esa-sceva/satcom-chunk-collection huggingface dataset:
# AI Agents Meet Blockchain: A Survey on Secure and Scalable Collaboration for Multi-Agents
## 8 Research Challenges and Future Directions
### Scalability and Interoperability  
While blockchain enhances reliability and decentralization, its integration with AI agents encounters significant scalability and performance limitations. The computational overhead of consensus mechanisms coupled with the latency of transaction validation impacts real-time operations of autonomous agents [92]. Moreover, the growing storage requirements of expanding blockchains create challenges in maintaining lightweight and efficient systems for agent interactions [24]. These constraints demand careful design decisions, such as optimizing transaction batching and utilizing alternative blockchain platforms in order to balance the trade-offs between decentralization, speed, and scalability in agent-driven applications. Another research challenge in decentralized collaboration and task integration is achieving seamless interoperability among different blockchain platforms [93]. AI agents operating across multiple blockchains face obstacles due to varying protocols, consensus mechanisms, and data structures [94]. These differences hinder efficient communication and task execution, limiting the scalability of multi-blockchain ecosystems. Additionally, managing cross-chain interactions introduces security risks, such as double-spending or transaction replay attacks, further complicating the integration process. The lack of standardized APIs or smart contract interfaces increases these challenges, making it difficult for AI agents to synchronize data and workflows across heterogeneous blockchain networks. Without effective interoperability, the potential for dynamic and decentralized collaboration is significantly restricted.  
Future research should prioritize the development of cross-chain communication protocols and privacy-preserving frameworks to enable seamless operation of AI agents across multiple blockchains. For example, uAgents use lightweight and secure networking protocols to facilitate interoperability while maintaining scalability and trustworthiness. Likewise, solutions such as blockchain interoperability hubs (e.g., Talus [95]) and cross-chain bridges (e.g., Delsium [96]) enhance privacy and secure interactions between decentralized systems. In addition, the Mina Protocol [97] utilizes the zk-SNARK model to integrate AI agents into blockchain ecosystems without exposing sensitive data. Moreover, decentralized swarm intelligence [98] and verifiable computation [99] allow AI agents to achieve consensus on data or actions while ensuring that sensitive inputs remain confidential. Furthermore, APIs from multi-chain solutions (e.g., Bitte [100]) simplify cross-chain operations to promote seamless collaboration across diverse ecosystems. By addressing these challenges, researchers and practitioners can unlock the full potential of AI agents in decentralized multi-blockchain environments to achieve enhanced security, efficiency, and autonomy.
## 2 Related Works
### Summary and Key Takeaways  
Across these related works, several key trends emerge:  
* Graph-based anomaly detection ([13; 14]) improves detection accuracy but suffers from scalability issues.
* AI-driven IDSs ([15]) enhance network security but lack real-time adaptability.
* Hybrid ML approaches ([16; 17]) show promise in anomaly detection but require optimization for large-scale deployment.  
Unlike previous studies, our Transformer Autoencoder + Hybrid Classifier (Isolation Forest + XGBoost) model does the following:  
* Learns both spatial and temporal patterns for anomaly detection.
* Differentiates between sensor malfunctions and cyber intrusions in real time.
* Optimizes computational efficiency for large-scale WSN deployments.  
This hybrid methodology bridges the gap between existing deep-learning-based anomaly detection and real-time classification, making it a novel and practical approach for Wireless Sensor Network security.
