esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Opportunistic Routing Algorithm
### Priority Calculation and Scheduling of Candidate Forwarding Nodes  
After obtaining the link connectivity \(LC_{i_{j}}(t)\) between the marine node and their neighbor nodes, the optimal expected packet advancement prediction \(h(Y_{i}^{t})\), the distance to sink \(d_{j-Sink}(t)\), and the remaining energy probability distribution \(\tilde{\xi}_{i_{j}}\), the proposed priority calculation formula of the candidate node \(j\) at time \(t\) is defined as,  
\[P_{i_{j}}(t)=\ln\left(1+\frac{LC_{i_{j}}(t)\cdot h\big{(}Y_{i}^{t}\big{)}\cdot \tilde{\xi}_{i_{j}}(t)}{d_{j-Sink}(t)}\right) \tag{15}\]  
From Equation (15), we can see that the optimal next-hop node is:  
\[j^{*}=\underset{j}{\text{argmax}}P_{i_{j}}(t) \tag{16}\]  
According to \(P_{i_{j}}(t)\), the priority rank of the candidate forwarding nodes can be obtained. Finally, we executed packet forwarding using timer-based coordination algorithm. The candidate nodes with the highest priority intend to perform the data packet delivery first, while the other candidate forwarding nodes remain dormant. When the high-priority candidate node does not successfully forward the data packet within time \(t_{\text{min}}\), a lower-priority candidate node will be activated and attempt to forward the data packet. The above process continues until the marine node's perception data is successfully forwarded as explained in Algorithm 2. The proposed opportunistic routing algorithm has two benefits: (1) Since the number of candidate nodes is less than the number of neighbor nodes, it reduces the number of packet duplications and beacon collisions; (2) For the node with priority \(k\), the waiting time is \(T_{e}\left(Y_{i}^{t},\theta\right)+(k-1)t_{\text{min}}\).The subsequent simulation results display that this greatly reduces the time delay in the routing process. The flowchart of the proposed opportunistic routing algorithm is shown in Figure 3.  
Figure 3: The flowchart of the proposed opportunistic routing algorithm.
## 4 System Model
### Computation Model
#### 4.2.2 Remote Execution  
For the remote execution approach where the computation task \(j\) of VR device \(i\) will be offloaded and processed at the base station or cloud server, the total execution time can be respectively calculated as:  
\[T_{i,j}^{E}=T_{i,j}^{off}+T_{i,j}^{E,exec}+T_{i,j}^{down} \tag{5}\]  
\[T_{i,j}^{C}=T_{i,j}^{off}+\xi+T_{i,j}^{C,exec}+T_{i,j}^{down} \tag{6}\]  
where \(\xi\) is constant denoting the propagation delay for transferring the computation task between the base station and cloud server. While \(T_{i,j}^{off}\), \(T_{i,j}^{down}\), \(T_{i,j}^{E,exec}\) and \(T_{i,j}^{C,exec}\) denote the offloading, downloading and execution time for processing the computation task \(j\) of the VR device \(i\) at the base station and cloud server, respectively which can be expressed as follows:  
\[T_{i,j}^{off}=\frac{a_{i,j}}{r_{i}^{U}} \tag{7}\]  
\[T_{i,j}^{down}=\frac{b_{i,j}}{r_{i}^{D}} \tag{8}\]  
\[T_{i,j}^{E,exec}=\frac{c_{i,j}}{f_{i}^{E}} \tag{9}\]  
\[T_{i,j}^{C,exec}=\frac{c_{i,j}}{f_{i}^{C}} \tag{10}\]  
where \(f_{i}^{E}\) and \(f_{i}^{C}\) denote the computational capability of the base station and cloud server which is assigned to the VR device \(i\).  
Consequently, the energy consumption for offloading, downloading and processing the computation task \(j\) of VR device \(i\) remotely at the base station and cloud server can be expressed as follows:  
\[E_{i,j}^{E}=p_{i}^{T}T_{i,j}^{off}+\beta T_{i,j}^{E,exec}+p_{i}^{R}T_{i,j}^{ down} \tag{11}\]  
\[E_{i,j}^{C}=p_{i}^{T}T_{i,j}^{off}+\beta T_{i,j}^{C,exec}+p_{i}^{R}T_{i,j}^{ down} \tag{12}\]  
where \(p_{i}^{R}\) denote the reception power of the VR device and \(\beta\) is constant denoting the energy consumption for being idle while processing the task at the edge and cloud.  
