, and decision-making through comprehensive, multidimensional modeling. Continuous, real-time interactions between the virtual and physical realms are necessary for accurate simulations [32].  
One example of the potential of DT for IoV is found in [33]. In this work, a simulation platform based on DT models is proposed to replicate and simulate charging and discharging processes of large-scale EV fleets in different kinds of dynamic scenarios. To replicate the realistic mobility of EVs, the operational motion parameters, such as steering angle, orientation angle, and acceleration, are integrated into the mobility model to simulate the realistic trajectories. A tracking method of the dynamic position and orientation is proposed to synthesize the reasonable trajectory of EVs in day-to-day traffic conditions. In this framework, in which the key elements of the real world are considered, the digital twins of EVs and charging points behave and interact with each other as real entities; the simulation results can be used to evaluate schemes for the deployment and management of EVs and charging infrastructures. In addition, the DT simulation platform can be used to verify the design of the deployment of the charging infrastructure and related impacts on the smart grid [34]. The advancements in this area can solve the problem of managing and exploiting real-time traffic data. The large amounts of traffic data available could help in creating a DT that creates a virtual representation of physical vehicles via various communication means.
## 5 Conclusions  
We demonstrated a novel design concept for a laser-based sensor that can detect the nature of water, pure or salty water, under different temperature conditions, without affecting the chemical properties of the water sample. The system combines machine learning and optics-laser concepts. The data set was generated using COMSOL Multiphysics and was used to train the MLP model. Simulation results show that the model prediction is acceptable with an MSE of 0.155 and \(R^{2}\) of 0.844. The proposed method uses low-cost and biochemical-free technology with possible Internet of things (IoT) applications. The designed sensor can pave the way for the development of a multi-parameter sensor and prototyping demonstration for a wide range of applications in water pollution and environmental monitoring, among other future applications.  
Conceptualization, S.V. and B.M.; methodology, S.V., B.M. and N.F.; software, B.M. and N.F.; validation, B.M., M.A. and B.N.; formal analysis, S.V. and B.M.; investigation, M.A., B.M. and S.V.; resources, S.V. and B.M.; writing--original draft preparation, B.M., S.V., B.N., M.A. and N.F.; writing--review and editing, B.M., S.V. and B.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Results and Discussion
### Effect of PAW Treatment on the Main VOCs in Rocket Leaves
#### 2.1.4 Aldehydes  
The aldehydes which were detected in the rocket samples were 2-methyl propanal, 2-methyl butanal, 3-methyl butanal, pentanal, hexanal, 2-hexenal (_E_), octanal, nonanal, 3-furfurral, benzaldehyde, \(\beta\)-cyclocitral, benzeneacetaldehyde, and 2-methyl benzaldehyde. It is worth mentioning that aldehydes showed a high degree of association with taste, flavour, and mouth-feel traits in rocket [16]. (_E_)-2-hexenal and hexanal were associated with the green aroma impression of the rocket-salad. Also, herbal aromas are caused by (_E_)-2-hexenal. Floral-fruity odour notes can be associated with nonanal. At the same time, nutty and almond-like odour impressions are known from furfurral and benzaldehyde. Hexanal and nonanal compose the fatty side-notes [19]. Overall, non-significant changes were observed in the total content of aldehydes in PAW-2, -10, and -20. However, a significant increase (_p_ < 0.05) in the level of total aldehydes was observed only in PAW-5 samples. A significant increase was observed in the content of 2-methyl propanal, 2-methyl butanal, pentanal, hexanal, octanal, 3-furfurral, benzaldehyde, and \(\beta\)-cyclocitral (_p_ < 0.05). In addition, a non-significant increase was revealed for all the other detected aldehydes (_p_ > 0.05). The results evidenced that plasma treated cells in PAW-5 accumulated higher amounts of several aldehydes compared to the controls. Previous studies have also reported an increase in aldehyde content in guava-flavored whey beverages [31] and milk [28] after plasma treatment. This increase in these aldehydes could be attributed to the degradation of several unsaturated fatty acids found in rocket [1] by auto-oxidation and/or the spontaneous decomposition of hydroperoxides. Such degradation could be the result of the damaging effect of reactive species produced by the plasma, which can initiate lipid peroxidation and produce hydroperoxide, and can then be converted to secondary oxidation products such as aldehydes or shorter fatty acyl compounds. [32; 33]. However, further studies are needed to confirm these assumptions.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 6 Practical Challenges in Underwater SLAM
### Underwater Communication
#### 6.1.3 Electromagnetic Spectrum  
* RF: limited to very low frequencies (<30 Hz) for long-range underwater use, with impractical antenna sizes for AUVs [90];
* MI: short-range (<20 m), suitable for localized swarm coordination [91].  
Optimizing AUV trajectories in the presence of ocean currents can significantly reduce energy consumption and mission time, particularly in large-scale underwater sensor networks where visible light communication is used for high-rate data retrieval [92]. Additionally, efficient algorithms for multi-AUV placement can maximize sensor coverage  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Frequency/Range** & **Bandwidth** & **SLAM Use Case** & **Limitations** \\ \hline Acoustic & 1–500 kHz & \textless{}100 kbps & Long-range mapping, DVL integration & Latency, multipath \\ Optical & 450-700 nm & Up to Gbps & Clear-water VSLAM & Turbidity, line of sight \\ RF & \textless{}30 Hz & \textless{}1 bps & Emergency signaling & low bandwidth \\ MI & 10–100 kHz & \textless{}100 kbps & Short-range AUV teams & Limited range \\ \hline \hline \end{tabular}
\end{table}
Table 7: Underwater communication spectrum for SLAM applications.  
and ensure optimal association between sensors and AUVs, even in scenarios with varying sensor priorities [93]. These advancements highlight the importance of integrating communication and motion planning to enhance the performance of optical underwater networks.  
