esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurement of Heat Flow Transmitted through a Stacked-Screen Regenerator of Thermoacoustic Engine
## 3 Experiments
By adjusting the phase difference and amplitude of the driving voltage signals, the specific acoustic impedance, given by the ratio \(P_{\rm H}/\)\(\langle U_{\rm H}\rangle\) of complex amplitudes of pressure and velocity, was tuned to have magnitude of \(0.3\rho_{\rm m}a\pm 20\%\) and phase angle of \(0^{\circ}\pm 30^{\circ}\) at the position of the hot end of the regenerator. It should be noted that the efficient thermoacoustic Stirling engines achieved the specific acoustic impedance as high as \(30\rho_{\rm m}a\) in the regenerator region [1]. In the present experimental setup, although it was equipped with eight loudspeakers, was not capable of producing such a high acoustic impedance while maintaining the magnitude of the velocity amplitude. This amplitude ratio simplifies the first term \(Q_{\rm A}\) on the right-hand side of Equation (3), and the ratio \(Q_{\rm A}\) over the second term \(Q_{\rm D}\) on the right-hand side of Equation (3) is then given as  
\[\frac{Q_{\rm A}}{Q_{\rm D}}=\frac{\mathrm{Re}\left[g\right]}{\mathrm{Im}\left[ g_{\rm D}\right]}\frac{0.3a\omega}{c_{\rm p}\left(dT_{\rm m}/dx\right)}. \tag{13}\]  
In the case of the regular flow channel regenerator made of the ceramic honeycomb catalyst, the amplitude ratio \(Q_{\rm A}/Q_{\rm D}\) was always below 0.09 for the present experimental conditions. For this reason, we can expect that the heat flow \(Q\) is well approximated by \(Q_{\rm D}\), and therefore a quadratic function of \(\left|\langle V\rangle\right|^{2}\). For reference, we evaluated the ratio \(Q_{\rm A}/Q_{\rm D}\) for stacked screen regenerators by inserting Ueda's effective radius \(r_{0}\) into Equation (3). Results showed that \(Q_{\rm A}/Q_{\rm D}<0.1\) for all mesh screens employed. Therefore, if the result of the thermoacoustic theory is also applicable to the stacked-screen regenerator, then \(Q\) would also be approximated as \(Q_{\rm D}\). We use this result to elucidate the function \(\mathrm{Im}\left[g_{\rm D}\right]\) experimentally, after the comparison of \(Q\) determined using the empirical equations and using experimentation.
# AI Agents Meet Blockchain: A Survey on Secure and Scalable Collaboration for Multi-Agents
## 7 Application and Use Case Scenarios
### Autonomous Fault Management  
Autonomous fault management leverages decentralized architectures and AI agents to address the complexities of fault detection and resolution in distributed systems. In dynamic environments such as micro-services architectures, fault propagation disrupts operations across interconnected components [72]. In a combined framework, AI agents contribute independently based on their specific expertise, while collective mechanisms enable consensus-driven solutions that reflect the system's overall intelligence. This decentralized approach aligns well with modern cloud-native and edge computing environments, where maintaining reliability despite complexity is a critical challenge. Therefore, autonomous fault management represents a foundational application of decentralized intelligence to drive operational efficiency and robustness in distributed computing systems.
## 5 Experimental Study
### Route Constructure in Consideration of Energy Consumption  
In this simulation, we consider how to construct an optimal routing tree with respect to minimum energy consumption. Firstly, we set \(k\) to be 7, \(r_{c}\) to be 20 m and \(q\) to be 80 nJ/bit to simulate a network, and the tree structures derived by SPT, MST, SLT, Greed Steiner and our algorithm are shown in Figure 5. In each picture, the red solid square, black solid circles and hollow circles respectively represent the sink node, source nodes and other relay nodes. Data aggregation occurs where information streams intersect.  
