esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Connections with Climate Indices  
The spatial patterns and associated principal component time series for the first three modes of the REOF analysis (Figure 2) account for 26.5% of the subseasonal SIF variability in the 13-year time series. All three modes have strong signatures in the equatorial band and mid-latitudes. The leading mode in SIF anomalies (EOF1, 12.9%) accounts for nearly twice as much variance as other modes. EOF1 captures the well-known ENSO pattern, with most of the variance over continents in the tropical Pacific and Indian Oceans. The corresponding PC1 is dominated by interannual variability and correlated with the ENSO Nino 3.4 Index (r = 0.49). The strong El Nino in 2015-2016 promoted SIF (GPP) increases in western North America, Europe, and northeastern Asia by bringing higher air temperatures and more moisture and rain. At the same time, significant SIF (GPP) decreases were seen in continental regions in the western Pacific, South Africa, and South America, where severe droughts occurred in association with this El Nino event. The strong La Nina in 2011 presented a contrast, with impacts generally opposite those of El Nino.  
The second mode (EOF2, 7.8%) is strongest in the northern Atlantic continents. The associated PC2 displays a higher frequency than the PC1 and is significantly correlated with the NAO time series (r = 0.31). NAO transitioned between its negative and positive phases repeatedly in 2007-2019, which was generally followed by the variations in PC2. Regions on both sides of the northern Atlantic share similar EOF spatial patterns, suggesting that North American and European SIFs (GPPs) have similar reactions to the temporal variability of the NAO.  
The PC of the third mode (EOF3, 5.8%) is most strongly correlated with the PDO (r = 0.48). The EOF3 pattern shares some ENSO (EOF1) characteristics but is stronger at higher latitude and weaker in the tropics. The resulting pattern is a tripole with North America and Australia in phase with each other and out of phase with South America and South Africa. PDO transitioned from its negative phase to a positive phase around 2014, which was generally matched by PC3. The mode indicates that continental SIF (GPP) in North America and Australia generally decreased (increased) before (after) 2014.
## 3 Results  
### Effect of Water Content and Mixing Ratio  
The optimum water ratio to be implemented into the sample mixtures was obtained via a series of compaction tests. Based on the experimental results presented in Figures 3 and 4, an increase in the proportion of CaCO\({}_{3}\) additive leads to a significant rise in the optimum moisture content required for the compaction of clay soil, reaching up to 1.32 times the original value. Correspondingly, the maximum dry unit weight of the clay soil decreases by up to 1.10 times. In the case of nano-CaCO\({}_{3}\) incorporation, the optimum moisture content increases by up to 1.10 times, while the maximum dry unit weight experiences a reduction  
Figure 2: Model testing equipment: (**a**) plan; (**b**) section. (1—Loading Frame, 2—Motor Controlled Loading System, 3—Load Cell, 4—Loading Head, 5—Displacement Transducer, 6—Model Footing, 7—Sample, 8—Test Box, 9—Computer, 10—Data Collection Unit).  
of up to 1.03 times. These trends indicate that the addition of CaCO\({}_{3}\), particularly in nano form, influences the compaction characteristics of clay soils by modifying their moisture-density relationship. It was observed that as the ratio of nanomaterial added into the soil samples was increased, the required amount of water in the mixtures increased as well. This event originates from the water absorption capability of Ca in CaCO\({}_{3}\). Comparatively, nano-CaCO\({}_{3}\) exhibits a more stable behavior in terms of required water content and unit weight alterations which makes it a better alternative to CaCO\({}_{3}\).
## 2 Improvement of DPIV Algorithm  
### Window Deformation Iterative Multigrid Algorithm  
The WIDIM algorithm is an effective DPIV postprocessing algorithm. The WIDIM algorithm process is shown in Figure 3[35]. Firstly, a large interrogation window is used for the initial iteration calculation to obtain the initial displacement field. The displacement field is interpolated to obtain the pixel displacement field. Secondly, the interrogation window is deformed according to the pixel displacement field, and the interrogation window is reduced. The second iteration is performed and the displacement field is updated. Finally, the calculation and update are performed continuously until the interrogation window is reduced to the specified size.
## 4 Discussion
### Chemical and Physical Deterioration  
When submerged in purified water, rocks will react with water chemically or physically in three manners.  
1. The hydrolysis of quartz. In the case of silicate rocks under conditions of moisture, water as a corrosive agent hydrolyzes the quartz minerals. The hydrolysis of Si-O-Si bond in quartz lowers the activation barrier to subcritical crack growth as strong Si-O-Si bonds at crack tips are replaced by weaker hydrogen bonds [35; 36; 37; 38; 48]: \[(-Si-O-Si-)+(H-O-H)\rightleftharpoons(-Si-OH-HO-Si-).\] (1) At the same time, the increasing external stress inducing crack creation and propagation also promotes the quartz hydrolysis. Theoretically, the higher content of quartz leads to the greater UCS reduction due to water presence, as shown in Figure 8. Therefore, quartz hydrolysis plays an important role in weakening the UCS of the sandstone and the granite, while it is absent for the marble according to the mineral compositions of the three rock types listed in Table 1. From Figure 8, it is worth noting that the UCS reduction of granite is apparently lower than the predicted value, probably due to the hydrolysis of quartz being limited by its extremely low porosity (lower permeability).
