esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Summary and Conclusions  
We have explored the rare uranium tri-iodide compound from first-principles theory and CALPHAD. In the bulk form, UI\({}_{3}\) has not before been investigated from first principles. Tangible quantities that are directly measurable by experiments, such as formation enthalpy, atomic volume, crystal structure, and magnetism, are reasonably well reproduced by our parameter-free DFT approach. The theory furthermore predicts the existence of band gaps in the electronic structure that may suggest a need for methodologies accounting for stronger electron correlation. However, no experiments that we are aware of have measured the band gaps and the state of the electron correlations (e.g., the degree of uranium 5f-electron localization) remains an open question. The band gaps are important for understanding the details of the electronic structure and spectroscopical properties but have limited impact on the energetics and associated quantities that we are primarily focused on here.  
Furthermore, we have calculated the nine single-crystal elastic constants for UI\({}_{3}\). These are predictions that may prove difficult to validate experimentally because of issues sourcing ample-sized samples of single-crystal UI\({}_{3}\). However, the calculated moduli appear realistic and support mechanical stability of the orthorhombic phase. These moduli may also help to constrain future semi-empirical modeling or interatomic potentials for UI\({}_{3}\).  
High-temperature thermodynamics of UI\({}_{3}\) is the principal target of the present investigation and we have leveraged the thermodynamical CALPHAD approach to provide guidance and consistency for these properties. DFT quantum molecular-dynamics simulations would predict the most accurate thermodynamical quantities, but it is computationally too exhaustive for a complex material such as UI\({}_{3}\). Instead, we have coupled our DFT to a quasi-harmonic Debye-Gruneisen scheme that is used to more efficiently estimate the thermal properties. Because the melting temperature is relatively low for UI\({}_{3}\), the QH treatment turns out to be fairly accurate in our estimation. We have compared the formation enthalpy, Gibbs energy, and specific heat with CALPHAD and the agreement is satisfactory, suggesting good accuracy. Our conclusion, for these properties at least, is that anharmonic phonons, beyond the quasi-harmonic limit, are not significant in UI\({}_{3}\).  
Conceptualization, P.S.; methodology, P.S., A.P., E.E.M., A.L.; writing-review and editing, P.S., A.P., E.E.M., A.L., T.W.H. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by the Laboratory Directed Research and Development Program at LLNL under Project Tracking Code No. 18-SI-001.  
Figure 3: UI\({}_{3}\) specific heat at constant pressure, Cp (J/mol/K), calculated from our DFT-quasi-harmonic (QH) model and from the SSUB6 CALPHAD database.  
**Acknowledgments:** This work was performed under the auspices of the U.S. DOE by LLNL under Contract DE-AC52-07 NA27344.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Literature Review of Network Intrusion Detection
### Deep Learning in Intrusion Detection  
Deep learning algorithms, particularly artificial neural networks, have attracted considerable attention in recent years due to their potential to enhance intrusion detection systems [45]. Convolutional neural networks (CNNs) [46] and recurrent neural networks (RNNs) [47] have proven effective in detecting network intrusions, with CNNs excelling at learning spatial features and RNNs at learning temporal features.  
Numerous studies have demonstrated the successful application of deep learning techniques in network intrusion detection [48]. For example, [PERSON] et al. [49] used a stacked autoencoder (SAE) to learn hierarchical features from the UNSW-NB15 dataset, outperforming traditional machine learning methods. Similarly, [PERSON] et al. [50] applied a deep belief network (DBN) to detect intrusions in the KDD Cup 1999 dataset, highlighting the capability of deep learning algorithms to handle high-dimensional data. More recently, researchers have examined the use of advanced deep learning architectures [51], including long short-term memory (LSTM) [52] and gated recurrent unit (GRU) networks [53], for intrusion detection in network traffic data [54]. These models have shown promise in capturing temporal dependencies in sequential data, which is especially relevant for analyzing network traffic.
# MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing
## 1 Introduction
* Aiming at the problem of low resource utilization due to resource conflict caused by the concurrent offloading of tasks by multiple users, an edge-computing, time-dependent task offloading algorithm based on user behaviour prediction is proposed. In a complex environment with large differences in resource conflicts and task time-dependent constraints, the training process of the MADDPG algorithm is improved to avoid a situation in which the local decision-making of single-agent algorithms is difficult to comprehensively optimize the global benefits, reduce the offloading delay, and improve the global resource utilization. * Simulation results show that the proposed algorithm performs better than the DQN and DDQN single-agent algorithms in terms of both delay reduction and resource utilization improvement. In addition, the algorithm reduces the response latency by 6.7% and improves the resource utilization by 30.6% compared to the suboptimal offloading algorithm based only on MADDPG and reduces nearly 500 training rounds during the learning process, which effectively improves the timeliness of the offloading strategy. The structure of this paper is organized as follows. Section 2 summarizes relevant work on task offloading. Section 3 describes the construction of a model related to timing-dependent task offloading and models the offloading problem. In Section 4, the proposed user-prediction-based timing-dependent task offloading algorithm is presented. Section 5 analyzes the simulation's experimental results. Finally, the contribution of this paper is summarized in Section 6.
