esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effects of AC55541 on Pacemaker Potentials in Murine Small Intestinal ICCs
Figure 4: Effect of guanosine 5\({}^{\prime}\)-diphosphate (GDP)-ß-S on trypsin-induced pacemaker potential depolarization in murine small intestinal interstitial cells of Cajal. (**A**) Trypsin slightly depolarized pacemaker potentials after the intracellular application of GDP-ß-S (1 mM). (**B,C**) Responses to trypsin in the presence of GDP-ß-S are summarized. Bars represent mean \(\pm\) standard of mean. **\(p\)** \(<\) 0.01 compared with the non-treated controls. CTRL, control. Figure 5: Effects of extracellular and intracellular Ca\({}^{2+}\) regulation on trypsin-induced pacemaker potential depolarization in murine small intestinal interstitial cells of Cajal. (**A**) Tryppsin slightly depolarized pacemaker potentials under external Ca\({}^{2+}\)-free conditions. (**B**) Trypsin depolarized pacemaker potentials in the presence of thapsigargin. (**C**,**D**) Responses to trypsin under Ca\({}^{2+}\)-free conditions and in the presence of thapsigargin are summarized. Bars represent mean \(\pm\) standard of mean. *\(p\) < 0.01 compared with the non-treated controls. CTRL, control. Thapsi., thapsigargin. Figure 6: Effects of U-73122 (an active phospholipase C inhibitor), staurosporine (a broad-spectrum protein kinase C (PKC) inhibitor), Go6976 (a calcium-dependent PKC \(\alpha\)/\(\beta\) inhibitor), rottlerin (a calcium-independent PKC \(\delta\) inhibitor), and xestospongin C (a blocker of inositol triphosphate receptor) on trypsin-induced pacemaker potential depolarization in murine small intestinal interstitial cells of Cajal. (**A–C**) Trypsin did not depolarize pacemaker potentials in the presence of U-73122, staurosporine, and Go6976. (**D**) In the presence of rottlerin, trypsin depolarized the pacemaker potentials. (**E**) Trypsin did not depolarize pacemaker potentials in the presence of xestospongin C. Responses to trypsin in the presence of U-73122, staurosporine, Go6976, rottlerin, and xestospongin C are summarized in (**F**,**G**). Bars represent mean \(\pm\) standard of mean. **\(p\) < 0.01, compared with the non-treated controls. CTRL, control; U73., U-73122; Stauro., staurosporine; Go., Go6976; Rot., rottlerin; Xesto., xestospongin C.  
TRPV4 Is Not Involved in Trypsin-Induced Pacemaker Potential Depolarization in Murine Small Intestinal ICCs  
The transient receptor potential (TRP) vanilla 4 (TRPV4) ion channel is one of the major downstream targets of PAR2 [38,39]. The coupling of PAR2 and TRPV4 is required for sustained inflammatory signaling [40]. Therefore, the role of TRPV4 channels in trypsin-induced pacemaker potential depolarization was investigated using HC067047 (a TRPV4 inhibitor). In the presence of HC067047 (100 nM), trypsin depolarized the pacemaker potentials (Figure 7A). The mean degree of depolarization and mean amplitude in the presence of HC067047 were 23.5 \(\pm\) 1.2 (Figure 7B, \(n\) = 8) and 2.3 \(\pm\) 0.8 mV (Figure 7C, \(n\) = 8), respectively. These results suggest that TRPV4 is not involved in trypsin-induced pacemaker potential depolarization in murine small intestinal ICCs.
## 3 System Model
### Analysis of Inference Process  
Next, we explain how to split complex DNN models whose structures are not strictly linear.  
Figure 3: A collaborative inference example.  
Modern DNNs are not simple linear structures but rather DAGs. A DAG is a representation of a graph composed of a set of vertices and directed edges, with no cycles. In a DAG, each directed edge has a direction, pointing from one vertex to another, and there are no paths that start at a vertex, traverse through several edges and return to the same vertex. The structure of a DNN model can be modeled as a DAG. In a DNN, each node represents a neuron or computing units, and directed edges represent the connections between neurons.  