2. The swelling of clay minerals. It is well-known that expandable clay minerals (e.g., smectite) cause high swelling stresses after water absorption, further expanding existing microfractures as well as creating new micro-fractures [28; 56]. The increase of crack density decreases the rock strength [57; 58]. In this study, this effect only acts on the sandstone containing smectite.
3. The dissolution of calcite. Calcite minerals are vulnerable to dissolution even in weak acidic environment [59; 60; 20; 56], as: \[CaCO_{3}+CO_{2}+H_{2}O\to 2 HCO_{3}^{-}+Ca^{2+}.\] (2) This will increase the porosity of rock and decrease the intergranular cohesion, which results in the UCS reduction of the sandstone and the marble.  
Figure 8: UCS reduction against quartz content (data from [4; 6; 25; 61; 62; 63; 64]). S, sandstone; G, granite; M, marble; Sh, shale.
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 4 Case Studies
### Case Study 1: The PQ8 Satellite  
The first case study examines the PQ8, a constellation under development at the University of Malta and illustrated in Figure 6. The PQ8 is a 1U CubeSat (10 \(\times\) 10 \(\times\) 10 cm\({}^{3}\) with a mass of 2 kg) hierarchically assembled from eight PQ1 satellites. Each PQ1 is formed by slicing the PQ8 in half twice, lengthwise and heightwise, resulting in eight 5 \(\times\) 5 \(\times\) 5 cm units, each weighing 250 g, closely resembling a 1p PocketQube in size and shape.  
The deployment sequence follows the architecture proposed in this article, forming intermediate configurations--PQ8 \(\rightarrow\) PQ4 \(\rightarrow\) PQ2 \(\rightarrow\) PQ1--as depicted in Figure 6a-d. The process begins with the PQ8 (\(S_{0,0}\)) at \(k=0\), which undergoes an initial disengagement maneuver, splitting into two PQ4 satellites (\(S_{1,i}\)) at \(k=1\). These are phased before further splitting into PQ2 satellites (\(S_{2,i}\)) at \(k=2\), and finally, eight independent PQ1 satellites (\(S_{3,i}\)) at \(k=3\).  
Figure 6: (**a**) PQ8 satellite. (**b**) PQ4 satellite. (**c**) PQ2 satellite. (**d**) PQ1 satellite.  
To model aerodynamic effects, the drag coefficients were first obtained, using the method proposed by [PERSON] et al. in [18] and outlined in Appendix A. The ballistic coefficients were then obtained through \(\beta=m/sc_{D}\). Table 4 presents the results, showing the ballistic coefficients for the PQ4, PQ2, and PQ1 stages. The PQ4 and PQ2 stages have identical ballistic coefficients, as their mass and surface area scale proportionally. In contrast, transitioning from PQ2 to PQ1 involves halving the mass while maintaining the same surface areas. To achieve this increased area and enhance differential drag in the PQ1 configuration, a deployable panel was added to the design.
## 3 Sample Environments
### Container-Less Measurements  
In some cases, contactless or container-less experiments are carried out to minimize the interaction of the sample with the sample cell or to reduce the background scattering due to window materials. This is particularly important for very high temperature experiments, where the sample-wall interaction can change the chemical composition of the sample [117]. So far, aerodynamic levitation, where a vertical gas stream keeps a droplet of material in the beam, is most commonly used to study materials at high temperature. Powerful lasers are used as a heating source [118]. For soft matter research, aerodynamic levitation appears to be less popular and acoustic levitation is used, instead [119]. A test experiment on the drying of lysozyme solution droplet has been reported [120]. Although this experiment was intentionally aimed at drying and thus increasing the protein concentration, it also highlights one of the method's shortcomings, namely solvent evaporation. The authors also noted that the strong 22 kHz soundwave, which helps to maintain internal mixing, also has the unwanted side effect of denaturing the protein.  
Independent of which levitation method one uses, a drawback is that one can only levitate droplets that are smaller than the average neutron beam, thus reducing the scattering intensity as well as possibly introducing parasitic scatter from the sample-air interface. Even if using a jet of liquid shooting through the beam, one will still be restricted to small sample sizes since it is difficult to create large, stable laminar flows. Another ingenious approach to contact-free measurements that mitigates the problem of size mismatch between sample and neutron beam is to create a free liquid film by flowing a solution between two wires [121]. See Figure 13.  
Figure 13: Container-less measuring device. A continuous stream of liquid is spread out by a nozzle between vertical wires. The free-standing film can be approximately matched to the size of a conventional neutron beam. Not shown is the bag filled with inert gas surrounding the system that is used to avoid the sample interacting with air moisture, resulting in an altered H-D ratio [121].  