## 3 Results and Discussion
### Case Study in Multi-Layered Soils
#### 3.3.3. Punch-Through Risk Assessment and Verification  
Integrated analysis of the actual penetration depth (8 m) and stratigraphic configuration indicated a potential punch-through risk at the LM-4/5 interface, representing a typical sand-over-weak clay stratigraphy. Equation (13) was consequently applied for punch-through assessment. The calculated ultimate bearing capacity of the LM-5 clay layer, \(q_{u,w}\) = 412 kPa, exceeded the equivalent bearing pressure transmitted through the sand layer, \(q_{u,e}\) = 365 kPa \(<q_{u,w}\).  
The CEL-derived bearing capacity curve (Figure 23) exhibited peak resistance, \(Q_{peak}\) = 8400 t, at a 12 m penetration, followed by reduction to a minimum value, \(Q_{post-peak}\) =7300 t, with further penetration. The preloading load \(Q_{pre}\) = 4707.5 t demonstrated non-inclusion within the (\(Q_{post-peak}\), \(Q_{peak}\)) range; compliance with 1.5\(Q_{pre}\) < \(Q_{peak}\); and a depth differential of \(>\)0.5\(B\) between the intersection points of \(Q_{pre}\) and the peak bearing capacity segment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Penetration Depth (m)** & **Error (\%)** & **Error Analysis** \\ \hline CEL & 7.8 & \(-\)2.5 & Soil-plugging behavior accurately captured \\ Single-layer theory & 10.0 & +25 &
\begin{tabular}{c} Overestimation of shallow sand layer \\ bearing capacity \\ \end{tabular} \\ Multi-layer theory & 6.9 & +14 & Significant error accumulation observed \\ \hline \hline \end{tabular}
\end{table}
Table 6: Penetration depth prediction accuracy at Lemen Offshore Wind Farm.  
Figure 23: Results of penetration depth calculation and punch-through analysis for multi-layered soils.  
Therefore, both theoretical and numerical analyses indicated a negligible punch-through probability at this site. Field operational data confirmed this prediction, validating the methodological reliability for multi-layered soil assessment.
# An Improved Genetic Algorithm for Solving the Semi-Soft Clustered Vehicle Routing Problem
## 5 Results and Discussion
### Computational Experiments
#### 5.3.4 Comparisons of Results for the SemiSoftCluVRP Instances  
To analyze the influence of the hard-soft attributes of clusters on the experimental results, we set up two groups of comparative experiments. The first group is the comparison of the results of the SemiSoftCluVRP instances with the corresponding CluVRP instances, and the second group is the comparison of the results of the SemiSoftCluVRP instances with the corresponding SoftCluVRP instances.  
For the first group of comparative experiments, the results are shown in the left parentheses in Column 5 of Table 1. From these results, it can be seen that compared to the results of the CluVRP, 55 of the results of the SemiSoftCluVRP have been improved, accounting for 71.43% of the total number of instances, with a maximum improvement of 6.50%. From the perspective of the type of instances, the number of improved results for A, B, P, and M-type instances accounted for 74.07%, 60.87%, 79.17%, and 66.67% of their respective total numbers, respectively; the maximum improvements in results were 4.93%, 2.41%, 6.50%, and 1.12%, respectively. Therefore, in the CluVRP, relaxing the hard attributes of some clusters to soft can help reduce the logistics costs of enterprises, but the specific effect is also related to the type of instance, as well as further factors such as the location of customers and depot.  
For the second group of comparative experiments, the results are shown in the right parentheses in Column 5 of Table 1. From these results, it can be observed that compared to the results of the SoftCluVRP, the objective function values of 54 corresponding SemiSoftCluVRP instances increased, accounting for 70.13% of the total number of instances, with a maximum increase of 7.52%. From the perspective of the type of instances, for A, B, P, and M-type instances, the number of instances with increased objective function values accounted for 62.96%, 56.52%, 87.50%, and 100% of their respective total numbers, respectively; the maximum increments in results were 7.52%, 3.27%, 7.30%, and 3.01%, respectively. Therefore, in the SoftCluVRP, if some clusters are enhanced to be hard, it will lead to an increase in the objective function values of most instances, with the magnitude of the increase varying depending on the type of instance. However, overall, it can be shown that the hard attributes of clusters have a significant impact on the logistics costs of enterprises.