From Figure 5, our algorithm outperforms STL, MST and SLT, which few nodes participate in the connection of network in the sense that it improves the utilization ratio of the network. What is more, the edges formed in the network are obviously less than in the results of other algorithms. All these contribute to reducing the communication energy, improving the network stability and effectively extending the network lifetime.  
\begin{table}
\begin{tabular}{c c c} \hline
**Symbol** & **Definition** & **Value** \\ \hline \(\sigma_{s}\) & A sharing parameter whose dimensions & [0.01 0.01] \\  & equal to the number of objectives & \\ \(\alpha\) & The energy consumed by sending each bit of data & 50 nJ/bit \\ \(\beta\) & The energy consumption in the amplification & 100 pJ/bit/m\({}^{2}\) \\  & circuit for forwarding each bit of data & \\ \(w_{0}\) & The data amount sent by each source node & 400 bit \\ \(r_{s}\) & The correlation range & 50 m \\  & & \(\rho=1-d/r_{s}\) while \\ \(\rho\) & The correlation coefficient between & \(d<r_{s}\ \ \rho=0\) \\  & two nodes in an approximated spatial model & otherwise \\ \(r_{c}\) & The maximum communication range of each sensor node & From 15 m to 50 m \\ \(n\) & Number of nodes & 50 \\ \(k\) & Number of source nodes & 7 and 15 \\ \(q\) & Average unit aggregation cost & 20 nJ/bit and 80 nJ/bit \\ _\(\varepsilon\)(epsilon)_ & Maximal permissible times of energy consumption of \(G^{*}\) to \(G^{*}\) & From 1 to 1.5 \\ \(E\_r\) & The initial energy of each relaying node & 2 mJ \\ \hline \end{tabular}
\end{table}
Table 1: Parameter table.  
Figure 5: Routing tree structures.  
Without considering aggregation cost in the process of tree construction, the energy consumption of Greed Steiner is still relatively high. As expected, our algorithm almost outperforms all other algorithms in different communication ranges. It can adapt itself to a variety of situations. In contrast to other algorithms, our algorithm can dynamically change route selection and decide to select which nodes to perform data aggregation according to different network structures and average unit aggregation costs, and it can effectively trade off multi-hop relay benefiting from high data reduction ratio and single-hop transmission benefiting from less unit aggregation cost.
## 5 Discussion  
This research aims to make use of food waste on the island of Tenerife to produce biogas through anaerobic digestion. The reutilisation of biodegradable waste, in the form biogas, requires an improvement in the current selective waste collection system on the island. This would be a step towards avoiding landfilling and replacing some of the primary energy supply, 96.34% of which is currently produced from fossil fuels.  
The selective collection of waste from kitchens and restaurants in the metropolitan area of Santa Cruz, Tenerife, started in 2021 and reached 1053 tons in 2022 [29], reflecting the political efforts to improve the recycling rate of metropolitan areas on the island. The percentage of collected biodegradable waste on the island is still very low (approximately 1700 tons of food waste from 1,196,050 tons of collected solid municipal waste in 2022) [57].  
With a theoretical potential of 299,012 tons of potential food waste (2021), from municipal solid waste, based on the European average of 25% being food waste [22, 23, 58]; this gives a biogas potential of 28.97 \(\times\) 10\({}^{6}\) Nm\({}^{3}\), equivalent to 18.15 \(\times\) 10\({}^{6}\) L gasoline. This would also mean savings of 42.66 \(\times\) 10\({}^{3}\) equivalent CO\({}_{2}\) tons. After the fermentation process, the remaining organic fraction can also be used as fertiliser. This amounted to 264,612 tons. Municipal initiatives began the collection of the biodegradable fraction from municipal households, with pilot projects implementing the brown waste container some years ago; a biological pretreatment to stabilise the food waste was also investigated. The biodegradable residues were ensiled with effective microorganisms and a positive effect on the biological waste stabilisation was observed. The DM and the VSs were conserved and the solubilised COD did not show any significant variation after the entire ensiling period. Organic acids were produced and the pH decreased in the EM-treated substrate that shows an inhibitory effect on pathogens and prevent odours; NH4\({}^{+}\) did not show any relevant effects.  
