esa-sceva/satcom-chunk-collection huggingface dataset:
# AI Agents Meet Blockchain: A Survey on Secure and Scalable Collaboration for Multi-Agents
## 3 Preliminaries
### Decentralized AI  
Decentralized AI (DeAI) employs blockchain technology to solve the constraints of centralized AI (CeAI) systems, such as single points of failure, data privacy problems, and scalability bottlenecks. CeAI frequently aggregates control over data and computation, which results in risks such as technological failures, cyberattacks, and biases in model outputs [22; 59]. DeAI addressed these issues by implementing techniques such as immutable audit trails, privacy-preserving methodologies like zero-knowledge proofs (ZKPs), and tokenized reward systems for contributors [22]. Collaborative frameworks such as federated learning are enhanced by blockchain consensus processes, allowing decentralized model training and validation independent of central authority. Platforms like Ocean Protocol [60] and Render Network [61] demonstrated the power of DeAI by cultivating collaborative ecosystems for the secure and transparent exchange of data, models, and computing resources. DeAI faces challenges such as computing overhead, scalability limitations, and the need to balance transparency with privacy. Resolving these issues demands advancements in cryptographic methodologies, efficient consensus protocols, and decentralized governance frameworks [62]. The applications of DeAI in healthcare, autonomous systems, and decentralized markets illustrate its innovative potential to create ethical, safe, and diverse AI systems. A DeAI application's lifetime is divided into the following five stages [22]:  
* Task Proposing: In this stage, the algorithms are prepared to guarantee that they satisfy the needs of decentralized systems in terms of privacy, scalability, and effective communication. The development of algorithms is essential at this phase to provide the basis for its entire life cycle.
* Pre-Training: Data are gathered, cleaned, and segmented while ensuring privacy and security. Concurrently, computing resources such as GPUs or distributed systems are configured to facilitate efficient training.
* On-Training: In this phase, model training and validation take place. Collaborating nodes concurrently update model parameters while maintaining synchronization. Validation guarantees the model's precision and generalizability among all participants.
* Post-Training: The trained model is ready for deployment and integration into applications for practical usage. This phase also enables model sharing via decentralized markets.
###### Abstract  
In this report, protein of hairtail (_Trichiurus japonicus_) muscle was separately hydrolyzed using five kinds of proteases (alcalase, trypsin, neutralse, pepsin, and papain), and the papain- and alcalase-hydrolysates showed higher 2,2-diphenyl-l-1-picrylhydrazyl radicals (DPPH\(\bullet\)) and hydroxyl radical (HO\(\bullet\)) scavenging activity than other three protease hydrolysates. Therefore, the protein hydrolysate of hairtail muscle (HTP) was prepared using binary-enzymes hydrolysis process (papain + alcalase). Subsequently, eight antioxidant peptides were purified from HTP using membrane ultrafiltration and chromatography technology, and their amino acid sequences were identified as Gln-Asn-Asp-Glu-Arg (TJP1), Lys-Ser (TJP2), Lys-Ala (TJP3), Ala-Lys-Gly (TJP4), Thr-Lys-Ala (TJP5), Val-Lys (TJP6), Met-Lys (TJP7), and Ile-Tyr-Gly (TJP8) with molecular weights of 660.3, 233.0, 217.1, 274.1, 318.0, 245.1, 277.0, and 351.0 Da, respectively. TJP3, TJP4, and TJP8 exhibited strong scavenging activities on DPPH\(\bullet\) (EC\({}_{50}\) 0.902, 0.626, and 0.663 mg/mL, respectively), HO\(\bullet\) (EC\({}_{50}\) 1.740, 2.378, and 2.498 mg/mL, respectively), superoxide anion radical (EC\({}_{50}\) 2.082, 2.538, and 1.355 mg/mL, respectively), and 2,2'-azino-bis-3-ethylbenzothazoline-6-sulfonic acid (ABTS) radical (EC\({}_{50}\) 1.652, 0.831, and 0.586 mg/mL, respectively). Moreover, TJP3, TJP4, and TJP8 showed higher reducing power and inhibiting ability on lipid peroxidation in a linoleic acid model system. These results suggested that eight isolated peptides (TJP1 to TJP8), especially TJP3, TJP4, and TJP8 might serve as potential antioxidants applied in the pharmaceutical and health food industries.  
hairtail (_Trichiurus japonicus_); muscle; peptide; antioxidant activity +
Footnote †: journal:
# Mambo-DQN: Adaptively Tunes Visual SLAM Parameters Based on Historical Observation DQN
## 1 Introduction  
Visual SLAM (Simultaneous Localization and Mapping) leverages the capture and analysis of image sequences from cameras to estimate the camera position and orientation in unknown environments in real time, while simultaneously constructing 3D maps. It is widely used in fields such as augmented reality (AR), virtual reality (VR), autonomous driving, mobile robots, and drone navigation [1].  