In view of communication and computation models, the total overhead for executing the computation task \(j\) of the VR device \(i\) in terms of time and energy can be respectively expressed as:  
\[Z_{i,j}=w_{i,j}^{t}(T_{i,j})+w_{i,j}^{e}(E_{i,j}) \tag{13}\]  
where \(w_{i}^{e}\) and \(w_{i}^{t}\in[0,1]\) denote the weighting parameters of execution time and energy consumption for VR device \(i\)'s decision making, respectively. While \(T_{i,j}\) and \(E_{i,j}\) are total time and energy which can be expressed as:  
\[T_{i,j}=\left[\alpha_{i,j,0}T_{i,j}^{L}+\alpha_{i,j,1}T_{i,j}^{E}+\alpha_{i, j,2}T_{i,j}^{C}\right] \tag{14}\]  
\[E_{i,j}=\left[\alpha_{i,j,0}E_{i,j}^{L}+\alpha_{i,j,1}E_{i,j}^{E}+\alpha_{i, j,2}E_{i,j}^{C}\right] \tag{15}\]
## 3 MPTCP Congestion Control Background
### Opportunistic Linked Increases Algorithm (OLIA)  
OLIA aims to improve performance in changing network conditions. Compared to LIA, the algorithm increases the sending rate more aggressively, leading to the faster use of network resources. It also uses an opportunistic approach to flow control by utilizing the available bandwidth across multiple network paths to achieve higher throughput than regular TCP. Additionally, OLIA has a probing mechanism that allows the sender to evaluate the bandwidth on various paths. This information is then used to adjust the sending rate in real time.  
OLIA uses a congestion control algorithm for all subflows and provides TCP-Reno's response to packet loss. It adopts a slightly modified TCP slow start algorithm to avoid sending traffic over congested paths. OLIA lowers the slow start threshold to one maximum segment size when multiple paths are available, preventing the overloading of any particular path with traffic [39].  
\[\frac{\frac{C_{i}}{RTT_{i}}}{\left(\sum_{j}C_{j}\right)^{2}}+\frac{\lambda_{i} }{CW_{i}}. \tag{3}\]  
The first term in (3) incorporates TCP-compatible resource pooling, congestion balancing, and fairness, whereas the second term uses the parameter \(\lambda_{i}\) to ensure responsiveness and prevent oscillations. The congestion window increase algorithm is responsive and adaptive to changing network conditions because OLIA adjusts \(\lambda_{i}\) based on the availability of subflows with smaller congestion window sizes. This adaptive behavior improves network performance and ensures that resources are used efficiently [39].
# Advances in Three Dimensional Bioprinting for Wound Healing: A Comprehensive Review
## 4 3D Bioprinting Techniques
### Other Techniques
#### 4.4.4. In Situ Techniques  
In situ 3D bioprinting technology relies on emulating human physiology to bioprint tissue with minimal damage to body tissues. In situ 3D bioprinting technology has been described as a new system for highly personalized medicine [217].  
There are two main options for in situ 3D bioprinting technology. The first is the portable device-based approach that allows direct printing. In this method, thanks to the small size of the system, it moves easily in and around the wound area and directly presses the defect area. This system is preferred due to its sterile operation and low cost compared to other technologies [218; 219]. The second system is a specialist-controlled approximation of the X, Y, and Z axes [220]. The texture to be printed is designed in a digital file format. The device, ready for printing in a controlled manner by the specialist, can print many bioinks with different formulations in the same system. The location of the defect within the complex structure can be easily printed with this system simulating complex tissues and implants [221; 222].  
With on-site 3D bioprinting, it is challenging to print personalized dressings in the defect area. The creation of the printing product in compliance with the morphology structure, and the selection of optimum conditions, are the main drawbacks that limit the wide use of the in situ 3D technology [223].  
CH hydrogels are becoming increasingly popular for their biocompatibility, antibacterial activity, biodegradability, and their cost; however, they occasionally involve toxic and organic solvents in their composition. To address this problem, a study was conducted for manufacturing high-strength (2.31 MPa) CH hydrogels via DIW [224]. The researchers produced an ink wherein CH was dissolved in an alkaline aqueous solution that presents considerable potential in tissue engineering. Another research inquiry showed the efficacy of VEGF-containing bioprinted GelMA hydrogels in treating full-thickness wounds in a pig model. When compared to empty GelMA and topical VEGF therapy, wounds treated with VEGF-releasing GelMA showed less shrinkage (18 \(\pm\) 4%) and a lower SEI (scar elevation index) (1.09 \(\pm\) 0.01). The number of rete ridges and the neeepidermis thickness were greater in wounds treated with VEGF-releasing GelMA, compared to empty GelMA and topical VEGF therapy. This work established in vitro and in vivo bioprinting of VEGF-loaded GelMA hydrogel [225].