A new developed small biogas plant has been proposed that is made of textile materials. Decentralised small biogas plant systems are increasing in importance as savings in logistics and transportation significantly influence operational costs. Based on this prototype, this system can produce up to a total power capacity of 75 kWel and process a maximum amount of food waste of approx. 80,000 tons per year. In the future, this concept should be applied to achieve a self-sufficient and decentralised energy supply. In summary, small-scale decentralised plants offer several advantages like localised treatment; lower transportation costs; simplified construction with easier and more cost-effective construction techniques displaying less demand for materials and easier engineering processes; efficient operation with simplified operations and maintenance; and versatile output for various applications such as vehicle fuel in the form of CNG or direct energy supply.
## 3 Antenna Performance Enhancement Techniques
### Effect of Corrugation on Bandwidth and Front-to-Back Ratio  
Removing the metal part, which might be in any geometrical shape such as square, rectangular, triangular, sine or any other), from the radiator's edge has a great impact on improving the most important performance parameters in terms of bandwidth, gain, and front-to-back ratio [38].  
Figure 8: Antenna configurations (**a**) reconfigurable 8 \(\times\) 8 MIMO system and (**b**) S11 parameters and isolation [11].
# Lung Segmentation in CT Images: A Residual U-Net Approach on a Cross-Cohort Dataset
## 1 Introduction
The 2D model has an input dimension of 512 \(\times\) 512 \(\times\) 1 and it is formed by 4 encoders and 4 decoders, in which bilinear interpolation is used for the upsampling step. On the other hand, the 3D model has an input dimension of 512 \(\times\) 512 \(\times\) 8, 3 downsampling steps, and 3 upsampling steps with trilinear interpolation. For both models, softmax function is used in the output layer and cross entropy is used as a loss function. Two types of models were trained, one for the segmentation of the whole lung region and another for the separated segmentation of each lung. Concerning the latter, each ground truth was separated into two additional masks, each containing only one of the lungs. Afterwards, each of these masks was flipped horizontally and used as training for the segmentation of the opposite lung. The University Hospitals of Geneva's Interstitial Lung Disease (HUG-ILD) dataset was used as external validation. The 2D model presents a _DSC_ of 0.9840 and 0.9840 for the whole segmentation and for the separated segmentation, respectively. [PERSON] et al. [17] introduced a residual U-Net for the lung segmentation in CT images. Initially, to improve the number of available training images, data augmentation was performed via flips, rotation, zooming, and shifting. The residual U-Net architecture is a combination of ResNet and U-Net architectures; hence, it presents an encoder path and a decoder path, each one with four stages. The _DSC_ was used as a loss function. In order to improve the model accuracy, a connected component algorithm was applied to remove non-lung regions. The Lung Nodule Analysis 2016 (LUNA16) and the Vessel Segmentation in the Lung 2012 (VESSEL12) datasets were used for training, whereas the model evaluation was performed with the HUG-ILD dataset. Two different architectures, ResNet34 and ResNet50, were implemented alongside the U-Net. It was verified that the latter presents slightly better results, achieving an average _DSC_ of 0.9868. Lung segmentation can be a very difficult task due to the influence of other pathologies that produce imaging changes. The presence of other respiratory comorbidities alongside lung cancer is frequent. Lung cancer and COPD, both predominantly caused by cigarette smoking, are closely linked, and each condition presents its own range of characteristic imaging features [18]. For this reason, apart from lung-cancer-specific patterns, it is imperative that the segmentation models are likewise able to identify features particular to other pulmonary conditions. The main goal of this work is the development of a deep-learning-based model for lung segmentation in CT images that must be robust on a cross-cohort dataset and capable of coping with the numerous and variable imaging appearances, derived from different pathologies with physiological heterogeneities.