The effectiveness of visual SLAM is partially reliant on parameter configurations, such as the number of pyramid layers, nearest neighbor threshold, initial corner point quantity, and keyframe evaluation threshold. These parameters are customarily set through expert experience and extensive experimentation in traditional methods, which is not only time-consuming but also routinely needs to be readjusted in new application scenarios. Thus, academics are engaged in exploring the implementation methods of visual SLAM adaptation [2]. References [3, 4, 5, 6] explore adaptive improvement methods for visual SLAM, which primarily highlight the self-adjustment of sensors and the refinement of feature extraction and matching, while these methods overlook the need for visual SLAM parameter adjustment in response to scene changes. Inspired by the application of deep reinforcement learning in the field of parameter adaptation, we innovatively designed a reinforcement learning method based on Mambo-DQN. The purpose of this method is to simplify the parameter adjustment of the visual SLAM system while ensuring real-time requirements, effectively improving the positioning accuracy and robustness of visual SLAM.  
Our main contributions can be summarized as follows:  
(1) We propose a method that combines Mamba-DQN with ORB-SLAM3. This method transforms the parameter adaptation problem of visual SLAM into an action decision task within deep reinforcement learning, thereby enhancing the positioning accuracy of the visual SLAM system.  
(2) We design the Mamba history observer and integrate it into the deep reinforcement learning agent to improve the decision-making quality of the agent.  
The proposed method is experimentally evaluated on the Euroc MAV and TUM-VI datasets, with the results compared with those of traditional and deep learning-based visual SLAM methods. The experimental results substantiate the efficacy of the Mamba-DQN algorithm, demonstrating that it achieves superior localization accuracy in 72% of the sequences and effectively satisfies the real-time requirements of visual SLAM.
## 2 Materials and Methods
### Motion Freeze (MF) Technique  
After 4D PET/CT images had been obtained, the MF technique was applied to reconstruct the 4D PET images [8]. The end-expiration phases (T45%, T55%, and T65%), which are the most reproducible phases in the respiration cycle, were selected for the generation of MF-PET images (PETMF-45%, PETMF-55%, and PETMF-65%). These three phases were also taken as the treatment period for respiratory-gated radiation therapy in this study.  
The MF technique is based on the optical flow method (OFM) used to perform image reconstruction [9]. The main computing equation is as below:  
\[v^{(n+1)}=v^{(n)}+\
abla f(\frac{\
abla f\cdot v^{(n)}+\frac{\partial f}{ \partial t}}{\alpha^{2}+\|\
abla f\|^{2}}) \tag{1}\]  
Terms definition:  
* \(v^{(n)}\): the average velocity of the surrounding voxels.
* \(n\): the iteration number.
* \(f\): the image intensity.
* \(\alpha\): the weighting factor.  
In this study, calculation of \(v\) was divided into the three-dimensional displacements of the voxels in the directions of head to feet, front to back, and left to right. In general, during reconstruction of MF PET images, a reference phase is first selected, which is normally the most reproducible phase, that is, the end-expiration phase (T55%). However, we did not use MF-PET imaging to delineate the tumor volume. In order to define the respiratory-gated window in the radiation therapy, three phases, T45%, T55%, and T65%, were used for MF image reconstruction.