## 2 Results and Discussion
### Evaluation of Catalytic Performance  
#### 2.2.1 Catalyst Preparation Condition Optimization  
To investigate the impact of P-BVIMCl preparation conditions on CO\({}_{2}\) catalytic performance, we synthesized a series of poly(ionic liquid) catalysts by adjusting various synthesis parameters, including solvent type, reaction temperature, reaction time, and the amount of AIBN used. Research has found that the amount of initiator AIBN and the duration of polymerization have a negligible effect on the yield of the cycloaddition reaction (Figure 5a,b). As shown in Figure 5c, using the binuclear imidazole-based poly(ionic liquid) as the catalyst, the study revealed that both the amount of initiator AIBN and the polymerization time exhibited negligible effects on the yield of GLC. When varying polymerization temperatures, self-polymerization failed at 80 \({}^{\circ}\)C. Increasing the temperature enabled polymer formation, with an increase in GLC yield. When the temperature reached 100 \({}^{\circ}\)C, the yield had already exceeded 90%. Further temperature elevation showed no significant yield improvement, indicating equilibrium attainment at 100 \({}^{\circ}\)C.  
For solvent effects, water/ethanol mixtures demonstrated optimal CO\({}_{2}\) catalytic performance (Figure 5e). Systematic investigation of water/ethanol ratios revealed no product formation at a 1:1 (\(v/v\)) ratio. The yield of GLC initially increased and then decreased with increasing ethanol proportion, achieving the highest yield of GLC of 92.6% at a 1:3 (\(v/v\)) water/ethanol ratio. These results established the optimal polymerization conditions as follows: 100 \({}^{\circ}\)C, 24 h, water/ethanol = 1:3 (\(v/v\)), and 10 wt% AIBN initiator, obtaining 92.6% conversion of glycidol (GO) and 99.0% selectivity of GLC (Figure 5d).  
Additionally, in order to compare the effects of self-polymerization and copolymerization with DVB on ion density and catalytic performance, the CO\({}_{2}\) catalytic performance of the copolymerization of BVIMCl with divinylbenzene (DVB) at a 1:1 molar ratio and P-BVIMCl was evaluated. Remarkably, the poly(ionic liquid) copolymerized with BVIMCl and divinylbenzene (DVB) at a 1:1 molar ratio under optimal conditions showed inferior catalytic performance compared to BVIMCl self-polymerization. This reduction was attributed to the decrease in ionic density and the diminishment of active sites caused by DVB incorporation, consequently lowering CO\({}_{2}\) cycloaddition efficiency (Figure 5f).  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \multirow{2}{*}{**Catalyst**} & **S\({}_{\text{BET}}\) (m\({}^{2}\cdot\)g\({}^{-1}\))** & **V\({}_{\text{P}}\) (cm\({}^{3}\cdot\)g\({}^{-1}\))** & **D\({}_{\text{ave}}\) (cm\({}^{3}\cdot\)g\({}^{-1}\))** & **CO\({}_{2}\) Adsorption (cm\({}^{3}\cdot\)g\({}^{-1}\))** \\ \hline P-BVIMCl & 6.0164 & 0.02855 & 3.72 & 1.853 \\ \hline \end{tabular}
\end{table}
Table 1: Textural properties and CO\({}_{2}\) adsorption capacities of P-BVIMCl.
# Microbial and Enzymatic Biodegradation of Plastic Waste for a Circular Economy
## 2 Microbial and Enzyme-Based Degradation of Plastic
### Enzymatic Degradation of Polymers
#### 2.3.2 Lipase-Catalysed Depolymerisation of Plastic  
Lipase enzymes, like cutinases, catalyse the hydrolysis of ester bonds in triglycerides to liberate fatty acids [113]. These enzymes have been identified as a sustainable and effective means for both the synthesis and degradation of polymers. Lipases can be used to degrade different types of plastics, particularly biodegradable plastics [114]. For example, a lipase isolated from _Rhizomucor miehei_ (formerly _Mucor miehei_) was used to efficiently degrade synthetic polymers such as PC and PET in an aqueous medium at 37 \({}^{\circ}\)C [115]. Additionally, a lipase from _Cryptococcus_ sp. can degrade biodegradable polymers such as polybutylene succinate (PBS) sheets of 200 \(\upmu\)m in 72 h when incubated in 100 mM phosphate buffer, pH 7.0 at 30 \({}^{\circ}\)C [116].  