To achieve model partitioning and parallel computation, we first use DAG traversal to find nodes that can be split. DAG traversal involves sequentially traversing nodes in a computational graph to determine the execution order and the dependency relationships of the computations. In DL, DAG traversal is commonly used in the forward- and backward-propagation processes of the model to compute the output values and gradients of each node. As shown in Figure 4, we adopt the current mainstream and intuitive approach, using traversal to find model split points and treating the layers between two split points as a logical layer. This approach works well for models with residual blocks, such as ResNet or PANet.  
Currently, some researchers consider not transforming the DAG into a chain but decomposing it into a set of execution units [24]. In that way, edge-computing nodes are responsible only for executing specific operations, and results are sent to the corresponding targets through forwarding tables. However, this scheduling method would introduce significant network overhead, and for our LEO satellite network scenario, such network overhead may even exceed the DNN inference time, which is unacceptable. Therefore, we did not use this method and instead opted for partitioning based on logical layers.  
Figure 4: Merge branches into logical sub-layers.  
Moreover, this logical layer-based partitioning method simplifies the complexity of our subsequent algorithms.
# Evaluating the Efficiency of Physical and Cryptographic Security Solutions for Quantum Immune IoT
## 3 Quantum Immune Security Solutions
### Quantum Immune Cryptography  
Quantum immune cryptographic solutions address both passive and active adversaries, with quantum capabilities. Research on cryptographic algorithms that are resistant against quantum computers fall into six categories that are presented in Table 1. We focus on the lattice-based cryptography [28; 29] that is a prominent candidate for IoT, as it provides good performance in addition to immunity against classical and quantum cryptanalysis. Lattice-based constructs have also been used to achieve fully homomorphic encryption [30]. A lattice is a set of points in an n-dimensional space with a periodic structure. The security of lattice-based cryptography depends on the assumption that particular lattice problems cannot be solved efficiently with classical or quantum computers. A foundational problem in lattice-based cryptography was the Shortest Vector Problem [28], i.e., finding a non-zero lattice vector with minimal Euclidean length. Recent key exchange protocols have applied lattice-based algorithm variations: Learning with Errors (LWE) and Ring Learning with Errors (R-LWE) problems [36]. Prominent implementations derived from these problems include:  
* NewHope [5] is a recent implementation of a key exchange protocol based on the R-LWE problem. An early version of the protocol was described by [PERSON], [PERSON] and [PERSON] [37] and improved by [PERSON] [38]. [PERSON] et al. [16] implemented the first version of the protocol. NewHope optimized the implementation with new parameters and a better error distribution. The optimized NewHope implementation outperforms the classical Elliptic Curve Diffie-Hellman key exchange (having around a 20% faster Transmission Layer Security (TLS) handshake) [39]. The algorithm has
## 2 Result and Discussions
### Transformation of Cells by Plasmids Expressing P, Complementation and Immunity Assays
#### 2.1.1 \"Trans-activation\" of P  
The gene \(P\) function of an \(imm\lambda\) prophage was previously shown to be _trans_-activatable [37,38], i.e. cells with a repressed \(\lambda\) prophage could fully complement and support the vegetative growth of an infecting \(imm434\) phage defective for \(P\) (by a factor of \(10^{3}\)), even though the expression of the prophage copy of \(P\) was repressed by the \(imm\lambda\) CI repressor blocking \(pR\) transcription in prophage with both \(oL\) and \(oR\) sites. [PERSON] [38] argued that \"a small but significant amount of P product was synthesized by the prophage\", which he rationalized as being explained by the existence of a minor constitutive promoter mapping between \(pR\) and \(P\) on the prophage genome. We saw _trans_-activation of \(P\) from repressed pcIpR-\(P\)-imm (slightly at 25 \({}^{\circ}\)C and nearly full complementation at 30 \({}^{\circ}\)C), which is explained by leaky transcription from \(pR\), since there is no room for an unaccounted promoter upstream of \(P\) as only 18 bases, including the ribosomal binding site, separates the start site for \(pR\) mRNA transcription and the AUG for \(P\). We conclude that trace, certainly not full, \(P\) expression is sufficient for \(ori\lambda\) dependent replication initiation.