## 3 Controller Design Based on Legendre Polynomial FAT
### Stability Analysis of the Proposed Method
\begin{array}{rl}s^{T}k_{d}\varepsilon_{m}-s^{T}\rho sign(s)&=k_{d}\Big{[}s ^{T}\varepsilon_{m}-s^{T}\delta sign(s)\Big{]}\\ &\leq k_{d}|s|(|\varepsilon_{m}|-\delta)\leq 0\end{array}  \tag{29}\]  
(b) From the sliding mode manifold \(s\), \(E=[e\ \dot{e}]^{T}\) and positive definite matrices \(P_{0}\), it indicates that \(sign(s)=sign(E^{T}P_{0}B)\) and we can be rewritten as  
\[ \begin{array}{rl}E^{T}P_{0}B\varepsilon_{m}-E^{T}P_{0}Bk_{d}{}^{-1}\rho sign (s)&=E^{T}P_{0}B\varepsilon_{m}-E^{T}P_{0}Bk_{d}{}^{-1}\rho sign(E^{T}P_{0}B) \\ &\leq\Big{|}E^{T}P_{0}B\Big{|}(|\varepsilon_{m}|-k_{d}{}^{-1}\rho)\\ &\leq\Big{|}E^{T}P_{0}B\Big{|}(|\varepsilon_{m}|-\delta)\leq 0\end{array}  \tag{30}\]  
According to the above proof (i.e., (a) and (b)), it can be obtain that \(V_{2}\leq 0\). Meanwhile, from the Equations (27)-(30), yields  
\[\dot{V}=-\frac{1}{2}E^{T}QE+V_{1}+V_{2}\leq-\frac{1}{2}E^{T}QE \tag{31}\]  
By integrating inequality Equation (31), it indicates \(\dot{V}\leq 0\), and then we can get \(s\), \(E\), \(P\), and \(\rho\) are bounded. Then, it will prove that \(s\) and \(E\) will be uniformly asymptotically convergence. Since \(\dot{V}\leq 0\) is semi-negative, let \(\Delta(t)=-\frac{1}{2}E^{T}QE\). Then, integrating inequality Equation (31) on the interval [0, \(t\)], it can obtained that \(\int_{0}^{t}\Delta(t)dt\leq V(0)\) is bounded, this indicates \(\int_{0}^{t}\Delta(t)dt\) has a finite limit, as \(t\rightarrow+\infty\). Due to \(E\) is bounded, so it has that \(\dot{E}\) is bounded, and further conclude \(\Delta(t)\) is bounded. Then, it can be obtained that \(\Delta(t)\) is continuous with respect to \(t\). Based on Lemma 1, we can have \(\Delta(t)\to 0\) as \(t\rightarrow\infty\), which implies that \(s\) and \(E\) are equal to zero. As a result, it can be proved that the tracking error \(e\) and \(s\) are uniformly asymptotic converged and stable.
# Four-Dimensional Thermal Analysis of 888 nm Pumped Nd:YVO4 Dual-Rod Acousto-Optic Q-Switched Laser
## 5 Conclusions  
In this paper, we built a theoretical model based upon high-power high-efficiency 888 nm in- band pumped Nd:YVO\({}_{4}\) dual-rod CW and AO Q-switched lasers. In addition, an 888 nm in-band pumping scheme effectively deduced quantum defects and compensated for the deleterious thermal effects. A compound symmetrical thermal stable cavity is designed for not only reaching good mode matching to reach high power but also outputting a stable near TEM\({}_{00}\) mode with beam quality that \(M_{x}^{2}=1.113\) and \(M_{y}^{2}=1.147\). Moreover, the investigations of the corresponding space-time-independent 4D analysis model are also made. Complex physical processes like GSD, fluorescence branching, ETU and CR are included and the coupled effect of thermal effect and temperature-dependent cross sections are considered in our model. By our model, we can accurately simulate the 4D evolution of power, population and temperature distributions in crystals at CW and different repetition-rate conditions. Our model can solve the 4D heat generation evolution in not only the steady pulse region but also the unstable region at the beginning. Our results show that the higher the Q-switched repetition-rate, the lower the thermal effect in crystal and the closer to the CW conditions, which is corresponding with the experiment experience and physical reality. The simulated output properties and temperature principles are in good agreement with the experimental results. Our theatrical model can also be further applied in multi-rod or other Q-switched laser systems with complex cavity types for instruction power upgrade and laser machining application.  
[PERSON] proposed the theoretical model, [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed materials; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 4 Engineering Applications
### Determination of Deformation Monitoring Thresholds  
According to the conclusions obtained from the \"Research Report on Surface Deformation Threshold of Open Pit Mining Slope of Heavy Steel Xichang Mining Co., Ltd.\", the monitoring system has not yet considered the indicators of deformation rate continuity, the improvement of the tangent angle, and the amount of single change. In order to adapt to the existing monitoring system, this study establishes a comprehensive early warning method through multiple indicators and determines the deformation rate as the threshold indicator.  
The displacement rate unit of the existing system is mm/h, while the threshold unit in Table 3 is mm/d, which needs to be adjusted. According to the report, using the threshold method proposed in this project can better reflect the slope deformation and give a reasonable warning level, especially when the daily deformation rate is used. The daily deformation rate can effectively reduce the monitoring error, reduce false alarms and improve the warning accuracy. Therefore, this project adopts the deformation rate threshold of 10 mm/d, i.e., 1.158 \(\times\) 10\({}^{-8}\) m/s.