Furthermore, a lipase extracted from _Pseudomonas cepacian_ caused 80% degradation of PBS in less than 24 h when incubated at 50 \({}^{\circ}\)C [117]. The same lipase showed greater degradation of PBS sheets when shaking (rather than under static conditions) at 37 \({}^{\circ}\)C in phosphate buffer pH 7.2-7.4, suggesting that shaking enhances mass transfer efficiency during the degradation process [118]. _Candidia antarctica_ lipase B was chemically modified to enhance its stability and activity. This modified lipase showed the ability to depolymerise PLA into its monomers within 40 h at 90 \({}^{\circ}\)C [119].  
Deep eutectic solvents (DESs) are mixtures of a hydrogen donor and acceptor which can create a favorable environment for protein chemical modification. Few studies have been conducted on the use of DESs to enhance enzymatic plastic degradation. One such study used the _Humicola insolens_ cutinase (HiC) in a DES environment for PET degradation and showed that the DES solution increased the hydrolysis yield by 1.5 times [120]. DESs are promising for the stabilisation of enzymes and enhancement of their catalytic activity [121]. This approach can be beneficial for efficient enzymatic degradation processes and can be scaled up for industrial application.
###### Abstract  
Thermal barrier coatings (TBCs) are effective protective and insulative coatings on hot section components of turbine engines. The quality and subsequent performance of the TBCs are strongly dependent on the adhesion between the coating and the metal substrate. The adhesion strength of TBCs varies depending on the substrate materials and coating, the coating technique used, the coating application parameters, the substrate surface treatments, and environmental conditions. Therefore, the roughness of the substrate surface has a significant effect on the performance of the TBC system. In this work, the roughness and microstructure of the 7 YSZ (7 wt.% yttria-stabilised zirconia) top coat under different bond coat roughness treatments were studied. The purpose of this paper was to investigate the influence of the roughness of the bond coat on the adhesion of 7 YSZ TBCs prepared by the electron beam-physical vapour deposition (EB-PVD) process. The VPA (vapour phase aluminium) bond coat was deposited on Inconel 718 nickel superalloy substrate using the above-the-pack technique. The ceramic top coat was applied to the bond coat using the EB-PVD process. The dependence between the TBC coating roughness and the bond coat roughness was determined. Adhesion strength measurements were performed according to the ASTM C 633 standard test method. The highest adhesion value observed in the tensile adhesion tests was 105 MPa. However, it was not determined whether the surface roughness of the bond coat affects the adhesion of the 7 YSZ top coat.  
thermal barrier coatings; electron beam-physical vapour deposition; EB-PVD; surface roughness; adhesion Article
# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 4 Materials and Methods
### Implementation Details  
To thoroughly evaluate the effectiveness of our UC-CMAF framework, we adopt a structured experimental pipeline. This includes data preprocessing, multimodal feature extraction from news content (text and image) and user comments, cross-modal fusion using attention mechanisms, and model training with evaluation on benchmark datasets. Each step is designed with reproducibility and clarity in mind, and detailed settings are provided to ensure transparent experimental procedures.  
Our multimodal embedding method employs carefully selected pre-trained models and architectures for feature extraction and fusion. For text processing, we utilize the pre-trained BERT-base-uncased model to extract 768-dimensional text features, with maximum sequence lengths set to 512 tokens for news articles and 128 tokens for comments, using a vocabulary of 30,522 tokens. Visual processing adopts ResNet-50 for feature extraction, reducing 2048-dimensional features to 768 dimensions through 2D convolution to maintain dimensional consistency. Image preprocessing includes resizing to \(224\times 224\) pixels and normalization using ImageNet statistics, with data augmentation applied during training through random horizontal flipping and color jittering. For comment processing, we select up to 50 comments per article (20 for the MMCoVaR dataset), with comments filtered by minimum length of 10 characters and maximum length of 280 characters, while preserving the temporal order of all comments. For user comments, we perform a multi-step preprocessing pipeline: (1) removal of non-linguistic content such as URLs, emojis, and special characters; (2) filtering of comments that are too short or too long; (3) BERT-based tokenization and normalization; and (4) retention of timestamp metadata for use in the temporal importance weighting module. This ensures that only informative and high-quality comments are incorporated into the model. The model is trained using the Adam optimizer with a learning rate of 0.001, a batch size of 64, and for 200 epochs. These settings are selected via cross-validation to ensure a balance between convergence speed and generalization. Xavier initialization is applied to stabilize training dynamics. For the ReCOVery and MMCoVaR datasets, we randomly sample up to 50 and 20 comments per article, respectively, padding with empty comments when necessary. The fusion weight parameter \(\lambda\) for integrating user reviews is set to 0.8 based on experimental validation.