## 3 Results and Discussion
### Created VR Application for Rehabilitation
#### 3.1.3 Motivation Enhancement  
The proposed solution takes the form of a game in which points can be earned (Figure 3d). The points and other statistics in the application have been written in Polish so that they can be understood by the potential group of patients using the game for rehabilitation. With the collection of points, not only can the patient's progress be measurably tracked but it is also an additional motivating factor for exercise. Studies show increased motivation when using gamification in immersive VR for gait rehabilitation [52], as well as for upper limb rehabilitation after stroke [53].
## 2 Results and Discussions
### Time Resolved Faraday Rotation
The demagnetization of the two sublattices is then allowed to proceed independently and scales with the very different magnetic moments of Fe (\(\mu_{\text{at}}\approx 2.22\ \mu_{\text{B}}\)) and Gd (\(\mu_{\text{at}}\approx 7.55\ \mu_{\text{B}}\)) [44]. As mentioned above, distinct sublattices with a different ultrafast magnetic response are a prerequisite for the formation of a transient ferromagnetic alignment of the two sublattices and, hence, for the occurrence of all-optical magnetic switching [45]. There are two more aspects which are noteworthy: first, as we qualitatively confirm the results of the FeGd demagnetization dynamics carried out with the established technique of MCD in the soft-X-ray range [10; 44; 46], we conclude that ultrafast Faraday rotation is indeed an accurate observable for the transient magnetization. Second, a lot of research in resonant XUV spectroscopy has recently been devoted to exploring a potential influence of transient changes in the electro-optical functions \(\beta(t)\) and \(\delta(t)\) to MCD signals [26; 29; 34; 47; 48; 49]. Transient changes in the transmission, \(T\)[18], or reflection [50] at a core-valence band transition have been shown to be on the order of a few percent, and hence of a similar magnitude compared to the changes in MCD signals in the XUV spectral range [38]. This is different for the Faraday rotation. On the one hand, we observe very large asymmetries of up to 40% and, on the other hand, small relative changes in the transmission, \(T\), have a negligible influence on \((I_{+}-I_{-})\) for \(\alpha\ll 1\), as is easily verified by inspection of Equation (4). Therefore, non-equilibrium electron distributions are expected to have an insignificant effect on the extracted magnetization dynamics.
## 4 Video Application Based on Deep Reinforcement Learning Algorithm  
Deep reinforcement learning is a combination of deep learning and reinforcement learning. With deep learning [26], through a gradual, layer-by-layer process, low-level data input is transformed into high-level abstract feature representations through nonlinear transformations, and deep learning pays more attention to the perception and expression of environmental feedback datasets. Reinforcement learning [61] obtains the optimal strategy to maximize the cumulative reward through the interaction between the agent and the environment. It focuses more on the strategies to deal with the problems through autonomous feedback learning. Deep reinforcement learning converts high-dimensional datasets of environmental feedback into abstract state features with the help of deep learning. Then, the optimal strategy is obtained with the help of reinforcement learning autonomous learning, which is used to solve the strategy problem in a complex environment.  
In the video application environment over CVIS, the complexity of the environment is influenced by traffic flow, the speed of the vehicle, wireless channel resources, signal strength and so on. The environmental state space is often high-dimensional, and traditional reinforcement learning algorithms have difficulty dealing with high-dimensional state space and continuous action space. With the performance of deep learning in high-dimensional data input processing, the above problems can be easily solved. Therefore, deep reinforcement learning has gradually become the most-covered direction of researchers in the field of video transmission under CVIS. According to the solution strategies based on different methods and numbers of agents, they are divided into the following three categories:  
1. Deep reinforcement learning algorithm based on value function: the algorithm mainly evaluates the Q-value generated by all actions, selects the action according to the Q-value and obtains the optimal strategy indirectly through the value function;
2. Policy gradient algorithm: the algorithm parameterizes the strategy, uses the weight parameters of the depth neural network to represent the strategy, optimizes the strategy through the gradient method and constantly modifies the parameters and gradually obtains the optimal strategy. The policy gradient algorithm is an algorithm to solve the optimal policy directly;
3. Multi-agent deep reinforcement learning: multiple agents choose the corresponding actions according to the current environment, establish different reward functions according to the relationship between agents and solve the optimal strategy.  
Figure 5: Multi-task learning model framework.  