## 3 The Lossless Image Compressor
### Proposed Lossless Algorithm
#### 3.2.4 Variable Length Coding  
The difference of the consecutive pixels (\(dX\)) is then mapped to a non-negative integer and then they are encoded in variable length coding. To get the best compression ratio, the difference of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Standard Images**} & \multicolumn{2}{c}{**CE Image**} \\ \cline{2-5}  & **Mandrill** & **Lena** & **WLI** & **NBI** \\ \hline \(dY\) & 15.23 & 8.28 & 4.21 & 6.12 \\ \(dE\) & 1.56 & 0.97 & 0.19 & 0.21 \\ \(dF\) & 2.99 & 1.30 & 0.19 & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: AVG. Absolute difference in consecutive pixel values.  
Figure 6: Change in consecutive pixel value at row 128 of (**a**) standard 256 \(\times\) 256 “mandrill” image; (**b**) 256 \(\times\) 256 WLI endoscopy image.  
luminance (_dY_) is encoded in Golomb-Rice code and the difference of chrominance (_dE_ and _dF_) are encoded in unary for WLI images. In all cases of luminance and chrominance, _dX_ is mapped one-to-one with another integer set, _m_dX_ and divided by a predefined integer, \(2^{k}\). In Golomb-Rice coding, the choice of \(k\) parameter is important since it dictates the code length. A detailed discussion on choosing \(k\) parameter can be found in Section 3.3.1 of [20]. The unary code can be generated by setting the Golomb-Rice code parameter \(k=0\) in the encoder. For NBI, _dY_ generally spans wider than WLI due to the presence of sharper edges in NBI images. This phenomenon can be noticed from Table 2. To get the best compression ratio, the \(k\) is set as 2 for WLI images and 3 for NBI images for encoding _dY_. The \(k\) parameter values are summarized in Table 3. Pseudo-color NBI images are reconstructed by combining two grayscale images in the computer software. One image is captured using green light and another using blue light. As the input NBI images are grayscale, only the luminance (Y) component is compressed and transmitted. The chrominance components are not sampled for NBI images and pseudo color is added later on the images using computer software.
# A Dual-Branch Speech Enhancement Model with Harmonic Repair
## 3 Experiments
### Ablation Studies  
This study, based on ablation experiments, analyzes the necessity of performance improvement for each module of the model network. It then evaluates the overall performance of the model based on PESQ, STOI, and SI-SDR. Finally, the comprehensive performance of the proposed method is verified. The ablation experiments are conducted on the DNS Challenge 2020 dataset and the VoiceBank + DEMAND dataset.  
The experiment results in Tables 2 and 3 show that using only the harmonic repair module and disabling the fading-out module leads to a significant decrease in speech quality. This is because the nonlinear operations of the harmonic repair module cause disturbances, especially in the recovery of harmonics and particularly in high-amplitude sections, amplifying these disturbances and ultimately affecting the enhancement performance. Pairing the fading-out module with harmonic repair significantly mitigates this issue. Replacing SkiM with naive LSTM demonstrates that SkiM achieves essentially the same performance level as naive LSTM while significantly reducing computational costs. Comparative experiments using the original 2D convolutional kernels for the real part branch's FFC module are conducted. These experiments show that thanks to FFC's advantage of having a large-scale receptive field, the context of speech is fully utilized in the recovery process of the real spectrogram. Unlike traditional convolution, where the receptive field is limited by the size of the kernel, FFC operates convolution in the frequency domain, considering all frequency components of the input simultaneously, which allows for better capture of long-term dependencies in the signal. This demonstrates the advantage of FFC's large-scale receptive field in fully utilizing the context of speech in the recovery process of the real spectrogram.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Blocks (\(N\))** & **PESQ** & **STOI (\%)** & **SI-SDR** & **CSIG** & **Para. (M)** \\ \hline
2 & 3.211 & 96.43 & 20.13 & 4.15 & 11.943 \\
3 & 3.350 & 97.54 & **20.64** & 4.08 & 12.503 \\
4 & **3.513** & **97.85** & 20.52 & **4.22** & 13.105 \\
5 & 3.498 & 97.76 & 20.55 & 4.12 & 13.842 \\ \hline \hline \end{tabular} The optimal values of the objective evaluation metrics are highlighted in bold font.  
\end{table}
Table 1: Selection of the number of stacked blocks for Repair and HFO Blocks.
# Formation Control Algorithm of Multi-UAV-Based Network Infrastructure
## 4 Formation Control Algorithm  
The formation of the UAVs has a profound effect on the performance in the multi-UAV-based network infrastructure. Based on the design of the infrastructure stated in Section 3.1 and the model described in Sections 3.2 and 3.3, we list the objectives of the UAVs' formation as follows.  
* All UAVs should maintain the ad hoc connectivity between each other.
* Congestions on the access networks and the ad hoc network should be reduced.