## 4 Discussion and Future Work  
Theoretically, the bin widths of the Vernier-type TDC could have been significantly uniform due to the absence of ultra-wide bins. However, according to the performance evaluation results of the fine-timestamp maker, the bin widths at the back part of the ultra-fine counter (25-27) show a remarkable decrease compared with those of other counting ranges (as shown in Figures 11 and 12). This could incur poor DNL performance. As the delay of RO in this design is not compensated, its jitters will gradually accumulate with the increases of the oscillation number. Accordingly, for the hit signals falling into different bins, their position determinations also have different uncertainties. This phenomenon causes some hit signals to be captured by other adjacent bins instead of their target bins. Although the bins located in front will lose some hit signals due to these uncertainties, they will also receive hit signals from neighboring bins as compensation, so it seems that the bin widths in front are generally uniform. Thus, the compensation from adjacent bins will be less than others, and there is a large jitter due to the previous accumulation; the last three TDC bins are therefore more likely to miss their hit signals. For these uneven widths of bins, this study adopts the bin-by-bin calibration method, but both the online and offline calibrations will consume a lot of block RAM resources inside the FPGA. In addition, improving the linearity of the ring oscillator by using the Xilinx Engineering Change Order (ECO) tool in this paper offers significant benefits, but also introduces the drawback of manual tuning of the FPGA bitstream, limiting its potential for large-scale use. Therefore, to further improve the performance of the TDC, we can investigate a jitter compensation method of the ring oscillator and a method that does not require manual adjustment when configuring the ring oscillator length accurately in the subsequent work. In conclusion, due to low resource consumptions, we could achieve multi-channel designs of TDC to perform more functions. For example, multiple channels measuring the same hit signal simultaneously could improve measurement accuracy, and the multiple channels taking turns to measure different hit signals could reduce the dead time. Note that the existing design in this work is the result of a trade-off between the performance of resolution, resource cost, dead time, and RMSE. However, the improvement in the resolution is at the expense of the increase in the dead time and RMSE. Therefore, if there is a specific demand, the frequency of the ring oscillator can be adjusted to achieve higher measurement accuracy or less dead time. The system clock signal usually pursues a faster frequency to reduce the length of time interpolation. If its frequency is limited and the BUFG resources in the FPGA are not tight, we can add more phase-shifted clocks so that the TDC could operate at a lower system clock frequency and still maintain high accuracy. To ensure a lower dead time without reducing
## 5 Experimentation and Result Analysis
### Experimental Setup  
In this work, all experiments were carried out in Intel(r) core(tm) i5-8250U processor @1.60 GHz 1.80 GHz, 8 GB RAM, with the Windows ten operating system. The coding and simulation of the model were executed in python 3.8, and PyTorch 2.0 and the sklearn library  
Figure 2: Proposed Model Multi-class Bi-LSTM architecture.  
were utilized. For the testing and validation, samples were taken from NSL-KDD+ datasets, described in Section 3.1. The proposed model outcome is endorsed with a stratified K-fold cross-validation approach with k fixed as 10. In addition, the proposed model outcome is associated with other existing models such as AE-CGAN-RF [28], LSSVM-IDS [31], RNN-IDS [9], and SSAE-LSTM [32], which were applied to the balanced NSL-KDD+ dataset.
## 6 Conclusions  
In this paper, considering the uncertainty of medical waste production quantity and population density, a multi-cycle and multi-objective logistics network model of medical waste emergency disposal under uncertain conditions is constructed. Through the deterministic transformation of the model based on the case of medical waste disposal in Wuhan, MOPSO-NSGA2 hybrid algorithm was used to solve the multi-objective model under the uncertain conditions and the sensitivity analysis, which verified the effectiveness and the superiority of the algorithm and obtained the medical waste emergency disposal logistics network decision-making model in a complex reality. The results are as follows: (1) This paper optimizes the existing NSGA2 algorithm. In order to overcome the slow search speed of the NSGA2 algorithm, particle swarm optimization with fast computing speed is introduced in order to combine the two algorithms and to complement each other. By comparing the convergence, the distribution, the number of Pareto optimal solutions, and the operation time of the hybrid algorithm with the single algorithm, the superiority of the hybrid algorithm is further verified. (2) The optimization results of each target in each cycle after iteration of the improved hybrid algorithm were compared. From the perspective of multi-cycle analysis, the economic cost and the infectivity risk involved in the multi-cycle model were lower than the cost in the single-cycle one. From the perspective of multi-objective analysis, at the same time, a variety of goals more suitable for real life should be considered in order to avoid losing. (3) Through MOPSO-NSGA2 solving the Wuhan IMW emergency disposal logistics network, the final optimal result was an economic cost of 110.139 million CNY and an infectious risk of 5137.88. In the first cycle, IMW is transported to T and NT1, respectively, for processing. In the second cycle, IMWs are still transported to T and NT1, but at this time, the processing capacity of T is improved by technological upgrading. In the third cycle, the existing processing centers T, NT1, and NT2 were all put into use. (4) The confidence level of triangular fuzzy quantity of medical waste production was analyzed. As the confidence level increases, both the optimal economic cost and the risk of infection increase. Under different confidence levels, the emergency disposal plans of medical waste in each cycle did not change, which further verified the stability of the logistics network constructed in this paper.  