In summary, restricted bandwidth, intermittent connectivity, and high latency undermine real-time cooperative SLAM. As an interim solution, many AUVs rely on acoustic links for basic telemetry while performing most SLAM computations onboard. Future hybrid strategies--combining acoustic, optical, and potentially, electromagnetic methods--could enable more flexible data-sharing frameworks in underwater SLAMs.
## 5 Discussion  
DGRL significantly improves the performance of WSN. However, there are also some challenges that need to be addressed further.  
The computational complexity of DGRL is mainly due to the computation of graph convolution and reinforcement learning. The structure of DGRL's neurons network includes four GCN layers and three full connected layers. The complexity of matrix operations to get the features of the next layer can be regarded as the computational complexity of neural networks. The complexity of a single-layer neural network can be expressed as \(O(|V|FF^{\prime})\), where \(|V|\) represents the number of nodes in the network topology, \(F\) represents the feature dimension of the node, and \(F^{\prime}\) represents the embedding dimension. Meanwhile, the computational complexity of the Dijkstra algorithm used in OSPF is only \(O(|V|^{2})\).  
In order to avoid congestion, DGRL will choose a path in which there are more hops but lower buffer occupancy. This policy will lead to more times of packet forwarding, resulting in an increase in the overall network load. However, the load pressure of nodes in the central position and nodes with relatively large degree can be relieved. As shown in the Figure 11a, under the same traffic intensity 50%, the total times of forwarding of DGRL are higher than OSPF. We selected several key nodes to count the number of packets forwarded by them, as shown in Figure 11b. It can be seen that compared with other algorithms, the key nodes in DGRL forward the least number of packets. Considering that in wireless sensor networks, the main energy consumption of nodes comes from the sending and receiving operations, reducing the forwarding times of key nodes in the network can prolong the network life cycle.  
Figure 10: Comparison between DGRL and its variants under traffic intensity 50%. (**a**) PDR. (**b**) Delay. (**c**) Dispersion.
# Distributed Learning for Dynamic Channel Access in Underwater Sensor Networks
###### Abstract  
In this study, the problem of dynamic channel access in distributed underwater acoustic sensor networks (UASNs) is considered. First, we formulate the dynamic channel access problem in UASNs as a multi-agent Markov decision process, wherein each underwater sensor is considered an agent whose objective is to maximize the total network throughput without coordinating with or exchanging messages among different underwater sensors. We then propose a distributed deep Q-learning-based algorithm that enables each underwater sensor to learn not only the behaviors (i.e., actions) of other sensors, but also the physical features (e.g., channel error probability) of its available acoustic channels, in order to maximize the network throughput. We conduct extensive numerical evaluations and verify that the performance of the proposed algorithm is similar to or even better than the performance of baseline algorithms, even when implemented in a distributed manner.  
acoustic communication; deep reinforcement learning (DRL); distributed algorithm; dynamic channel access; multi-agent RL; underwater sensor networks +
Footnote †: journal:  
_Article_  
Distributed Learning for Dynamic Channel Access in Underwater Sensor Networks
## 3 Results and Discussion
### Retrofitted Joints  
From the non-retrofitted joints, three joints with different governing buckling modes were selected for retrofitting. The properties of these joints and their critical buckling modes are listed in Table 4. The joints were retrofitted with FRP patches in various orientation, length, and number of plies to investigate their effects on the buckling and post-buckling response.
## 4 Experiments
### Ablation Study  
We conducted ablation studies on the VisDial v1.0 validation splits to evaluate the influence of each component in our model. Modality Alignment module is not ablated because this module handles the visual features. We use the same discriminative decoder model for all ablations to exclude the impact of multitask learning.  
In Table 4, the first rows of each block indicate the impact of each module in our model. Since the two modules (i.e., Context Matching and Topic Aggregation) are interdependent, we employ simple visual features instead of topic-aggregation representation for **MVAN w/o Topic Aggregation**, whereas we simply remove context-matching representation for **MVAN w/o Context Matching**. Both models obtain slightly lower performance with respect to all evaluation metrics than **MVAN**. We can hence infer that the two modules are complementary with respect to each other and our model integrates these complementary characteristics well for the task.  
Recent approaches [11; 17; 24] reported that they observed a trade-off relationship between two primary metrics (i.e., NDCG and MRR) in the visual dialog task. We also found the trade-off relationship through ablative experiments with and without dialog history features (see Table 4). Specifically, adding dialog history features improves the MRR score by 3.54% on average, whereas NDCG score is decreased by 1.92% on average. We observe that the model has a tendency to predict the answers more precisely (i.e., it has a better MRR score) when the dialog history features are added. This may imply that question-related clues in the dialog history are important factors in reasoning the ground truth, but they hinder the model's generalization ability (i.e., they lower the NDCG score).