## 2 Optimized Distributed Raman Amplification Design over Single Fiber Span  
As the stimulated Raman scattering allows a Stokes shift (13 THz) from the pump to the signal which gives more flexibility for the choice of the pump wavelengths and the Raman gain fiber, Raman amplification is highly configurable. For example, in Reference [30], a cascaded third-order Raman pump was used in an unrepeatered transmission experiment. In Reference [34], the authors presented a sixth-order Raman pump configuration. However, the commonly used Raman schemes are based on first-order and second-order Raman amplifiers which have relatively configurations, higher pump-signal conversion efficiency and consequently lower cost [35; 36; 37; 38; 39; 40; 41; 42; 43]. Using only forward Raman pumping is generally not feasible for transmission systems because firstly, it introduces significant RIN-related penalty to the transmitted signal from the pump, and secondly the signal suffers high fiber nonlinearity due to high signal power near the input sections of the transmission span [35; 36; 37; 38; 39; 40; 41; 42; 43]. Therefore, in this paper, we mainly focus on the designs of first-order and second-order distributed Raman amplification schemes using backward or bidirectional pumping which requires the RIN penalty mitigation technique.
## 5 Data Evaluation
### Accuracy Results
#### 5.3.1 Values of Three-, Four-, and Five-Node Motif Frequencies  
Figure 6 and Table 3 show the real values of the three-, four-, and five-node motif frequencies of the graphs studied in this paper. Table 3 and Figure 6 show the real values of three-node directed motif frequencies for the undirected and directed graphs of Flickr, Pokec, LiveJournal, Wiki-Talk, and Web-Google, respectively. Here, undirected graphs are obtained by discarding the edge directions of directed graphs. Among all three-node directed motifs, the seventh motif exhibits the smallest frequency for all these directed graphs. Flickr, Pokec, LiveJournal, Wiki-Talk, and Web-Google have \(1.35\times 10^{10}\), \(2.02\times 10^{9}\), \(6.90\times 10^{9}\), \(1.2\times 10^{10}\), and \(7.00\times 10^{8}\) three-node CIS, respectively. Figure 6 reveals the actual values of the three-node signed motif frequencies for the graphs Sign-Epinions, sign-Slashdot08, and sign-Slashdot09. Sign-Epinions, sign-Slashdot08, and sign-Slashdot09 have \(1.72\times 10^{8}\), \(6.72\times 10^{7}\), and \(7.25\times 10^{7}\) three-node CIS, respectively. Figure 6 reveals the actual values of four-node undirected motif frequencies for the graphs soc-Epinions1, soc-Slashdot08, soc-Slashdot09, and com-Amazon. Graphs soc-Epinions1, soc-Slashdot08, soc-Slashdot09, and com-Amazon have \(2.58\times 10^{10}\), \(2.17\times 10^{10}\), \(2.42\times 10^{10}\), and \(1.78\times 10^{8}\) four-node CIS, respectively. Figure 6 reveals the actual values of five-node undirected motif frequencies for com-Amazon, com-DBLP, p2p-Gnutella08, ca-GrQc, ca-CondMat, and ca-HepTh. Com-Amazon, com-DBLP, p2p-Gnutella08, ca-GrQc, ca-CondMat, and ca-HepTh have \(8.50\times 10^{9}\), \(3.34\times 10^{10}\), \(3.92\times 10^{8}\), \(3.64\times 10^{7}\), \(3.32\times 10^{9}\), and \(8.73\times 10^{7}\) five-node CIS, respectively.  
Figure 6: Real values of motif frequencies: (**a**) three-node directed motifs; (**b**) three-node signed motifs; (**c**) four-node undirected motifs; (**d**) five-node undirected motifs.