Conceptualization, F.Z.; methodology, F.Z.; software, X.W.; validation, X.W.; formal analysis, B.L.; investigation, W.S.; data curation, Z.L.; writing--original draft preparation, F.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Social Science Fund of China (Grant No. 22 BGL203).  
Not applicable.  
The simulation data can be obtained from the corresponding author upon request.  
This work was supported by the National Social Science Fund of China (Grant No. 22 BGL203).  
The authors declare no conflict of interest.
## 3 CDL-Based DR Scheme
### Broadcast Total Rebate  
If the DR event needs to be implemented, the LSE will broadcast a total rebate for all the customers who participate in the DR. The rebate value is dynamically determined by LSE. Combining it with the real-time market and calculating an optimal rebate value is suitable for this model from LSE's viewpoint. Because this paper focus on social welfare, the rebate value can be calculated simply as:  
\[\pi_{R}(t)=\sigma\pi_{r}|P_{\Delta CDL}(t)| \tag{4}\]  
where \(\sigma\) is a positive constant and \(\pi_{r}\) is the retail rate.  
Every customer will receive the total rebate and the CDL, and after a limited time, for example, 5 min, it is required that the customer claims an ideal rebate value ratio \(k_{i}\in[0,1]\), which is as much of a proportion of the total rebate the customer wants to obtained. It should be noted that every customer determines their rebate ratio greedily; however, after gathering all the rebate ratios, LSE will calculate the actual ratio \(k_{i}^{*}(t)\) for each customer, which ensures that the total rebate ratio \(K(t)\) will not exceed the \"1\":  
\[K(t)=\sum_{i\in\mathcal{I}}k_{i}(t) \tag{5}\] \[k_{i}^{*}(t)=\left\{\begin{array}{ll}k_{i}(t)&K(t)\leq 1\\ \frac{k_{i}(t)}{K(t)}&K(t)>1\end{array}\right.\]  
Thus, the actual rebate for customer \(\pi_{i}(t)\) at time slot \(t\) is:  
\[\pi_{i}(t)=k_{i}^{*}(t)\pi_{R}(t) \tag{6}\]  
At the same time, getting a rebate also means a corresponding DR task, and the target load for each customer will be:  
\[D_{i}(t)=D_{i}(t-1)+k_{i}^{*}P_{\Delta CDL}(t) \tag{7}\]
# Power-Efficient Random Access Design for Machine Type Communication
###### Abstract  
Machine type communication (MTC) is the key solution to the information exchange between devices. It is the fundamental part of the Internet of Things (IoT). MTC is quite different from the human type communication (HTC), as most of the MTC applications have low requirements on data rate and latency. However, the battery life or the power consumption are very critical to MTC. Therefore, one of the most important issues involving MTC is to provide an efficient method for an MTC device to access the cellular network due to the fact that the data transmission is triggered by the device in some MTC scenarios. We address the issues in the traditional random access procedure in the LTE system and propose a power-efficient random access signal design for MTC. We analyze the bandwidth selection under different coverage requirements and propose an effective bandwidth concept to enable a power optimized random access signal design for MTC.  
machine type communication; Internet of Things; random access; coverage class; hopping pattern Article
## 3 Results and Discussion
### Amino Acid Analysis  
Amino acid analysis is a widely used method for evaluating the amino acid content of proteins in crude and processed foods [28]. Therefore, amino acid measurements were performed for Samples A, B, and C, and the results are shown in Table 3. From this analysis, the total amounts of amino acids in Samples A, B, and C were 3.1 umol/mL,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Glucose (g/100g)** & **Allose (g/100g)** & **Raffinose (g/100g)** \\ \hline A & 3.47 \(\pm\) 0.03 *** & 1.06 \(\pm\) 0.03 **,** ** & 2.14 \(\pm\) 0.04 **,** ** \\ B & 3.45 \(\pm\) 0.02 *** & 1.63 \(\pm\) 0.02 & 2.75 \(\pm\) 0.05 *** \\ C & 7.05 \(\pm\) 0.04 & 1.61 \(\pm\) 0.01 & 1.49 \(\pm\) 0.04 \\ \hline \hline \end{tabular}  
* \(p<\) 0.01: A vs. B, **\(p<\) 0.01: A vs. C, *** \(p<\) 0.01: B vs. C.  