Through the above three ways, this paper divided and classified the video application algorithms over CVIS based on deep reinforcement learning, and its characteristics and advantages were summarized.
# Silver Nanoparticles-Loaded Exfoliated Graphite and Its Anti-Bacterial Performance
## 3 Results and Discussion
### Inhibition Ring Test and Anti-Adhesion Effect of Bacteria  
We synthesized silver nanoparticles-loaded exfoliated graphite with different Ag loading by using different concentrations of AgNO\({}_{3}\). The silver content of samples was analyzed by ICP-AES. The results are summarized in Table 1.  
The anti-bacterial activity of the Ag-EG against Gram negative bacteria _P. aeruginosa_ and Gram positive bacteria _S. aureus_ was evaluated using the zone of inhibition method. In nutrient agar plates containing different Ag-EG that were made into round pieces, there are four of the same round pieces on every plate to decrease errors. Figures 5 and 6 show that the expanded graphite without Ag nanoparticles did not show any antibacterial properties, while the others exhibited different antibacterial activities. The antimicrobial mechanism of silver nanoparticles is not completely understood. The most accepted mechanism is related to the fact that silver nanoparticles can release Ag\({}^{+}\) ions, which may affect the metabolic processes and the mechanism of cell division, causing serious problems in the membrane permeability, resulting in the death of the bacteria [40; 41].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline Concentrations of AgNO\({}_{3}\)/10\({}^{-3}\) mol\(\cdot\)dm\({}^{-3}\) & 0.3125 & 0.625 & 1.25 & 2.5 & 5 & 20 & 80 \\ \hline \hline \multicolumn{1}{c}{Siver content/\%} & 0.071 & 0.16 & 0.38 & 0.73 & 1.26 & 3.75 & 8.24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sliver content of Ag-EG synthesized with different concentrations of AgNO\({}_{3}\).  
Figure 7 shows the influence of silver loading on the inhibition zones. Both two curves showed the diameters of the inhibition zones increased with the increasing Ag content when the Ag content was less than 1.26% for _P. aeruginosa_, 3.75% for _S. aureus_, but almost did not change when the content continued to increase. It was also observed that _P. aeruginosa_ was comparatively more sensitive to the Ag-EG and produced larger growth inhibition zones. In high content, the agglomeration of Ag nanoparticles meant that it was harder to release Ag\({}^{+}\). The experiment showed that Ag-EG has anti-bacterial activity against _P. aeruginosa_ and _S. aureus_, and the Ag content is much less than that of Ag-GO [(33; 42)].  
Figure 8 shows the adhesion of _P. aeruginosa_ and _S. aureus_ on the surface of the EG and Ag-EG after incubating with washed bacteria for 2 h and 12 h. There were no bacterial colonies around Ag-EG round pieces after either incubating time. In contrast, some bacterial colonies grew around EG round pieces. This phenomenon was observed in both _P. aeruginosa_ and _S. aureus_. Hence, the Ag-EG material exhibited significant improvement in the property of anti-bacterial adhesion. This experiment demonstrated that Ag-EG material can be used in a variety of large scale industrial applications that require materials with long term antibacterial and anti-adhesion properties.
## 5 Experimental Validation and Benchmarks
### Applied Solutions in Real-World Networks  
In addition to theoretical exploration, I tested the proposed AI solutions in several real-world communication networks to validate their effectiveness and practical applicability.  
Figure 8: Control experiments isolating latency and energy consumption for AI models. Each point represents the trade-offs observed for a specific AI approach, highlighting the computational demands and energy efficiency.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Latency (ms)** & **Energy Consumption (J)** \\ \hline Deep Learning & 95 & 50 & 1.5 \\ Reinforcement Learning & 92 & 55 & 1.8 \\ Decision Tree & 85 & 35 & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance benchmarks for AI models in traffic prediction.  
These applications demonstrate the feasibility of deploying AI-driven models in dynamic, resource-constrained environments and their ability to meet stringent requirements, such as privacy, scalability, and real-time performance.