# A Robust Security Architecture for SDN-Based 5G Networks
###### Abstract  
5G is the latest generation of cellular mobile communications. Due to its significant advantage in high data rate, reduced latency and massive device connectivity, the 5G network plays a vital role in today's commercial telecommunications networks. However, the 5G network also faces some challenges when used in practice. This is because it consists of various diverse ingredients, termed heterogeneity. The heterogeneity of the 5G network has two consequences: first, it prevents us to use this technology in a uniform way, preventing the wide use of 5G technology; second, it complicates the structure of the 5G network, making it hard to monitor what is going on in a 5G network. To break through this limitation, researchers have worked in this field and design their own protocol, in which software-defined networking (SDN) is one key design concept. By separating control and data plane, SDN can make the 5G network functional and programmable, such that we can handle the heterogeneity in traditional 5G networks. In light of this, we say that SDN-5G network is attractive, but its advantages are not free. The intelligence centralization used in SDN has its own drawbacks when it comes to security. To break through this limitation, we propose a robust security architecture for SDN-based 5G Networks. To find the illegal request from malicious attackers, we add extra cryptographic authentication, termed synchronize secret. The basic idea of our scheme is leveraging preload secrets to differ attacks from regular network communications. The simulation results indicate that our work can completely handle the security problem from SDN with a low disconnect rate of 0.01%, which is much better than that from state of the art.  
5G networks; SDN; hash; cryptographic authentication +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 85; doi:10.3390/611040085
## 4 Evaluation  
### Datasets and Experimental Settings  
We trained and evaluated our model on the Microsoft Machine-Reading Comprehension (MS-MARCO) dataset [21]. The training set contains approximately 400 M tuples of queries and relevant and non-relevant passages. The development set contains approximately 6900 queries, each paired with the top 1000 passages retrieved with BM25 [22] from the MS-MARCO dataset. On average, each query has one relevant passage. We trained the model via negative sampling using the ratio of 1:5 for the 1000 passages corresponding to each query. To implement the proposed model, we adopted the pre-trained GloVe algorithm. The vocabulary size of GloVe was 300. We set the hidden size of the GRU neural network to 200. The model optimization was performed with Adam [23] at a learning rate of 0.0001, and the learning rate was halved if the validation performance did not improve. The dropout rate was set to 0.2, and the mini-batch size was set to 256 sequences.  
We used mean reciprocal rank at 10 (MRR@10) [24], which represents the MRR score of documents ranked in the top 10 because it is essential for relevant documents to be highly ranked for MRC models, as expressed in Equation (7):  
\[\text{MRR@10}=\frac{1}{n}\sum\
olimits_{i=1}^{10}\frac{1}{r_{i}} \tag{7}\]  
In Equation (7), \(r_{i}\) is the rank of the first passage containing a correct answer produced by the \(i\)th query and \(n\) is the number of queries.
# Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks
## 3 Experiments and Results
### Real Data Illustration
#### 3.3.2 Results  
Table 4 displays the scores of each method for the AAR and PAR performance metrics for each test set, as derived from the real datasets and their corresponding model parameters. It is vital to emphasize that errors in classification are inevitable in practice [6]. Therefore, in this case, for the PAR metric, we not only considered the scenario where all attributes were accurately estimated (i.e., PAR(\(K\))) but also calculated instances where an attribute error occurred (i.e., PAR(\(K-1\)); [23]), to provide a more general assessment of the methods. The results in Table 4 show that the performance patterns of each method on the real datasets were generally consistent with those on the simulated datasets. In a detailed examination, neural-network-based methods outperformed the traditional approaches overall. In particular, our method, Dual Q-Net, emerged as the top performer across all datasets, both in terms of AAR and PAR metrics. In terms of AAR, the parametric models outperformed the non-parametric models among the traditional models. Specifically, on the EPTT datasets, the GDINA model performed the best, followed by the DINA model, while the NPC and GNPC models were the worst and had no significant difference. On the FRAC datasets, the DINA model led the other three traditional models, followed by GDINA, NPC, and GNPC. On both the EPTT and FRAC datasets, the classical methods exhibited an upward trend in AAR scores as the sample size increased. In contrast, the neural-network-based methods demonstrated performance patterns similar to those on the simulated datasets, and their performance remained robust across varying sample sizes. The number of attributes affected the performance of all methods. This is shown by the fact that the model's performance decreased with an increase in the number of attributes. Among them, the neural-network-based methods were less affected by the number of attributes and showed better robustness. It should be emphasized that only the DINA model maintained a higher AAR score on the FRAC datasets than on the EPTT datasets. This was because the benchmark of the FRAC datasets was based on the prediction results of the DINA model on the whole FRAC dataset.