\end{table}
Table 2: Contents of glucose, allose, and raffinose in Samples A, B, and C.  
3.5 umol/mL, and 4.4 umol/mL, respectively. Furthermore, different types of amino acids varied in content in Samples A, B, and C. The various amino acid contents in Samples A, B, and C were glutamic acid (0.25 umol/mL, 0.28 umol/mL, and 0.35 umol/mL); alanine (0.58 umol/mL, 0.78 umol/mL, and 0.9 umol/mL); valine (0.14 umol/mL, 0.21 umol/mL, and 0.23 umol/mL); leucine (0.23 umol/mL, 0.25 umol/mL, and 0.32 umol/mL); histidine (0.01 umol/mL, 0.04 umol/mL, and 0.07 umol/mL); and arginine (0.03 umol/mL, 0.17 umol/mL, and 0.47 umol/mL). Glutamic acid has been reported to contribute to taste and acidity [18], and alanine to acidity and bitterness [28]. Valine and leucine are branched-chain amino acids that comprise muscle proteins and contribute to motor functions. The intake of branched-chain amino acids can help reduce sarcopenia, a disease that causes a decrease in total body muscle mass and muscle strength with aging [29]. Histidine is an essential amino acid that works on the sympathetic nervous system to break down fat by working on nerve cells and is converted to histamine in the body [30]. It is known to exert both hepatoprotective and anti-inflammatory effects [31]. Arginine promotes growth hormone secretion and increases muscle strength [32]. Moreover, it lowers blood pressure through its vasodilating effect [33]. Thus, in terms of flavor, richness, and benefits to the body, Sample C contained more amino acids that are beneficial to health than Samples A and B. GABA, PEA, and MEA are not amino acids. However, we measured GABA, PEA, and MEA in order to see if they might be components that reflect differences in manufacturing methods, fermentation, etc., of each rice wine, but no significant differences were observed. There are several possible reasons for the differences in the type and content of amino acids obtained in this study. These are (1) differences in the type of rice used in the production of rice wine, (2) differences in the production method, (3) differences in amino acid and carbohydrate production due to differences in the yeast used in the rice wine production process, and (4) the variability associated with a fermentation process of rice wine.
# Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton using Electromyography
## 2 Background
### Electromyography  
The intensity of an electromyographic signal is directly related to the amount of force exerted by the skeletal muscles [11; 12; 13]. With the correct interpretation of the electromyographic signals, the exoskeleton control system can identify the user's movement intentions.  
The two basic EMG techniques are intramuscular and surface. The former uses invasive needle-based electrodes, whereas the latter, also known as sEMG, uses non-invasive surface electrodes. The advantage of intramuscular EMG is that signal acquisition is less susceptible to interference than sEMG [13; 28], which enables obtaining information from smaller muscles or those located below other muscles [10; 13]. The advantage of sEMG lies mainly in the fact that it is non-invasive. Examples of the use of sEMG are found in studies that monitor signals to control exoskeletons for arms [4; 8; 11; 16; 17; 19], hands [9], wheelchair [15], and knee [29]. In [3], the state-of-the-art on lower limb exoskeletons is analyzed.