## 2 Basic Theory
### Assessment Model--Fuzzy Comprehensive Assessment Model  
Because there are fewer fault records of the flight control system operating data, common neural network methods and gray clustering methods are less accurate. Therefore, a fuzzy comprehensive assessment method is selected to evaluate the health status. The method divides the characteristic parameters into a number of intervals, constructs a fuzzy assessment matrix of characteristic parameters in a specific channel, weights the matrix row by row to obtain each channel's assessment vector and finally outputs the channel health index.  
Step1: Establish a health status interval set: This interval is a collection of different health levels of each channel's characteristic. Assuming there areastatus levels, the health status interval set can be expressed as:  
\[L=\left\{\begin{array}{cccc}l_{1}&l_{2}&...&l_{m}\end{array}\right\}, \tag{2}\]  
where \(L\) represents the health status of each characteristic parameter and \(l_{i}\) represents the corresponding health level.  
Step2: Calculate the membership vector of each characteristic parameter: through the fuzzy calculation of a health status interval as mentioned above, the corresponding factor membership vector can be obtained as:  
\[r_{ij}=\left\{\begin{array}{cccc}r_{ij}^{1}&r_{ij}^{2}&...&r_{ij}^{m}\end{array} \right\}, \tag{3}\]  
where \(r_{ij}^{m}\) is the membership degree of the \(j\)-th characteristic parameter in the \(i\)-th channel to the \(m\)-th health state.  
Step3: Construct a fuzzy assessment matrix: by calculating the membership vector of each characteristic parameter and combining the membership vectors of all the characteristic parameters in each channel, a fuzzy assessment matrix is constructed:  
\[R_{i}=\left(\begin{array}{cccc}r_{i1}^{1}&\ldots&r_{i1}^{m}\\ \vdots&\ddots&\vdots\\ r_{in_{i}}^{1}&\ldots&r_{in_{i}}^{m}\end{array}\right)=\left(\begin{array}[] {cccc}r_{i}^{1}&r_{i}^{2}&\ldots&r_{i}^{m}\end{array}\right), \tag{4}\]  
where \(i\) is the number of health state intervals and \(n_{i}\) is the number of channel characteristic parameters.  
Step4: Determine the weight vector of characteristic parameters in each channel: based on the characteristic parameter selection in Section 2.1, the rough set reduction method in Section 2.4 below is used to identify the importance of the factor to the upper factor, and then construct the weight vector as:  
\[w_{i}=\left\{\begin{array}{cccc}w_{i1}&w_{i2}&...&w_{in_{i}}\end{array} \right\}\, \tag{5}\]  
Step 5: Calculate the assessment vector:  
\[b_{i}=w_{i}R_{i}=\left\{\begin{array}{cccc}b_{i}^{1}&b_{i}^{2}&...&b_{i}^{m }\end{array}\right\}, \tag{6}\]  
where \(b_{i}\) represents the assessment vector of the \(i\)-th channel.  
Step 6: Calculate the channel health index: the channel health index is obtained by weighting and normalizing the assessment vector of each channel:  
\[SCHI_{i}=\frac{\sum\limits_{k=1}^{m}b_{i}^{k}(m-k+1)}{m\sum\limits_{k=1}^{m}b_ {i}^{k}}, \tag{7}\]  
where \(m\) is the number of health levels in the health status interval and \(k\) is the number of elements in \(b_{i}\).
# Structural Identification and Coagulation Effect of _Flammulina velutipes_ Polysaccharides
###### Abstract  
Two polysaccharides were isolated successfully from _Flammulina velutipes_ and identified as CHFVP-1 (24.44 kDa) and CHFVP-2 (1497 kDa). Based on the results of Fourier transform-infrared spectroscopy (FT-IR), gas chromatography (GC), gas chromatography-mass spectrometry (GC\(\rightarrow\)MS), and nuclear magnetic resonance (NMR) spectroscopy regarding the structure of CHFVP-1 and CHFVP-2, CHFVP-1 was constructed with the backbone of\(\rightarrow\)6)-\(\alpha\)-D-Galp-(1\(\rightarrow\) and the branch of Galp by an \(\rightarrow\)3,6)-\(\alpha\)-D-Manp-(1\(\rightarrow\)attached with T-\(\beta\)-D-Glc or t-\(\kappa\)-L-Fucp side chains. Meanwhile, the CHFVP-2 was a glucan with the construction of \(\rightarrow\)6)-\(\beta\)-D-Glc\(p\)-(1\(\rightarrow\) and T-\(\beta\)-D-Glc\(p\). Moreover, the coagulant activity in vitro of CHFVP-1 and CHFVP-2 was evaluated, and the results showed that CHFVP-1 exerts procoagulant activity by shortening the activated partial thromboplastin time (APTT) and thrombin time (TT), while CHFVP-2 did not reveal a definite coagulant activity. The finding would benefit the further application of _F. velutipes_ in the field of medicine.  