## 7 Outcomes
### RQ2: What Is the Performance of Anomaly Intrusion Detection in IoT Using Deep Learning Techniques?
#### 7.3.3 Time Complexity  
Time complexity is considered an obstacle because the current detection techniques were developed based on batch processing applications rather than real-time detection. As mentioned before, the IoT environment deals with real-data streaming. Hence, the time complexity is crucial in detecting threats in IoT applications. In addition, it can assess the impact on several attributes associated with security threats. Deep learning is highly capable of resolving time complexity issues in IoT by implementing GPU components to deal with real-time processing in an efficient manner [62].
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 4 Review of [PERSON]'s Scheme
### Key Agreement Phase  
The mobile device provides \(ID_{M}\) and a password (\(PW_{M}\)); computes \(B_{M}^{*}=h(h(ID_{M}\parallel r_{M})\oplus h(PW_{M}\parallel r_{M})),C_{M}^{*}=h (ID_{M}\parallel r_{1}),E_{M}^{*}=h(C_{M}\parallel B_{M}^{*}\parallel r_{1} \parallel D_{M})\), and \(F_{M}^{*}=h(E_{M}^{*}\parallel C_{M}^{*})\); and confirms \(F_{M}\)? = \(F_{M}^{*}\). If it does not match, the process is rejected; otherwise, the device picks a random number (\(r_{3}\)), records time \(T_{1}\), computes \(PK_{M}=r_{3}\cdot P\) and \(G_{M}=h(C_{M}\parallel D_{M}\parallel T_{1})\), and transmits \(\{A_{D},C_{M},G_{M},PK_{M},T_{1}\}\) to the GCS over a public channel.  
First, the GCS checks the freshness of the message (\(T_{2}-T_{1}\leq\Delta T\)); rep possesses \(ID_{M},C_{M}\), and \(D_{M}\) from the stored record; computes \(G_{M}^{*}=h(C_{M}\parallel D_{M}\parallel T_{1})\); and confirms \(G_{M}?=G_{M}^{*}\). If it does not match, the process is rejected; otherwise, it computes \(H_{DM}=D_{M}\oplus PK_{D}\) and \(I_{M}=h(A_{D}\parallel PK_{D}\parallel ID_{D}\parallel D_{M})\) and transmits \(\{I_{M},PK_{M},A_{D},C_{M},H_{DM},T_{3}\}\) to the drone over an open channel.  
Then, the drone checks the timestamp (\(T_{3}-T_{2}\leq\Delta T\)), computes \(D_{M}=H_{DM}\oplus PK_{D}\) and \(I_{M}^{*}=h(A_{D}\parallel PK_{D}\parallel ID_{D}\parallel D_{M})\), and confirms \(I_{M}^{*}=I_{M}\). If it does not match, the process is rejected; otherwise, it picks a random number (\(r_{4}\)); records timestamp \(T_{5}\); computes \(PK_{D}^{*}=r_{4}\cdot PK_{M},SK_{D}=h(ID_{D}\parallel D_{M}\parallel C_{M})\), and \(Auth_{D}=h(SK_{D}\parallel C_{M}\parallel D_{M}\parallel T_{5})\); and transmits \(\{Auth_{D},PK_{D}^{*},T_{5}\}\) to the GCS over an open channel.  
Next, the GCS checks the timestamp (\(T_{6}-T_{5}\leq\Delta T\)), computes \(SK_{GCS}=h(ID_{D}\parallel D_{M}\parallel C_{M})\) and \(Auth_{GCS}=h(SK_{D}\parallel C_{M}\parallel D_{M}\parallel T_{7})\), confirms \(Auth_{GCS}?=Auth_{M}\), and transmits \(\{Auth_{GCS},PK_{D}^{*},T_{7}\}\) to the mobile device over a public network channel.  
The mobile device first checks the timestamp (\(T_{8}-T_{7}\leq\Delta T\)), computes \(SK_{M}=h(ID_{D}\parallel D_{M}\parallel C_{M})\) and \(Auth_{M}=h(SK_{D}\parallel C_{M}\parallel D_{M}\parallel T_{7})\), and confirms \(Auth_{D}?=Auth_{M}\). The process is rejected if it does not match; otherwise, the device keeps \(SK_{D}=SK_{GCS}=SK_{M}\) as the shared secret session key, as in the key agreement phase.