## 4 Discussion
The results reported in Section 3.3 regarding the values of the DRI\({}_{\text{24,max}}\) measured with the AIR-nl respirometer confirm the trends found by the online DRI. The online measurement of the respirometric index was designed and implemented as previously described for multiple reasons:  
* It is based on O\({}_{2}\) measurements instead of CO\({}_{2}\), preferable as stated by international standards like ASTM 1996 [39]. * Since the aeration in the bioreactor is continuous, the respirometric index is dynamic (i.e., not static: without any forced air ventilation) --which is preferable as stated by [PERSON] et al. [40] and [PERSON] et al. [38]--and adopted for standards around the world as a biological activity index (ASTM 1996 [39], in Italy UNI/TS 11184:2006 [41]). * It is based on the AIR-nl respirometer, which slightly overestimates the oxygen consumption rate compared to the other Italian respirometric method (DiProVe) [16], which results in conservative stability values. * The upper and lower oxygen content thresholds are settable, and in this case the oxygen was never below 14% as stated by the Italian standard [41] for composing processes. * It prevents any decrease in moisture content during aeration, since the air is constantly kept in a saturated vapor condition, and this is important since humidity affects microbial activity [29; 30; 31; 32; 33; 34]; a future improvement to avoid high moisture content in final compost could be the recirculation of external air while excluding the water bath in the last days of the process, allowing the dehumidification of the biomass. * It is effectively an online measurement and it does not require to withdraw a sample from the composting pile and it does not require any extra time for another time consuming respirometric test. The configuration proposed in this study, with a zirconia sensor inside the closed bioreactor, was effective and stable in measuring oxygen content, even if in wet and extreme conditions for the eventual presence of ammonia and other compounds that often cause failure in probes. This may suggest a possible improvement in composting plant management, i.e., the possibility of monitoring the respirometric activity _during_ the process. If the laws and regulations would be accordingly updated, the DRI online measurement, as here proposed, could be used to control and optimize the composting process, e.g., by ending the thermophilic phase when the oxygen consumption rate is constantly under a pre-defined threshold. In addition, the use of the DRI online measurement could avoid the offline measurement of the stabilization index of the final products, i.e., the compost or, as here proposed, the hydrochar co-compost.
## 6 Recommendation and Outlook  
Analyses with static equivalent loads are easy to carry out, as no special software must be used that meets the requirements for simulations in the field of high-speed dynamics. However, the results are strongly on the conservative side, so an economic design cannot be achieved.  
The TNT scenario derived in this work, which is congruent to the triangle blast load, can be used to perform an analysis with the *load_blast_enhanced feature integrated in LS-DYNA. Other software products offer comparable implementations. However, it should be noted here that the results are also slightly conservative compared with the more precise calculation.  
It is therefore recommended to use the calculation method derived in this paper, which applies a time- and distance-dependent triangular pressure load for each element of the tank. The approach uses an approximate TNT equivalence but should not be confused with the TNT equivalence method for determining the effect of a vapor cloud explosion. The shortcoming of the method is that a slightly too high blast wave velocity is considered. Future work could investigate the extent to which the blast wave velocity influences the results. Future research should then deal with this specific aspect. For example, complex CFD analyses can investigate the pressure propagation onto cylindrical tanks with roofs depending on the blast scenario, tank height-radius-row height ratios and relative position of the blast source to the tank. However, future research directions should include experimental work to support and validate the numerical analyses. The challenge here is the appropriate implementation of a slow deflagration event, as experiments are mainly conducted with explosives that represent shock wave scenarios.  
The type of roof load to be considered for the design cannot be conclusively clarified with the present research. One recommendation is to consider a compression load for the design of the structure but a suction load for the design of the anchoring. This covers both cases and ensures reliability. Future work in the field of CFD simulation or experimentation is also necessary.  
Design criteria for blast-loaded tanks should be based on deformation rather than on stress limits. As a blast event is considered a singular event in the design lifetime of a tank system, the adoption of the consequences defined for contingency level (CL)/safe shutdown (SS) should be considered. The allowable ductility should be chosen conservatively  
Figure 15: Approximation of the bending deformation of the tank wall and roof due to (**a**) roof compression load and (**b**) roof suction load.  
to \(\mu=1\) if no further research on this is available. Consideration should be given to whether larger deformations can be permitted for the anchoring.
# An Energy-Efficient and Robust Multipath Routing Protocol for Cognitive Radio Ad Hoc Networks
## 5 Performance Evaluation
### Simulation Results and Discussion  
In this subsection, the simulation results of ERMR and L2 ER are comparatively discussed. By varying the number of active sessions and the number of SUs, the abovementioned four performance metrics are evaluated and compared.