## 3 Results and Discussion
### Ionic Liquid-Based Membranes Applied to Wastewater Treatment  
As stated in the Materials and Methods section, a polymer inclusion membrane based on the ionic liquid [MTOA\({}^{+}\)][Cl\({}^{-}\)] was employed as separator for the treatment of white oil refinery industry wastewater in the MFC system. The ionic liquid membrane was characterized before (fresh membrane) and after MFC operation by scanning electron microscope with the purpose of obtaining information about morphology, topography and chemical composition. Figure 2A,B show the SEM micrographs of the [MTOA\({}^{+}\)][Cl\({}^{-}\)] membrane before and after use in MFCs, respectively. The fresh membrane offers a homogenous appearance with no porosity observable. The surface of the membrane is virtually smooth and clean. Figure 2B shows that, after being employed as a separator in the MFC system, the membrane becomes rougher due to the formation of deposits over the surface that has been in contact with the oil industry wastewater.  
Figure 3 and Table 2 (EDX normalize %wt) includes the EDX spectrum and the EDX analysis results (in %wt) of the ionic liquid membrane before and after MFC operation. In Figure 3, a reduction of the Cl content of the membrane is observed, which, according to the EDX analysis, corresponds to a percentage of 3.28%. Given that the Cl content of the membrane stems from the ionic liquid composition and PVC (polymer matrix), it can be concluded that a certain amount of the ionic liquid has been released from the membrane to the medium, since PVC is recognized as an insoluble polymer.  
Figure 2: Scanning electron micrograph of the membrane with [MTOA\({}^{+}\)][Cl\({}^{-}\)] ionic liquid after being used in MFC with white oil refinery industry wastewater.  
The same applies to the content of the nitrogen, although, in this case, the reduction percentage is higher, since nitrogen is not present in the polymer but only in the ionic liquid. The stability of polymer ionic liquid membranes based on [MTOA\({}^{+}\)][Cl\({}^{-}\)] in contact with water has been previously studied [9], showing that they are capable of retaining an important amount of ionic liquid after long-time water contact. As noted before, the membrane showed that some deposits were formed over the membrane surface (Figure 2). After MFC operation, new chemical elements (S, Ca, Fe, Zn) were found absorbed in the membranes, which would come from the white oil refinery wastewater used as fuel. In addition, higher concentrations of oxygen and carbon concentration were found in the used membranes compared with their concentration in the fresh separator. Both findings could be related to the deposition of CO\({}_{2}\) generated during the microbial activity on the membrane.
## 6 UOWC Security Concerns and Challenges and Future Directions
### Challenges and Future Directions  
The UOWC technology has made significant progress in recent years, achieving remarkable feats. However, in many areas, this field of research is still in its infancy, and several challenges remain unresolved. Field research and simulators are necessary to gain the best insight into the underwater environment and channel simulation. This section discusses several potential research problems with UOWC channel simulation platforms. Firstly, most UOWC simulation studies do not account for all the underwater impairments or events that photons may encounter together when propagating through the water. These impairments include absorption, single-scattering events (forward, backward, isotropic), multiple-scattering events, and no event when photons directly reach the receiver plane. Available simulation platforms need to study the integration of all these impairments in underwater channels simultaneously. Numerous research projects have been conducted to simulate the UOWC channel assuming a homogeneous medium, where the ocean's components have uniform characteristics across its volume. However, the ocean's characteristics change with depth for the vertical link, resulting in medium inhomogeneity. Previous researchers have neglected the impact of medium inhomogeneity. More research is needed to incorporate the medium
## 3 Key Technologies of UAV-Assisted SAGINs
### Deployment of UAVs
#### 3.5.2 Anti-Interference  
In interference scenarios, UAV communication channels are prone to degradation, increasing network latency and reducing throughput. [PERSON] et al. [132] considered real-time latency and backhaul attenuation issues during UAV deployment. Additionally, co-channel interference diminishes communication quality between UAVs and ground base stations. [PERSON] et al. [133] employed mean-shift techniques and GPS-provided user location data to determine the Cartesian coordinates of UAVs and optimized their altitude and transmission power using block coordinate descent techniques. [PERSON] et al. [134] optimized 3D UAV deployment to continuously mitigate interference rather than addressing it in a one-off manner. To enhance UAVs' autonomous anti-interference capabilities, [PERSON] et al. [135] proposed an adaptive deployment strategy based on reinforcement learning (RL) and used QoE optimization to enhance user experience in UAV deployment.