polysaccharides; _Flammulina velutipes_; identification; coagulation effect 10.390/app1104736  
11
# Hyperspectral Imaging to Evaluate the Effect of Irrigation Water Salinity in Lettuce
## 3 Results and Discussion  
### Effect of Salinity on Water Content and Osmotic Potential  
The water content and osmotic potential data are shown in Table 1. The water content of the leaves decreased when saline concentration increases from 50 mM NaCl to 150 mM NaCl, while similar values were obtained in the control and in the samples subjected to 50 mM NaCl. The same trend can be observed with the osmotic potential of the leaves, but in this parameter distinguishing also between control and 50 mM NaCl, being significantly more negative than control. Salinity increase can induce the accumulation of ions, mainly Na\({}^{+}\) and Cl\({}^{-}\), in the leaves. This fact would be in accordance with the results shown in Table 1, since an accumulation of ions implies a water content reduction and a more negative osmotic potential in the leaves. These results confirm that the salinity levels studied are actually having an effect on the leaves.
# A Compact Quad-Element UWB-MIMO Antenna System with Parasitic Decoupling Mechanism
## 2 Antenna Configuration
### UWB-MIMO Antenna Configuration  
The proposed MIMO antenna design has four identical radiating elements. Four antennas arranged in MIMO configuration are shown in Figure 1. Rear view of the MIMO antenna is shown in Figure 1. Partial ground plane with tapered top edges with certain defects have been introduced on the rear-side to achieve wide impedance bandwidth. The ground plane is defected with slits and stubs on the both sides. Antenna 1, 2 and 3, 4 are placed side-by-side whereas antenna 1, 4 and 2, 3 are in across configuration with respect to each other.
## 4 REEMR: Reliable Energy-Efficient Multi-Level Routing Algorithm
### Fuzzy Petri Net Based Multi-Hop Routing
compttruth(\(\alpha(p_{s})\)) begin if(IRS(\(p_{s}\))=\(\phi\) exit else for all \(p_{k}\in\)IRS(\(p_{s}\)) if \(\alpha(p_{s})\geq f\) a transition is enabled, create a new leaf node \((p_{k},\alpha(p_{k}))\) in the tree, and create a new branch labelled \(\mu\) is directed from \((p_{s},\alpha(p_{s}))\) to \((p_{k},\alpha(p_{k}))\), where \(\alpha(p_{k})=\alpha(p_{s})\times\mu\). if \((p_{k}=p_{j})\) \(p_{k}\) is called success leaf node else suppose \(p_{s}=p_{k}\) call compttruth(\(\alpha(p_{s})\)) endif endif endfor
```  
The branch from \(p_{s}\) to \(p_{j}\) is called a reasoning path, namely a route from the source cluster head to the base station. Let \(Q\) be a set of success leaf nodes, \(Q=\{(p_{j},\alpha_{1}),(p_{j},\alpha_{2}),\ldots,(p_{j},\alpha_{l})\}\), where \(\alpha_{l}\in[0,1]\). Set \(z\) = Max(\(\alpha_{1},\alpha_{2},\ldots,\alpha_{l}\)). The truth degree for the goal place \(p_{j}\) is \(z\). Consider the topology in Figure 8. To choose a reliable route, cluster head \(p_{1}\) broadcasts a RREQ packet to its neighbors \(p_{2}\), \(p_{4}\), and \(p_{5}\), until the base station receives the RREQ packet. The base station \(p_{10}\) selects a reliable route using the fuzzy reasoning algorithm After performing the knowledge inference, a route sprouting tree can be illustrated in Figure 9. The different branches in the sprouting tree represent the different routes. From the sprouting tree, we can get 10 routes from the source cluster head to the base station. Comparing the truth degree of the base station, \(\text{Max}(0.47,0.53,0.41,0.46,0.52,0.59,\)\(0.58,0.66,0.39,0.43)=0.66\). The larger truth degree means higher reliability of the routes. Therefore, the route \(1\to 2\to 6\to 9\to 10\) should be chosen,which ensures the high reliability and low packet delivery delay in communication.