* The infrastructure should connect the mobile devices as many as possible.
* UAVs should not be redundantly deployed; Formation should use minimal UAVs to maximize the network performance.
* Formation should be changed in response to the change of \(M\).  
To achieve the multiple objectives in real time, we designed a novel feedback-based formation control algorithm of the network infrastructure. For each UAV in a specific start location, the algorithm determines the flying direction of each UAV in a short period, according to the periodically received information from the UAV itself and the neighbors. Our approach is to define the forces designed to fulfill the requirements of the network infrastructure and applied to the UAVs to change the direction of each [36]. By the vector sum of the forces, each UAV flies to the specific direction with a limited speed as shown in Figure 3.  
Figure 3: Each UAV moves toward the vector sum of the applied forces.  
Figure 2: An example of ad hoc network topology.  
The periodically received information of the UAV used by the algorithm includes  
* The position of the gateway, \(\mathbf{p_{0}}\)
* The position of the UAV, \(\mathbf{p}\)
* The positions of the nearby UAVs, \(\mathbf{p}_{i}\)
* The positions of the next hop UAVs, \(\mathbf{p}_{j}\)
* The number of the connected devices of the UAV, \(n\)
* The numbers of the connected devices of the nearby UAVs \(n_{i}\)  
Which are collectable (We use the abbreviated notations in those variables for readability). The subsections listed in the below describe the forces applied to the UAVs and the control mechanism of our algorithm.
## 5 Active Coating and its Control Algorithm
### The Measuring Section of the Active Control Damping System
It is easy to see that at \(h_{c}(\mathbf{r})>>\overline{h}(\mathbf{r},t)\), \(h_{c}(\mathbf{r})>>\overline{h}(\mathbf{r},t)\), the spatially averaged value (along \(S_{k}\)) \(H_{k}(t)=\sigma_{k}^{-1}\underset{S_{k}}{\iint[\overline{h}(\mathbf{r},t)+ \overline{\overline{h}}(\mathbf{r},t)+\overline{\overline{h}}(\mathbf{r},t)]dS _{k}}\) of change of total thickness of two piezoelectric layers and one polymer layer is expressed via measured capacities \(\overline{C}_{k}(t)\), \(\overline{\overline{C}}_{k}(t)\), \(\overline{\overline{C}}_{k}(t)\) as the following:_Algorithms_**2009**_, 2_  
\[H_{k}(t)=h_{c}\Bigg{[}\frac{\overline{c}_{k}(t)}{\overline{c}_{k}^{\,0}}-1 \Bigg{]}+h_{c}\Bigg{[}\frac{\overline{\overline{c}}_{k}(t)}{\overline{\overline{ \overline{c}}}_{k}^{\,0}}-1\Bigg{]}+h_{r}\Bigg{[}\frac{\overline{\overline{ \overline{c}}}_{k}(t)}{\overline{\overline{\overline{c}}}_{k}^{\,0}}-1\Bigg{]} \,\ \ \text{where}\ \ \overline{c}_{k}^{\,0}\,\ \overline{\overline{c}}_{k}^{\,0}\,\ \overline{ \overline{\overline{c}}}_{k}^{\,0}\ -\ \text{relaxed values of}\]  
capacities \(\overline{C}_{k}(t),\ \overline{\overline{C}}_{k}(t),\ \overline{\overline{ \overline{C}}}_{k}(t).\) Knowing the value \(H_{k}(t)\) and the normal displacement \(U_{k}(t)\) of the boundary \(\mathrm{z}=-2h_{c}-h_{r},\) averaged along the area \(S_{k},\) we would obtain the value \(Y_{k}(t)=H_{k}(t)+U_{k}(t)\) [used in algorithm (15)]. However, the statement of the problem in this work (see Introduction) excludes the usage not only of mechanical support(s) for active pistons (dynamical vibrostat), but also excludes the usage of any inertial coordinate system (cinematic vibrostat), relatively with which we would like to measure normal displacements of controlled surface. We can measure the displacement \(G(\mathbf{p}_{k},t)\) of boundary \(\mathrm{z}=-2h_{c}-h_{r}\) in the center \(\mathbf{p}_{k}\) with an inertial accelerometer, if this displacement is temporally smooth (_i.e._, the lengths of the corresponding acoustical waves in piezoelectric material and in inertial body of accelerometer are much greater than the accelerometer's dimensions). We need the spatially averaged value \(U_{k}(t)=\sigma_{k}^{-1}\big{|}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! and targeting of incident waves. The active coating, described above, is spaced immediately on the surface of protected body. This coating creates the desired normal velocity (2) on external surface \(S_{V}\).