# A Tweak-Cube Color Image Encryption Scheme Jointly Manipulated by Chaos and Hyper-Chaos
## 5 Performance Analysis
### Noise Attacks Analysis  
When the encrypted image is transmitted through the physical channel, it may be damaged by attackers [38]. Therefore, the cryptosystem should have the ability to resist noise attacks. That is, the cipher image after being attacked can still recover the information conveyed by the original image. In the experiment, we added Gaussian noise, Salt and pepper noise, and Speckle noise to the three cipher images in Figure 6b. As displayed in Figure 11, the decrypted images can still be successfully identified after being assaulted.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{3}{c}{**Plain Image**} & \multicolumn{3}{c}{**Cipher Image**} \\ \cline{2-7}  & **R** & **G** & **B** & **R** & **G** & **B** \\ \hline Couple & 6.2499 & 5.9642 & 5.9309 & 7.9973 & 7.9974 & 7.9973 \\ House & 6.4311 & 6.5389 & 6.2320 & 7.9970 & 7.9973 & 7.9967 \\ Tree & 7.2104 & 7.4136 & 6.9207 & 7.9972 & 7.9973 & 7.9971 \\ Lena & 7.2682 & 7.5901 & 6.9951 & 7.9993 & 7.9993 & 7.9994 \\ Peppers & 7.3319 & 7.5242 & 7.0793 & 7.9992 & 7.9993 & 7.9992 \\ Baboon & 7.7067 & 7.4744 & 7.7522 & 7.9992 & 7.9994 & 7.9993 \\ Lena in [35] & - & - & - & 7.9872 & 7.9875 & 7.9869 \\ Lena in [36] & - & - & - & 7.9970 & 7.9972 & 7.9970 \\ Lena in [37] & - & - & - & 7.9972 & 7.9973 & 7.9972 \\ Peppers in [35] & - & - & - & 7.9869 & 7.9880 & 7.9884 \\ Peppers in [37] & - & - & - & 7.9971 & 7.9975 & 7.9974 \\ Baboon in [35] & - & - & - & 7.9876 & 7.9879 & 7.9876 \\ Baboon in [37] & - & - & - & 7.9972 & 7.9972 & 7.9972 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The results of information entropy.
## 4 A Case Study: DQN-Based Intelligent Thermal Management System  
### Implementation of Digital Twin Platform for IDC Prototype  
According to the digital twin framework of [PERSON] et al. [37], a scaled data center experiment prototype with sensors and control hardware (Arduino and Actuators) are established as physical entity. The virtual entity built in Unity includes a 3D model, information display window and control widgets. Services include the learning-based prediction module, DQN decision-making module and CFD simulation module. All devices in the system are wired, which allows data and signals to be transmitted internally.  
Figure 7 displays the user interface, where real-time temperature sensor data is visualized in the bottom-left chart shown in Figure 7. Based on the temperature readings, CRAC temperature, and server power, the fast thermal environment reconstruction module predicts a two-dimensional thermal map which updates every five seconds in the upper-left figure illustrated in Figure 7. From the predicted thermal field, the deep Q-learning network identifies potential local hotspots and optimizes the temperature setpoint for the CRAC units. According to the new CRAC temperature and server powers, the power usage effectiveness can be calculated as the total power divided by the total server power and displayed on the upper-right panel in Figure 7. The optimization process of the Deep Q-Learning network can be seen on the bottom-right figure in Figure 7, where the reward curves are plotted. The inclined profiles of the total rewards are expected during the optimization process.  
An intelligent cooling decision module is designed based on DQN, which receives the sensors' value in an IDC test-bed and temperature map generated by a prediction module to give an optimized cooling policy to the digital twins. Accordingly, the monitor can manipulate according to the DQN decision directly. The above functional modules are  
Figure 7: Digital twin system of IDC prototype based on Unity. (**a**) The UI of virtual twin. (**b**) Dynamic charts of real-time temperature value from sensors in testbed. (**c**) Fast reconstructed temperature field from surrogate model. (**d**) The real-time indicators of current status. (**e**) Reward curves of DQN decision process.  
integrated in the digital twin system. The scope of the IDC digital twin system is illustrated in Figure 8.  
As a result, the DQN decision agent in the Digital Twin system is not only capable of recommending an AC set temperature value for the current measured condition but also of exhibiting all the evolution records of thermal management decisions and rewards.