A rather specialized container-less measurement is the formation of soot particles in a combustion process. Here the flame is placed on a vertical translation stage so that the distance between the flame-neutron beam interception point and the flame mixer can be varied. This method has been used at a number of synchrotron and neutron facilities [122]. Just as in similar SAXS experiments, the data quality remains low and suitable caution is required when fitting the data.  
Whatever the method used, one should be aware of the fact that container-less measurements do not mean that there is no sample-environment interaction. Oxidation, denaturing of protein, and exchange of deuterated for protiated water have been reported. It is also well-known that elongated molecules can orient themselves around air-water interfaces.
## 1 Introduction
The self-attention technique enables the model to concentrate on the most relevant segments of the input sequence, thereby enhancing its capacity to identify tiny irregularities. The suggested model is assessed using the WSN-DS dataset, and its performance is juxtaposed with existing methods to illustrate its effectiveness in detecting intrusions within WSN environments, achieving a precision of 98.63%. Our proposed method represents a substantial performance improvement. The major contribution of the model in the field of intrusion detection for WSNs lies in the novel integration of Conv1D, GRU, and Self-Attention mechanisms into a unified deep learning architecture that achieves both high detection accuracy and real-time performance, specifically addressing challenges unique to WSNs such as resource constraints, data imbalance, and temporal dependencies in attack patterns. 2. Hybrid Architecture for Temporal and Spatial Feature Extraction: The integration of Conv1D and GRU facilitates the model's ability to concurrently identify spatial patterns through convolutions and temporal dependencies through recurrent units in network traffic data, essential for detecting intricate attack behaviors in WSNs. 3. Incorporation of Self-Attention for Interpretability and Focused Learning: the Self-Attention layer empowers the model to dynamically prioritize important features such as anomalous packet frequency, sudden data rate increases, irregular routing intervals, and rare protocol combinations. These temporal signals are passed through the attention mechanism, which then learns to focus on the most predictive ones depending on the attack context, improving interpretability and the model's sensitivity to subtle attack signals--an essential property in low-data or noisy environments like WSNs. 4. Effective Handling of Class Imbalance by Integrating SMOTE in the Preprocessing Pipeline: The model effectively tackles severe class imbalance, a common issue in intrusion detection datasets, thereby ensuring fair detection across all attack categories, not just the majority class. The remainder of the paper is organized as follows: Section 2 provides a review of related work, while Section 3 introduces and describes the proposed model. Section 4 outlines the experimental setup, and Section 5 presents the ablation study. Section 6 details the results analysis, followed by a broader discussion in Section 7. Finally, Section 8 concludes the paper and highlights potential directions for future work.
# Assessment of Appropriate Geometry for Thermally Efficient CO\({}_{2}\) Adsorption Beds
## 3 Results and Discussion
### Adsorption Concentration  
The adsorption concentration in terms of the CO\({}_{2}\) molar fraction at bed exits is shown in Figure 5. As the inlet CO\({}_{2}\) molar fraction is 0.15, the saturation conditions are reached when the CO\({}_{2}\) outlet has 95% of the CO\({}_{2}\) inlet (y = 0.1425). However, when the CO\({}_{2}\) outlet reaches 5% of the CO\({}_{2}\) inlet (y = 0.0075), this status is called the breakthrough point. The breakthrough point is for the CO\({}_{2}\) separation applications to avoid the release of CO\({}_{2}\) into the atmosphere. This means that, in real applications, the adsorption process should be stopped at the breakthrough point. It is obvious from Figure 5 that the triangular cross-sectional bed has a higher breakthrough time and lower saturation time, which are preferable. The higher breakthrough adsorption time means that the bed is able to adsorb more CO\({}_{2}\), whereas a lower saturation time indicates a quick sorption process. The square cross-sectional bed comes second in terms of this advantage, while the circular one comes last. These results could be explained by the good cooling process during the adsorption process closer to the three corners of the triangular cross-section, which enables the bed to adsorb more CO\({}_{2}\). The cooling process is better for the triangular bed than the square bed.
# Smart Energy Meters for Smart Grids, an Internet of Things Perspective
## 2 Sensing Techniques
### Electricity Meters
#### 2.3.2 Grid Sensing
Event and signal processing can also be performed on derived waveforms such as the differential waveform, which is obtained by subtracting two synchronous parts of the waveform. Similarly, harmonic waveforms can be analyzed for different parameters by decomposing the waveforms using Fourier transform and can be further time stamped to generate harmonic synchro-phasor waveforms, which provide sparse bases for state estimation of the grid. Such synchro-phasors are used in topology identification, amongst other applications. Fourier decomposition and time stamping of differential waveforms give rise to differential harmonic synchro-phasors, which are used to detect fault location. These waveform analyses can be used at higher levels for fault detection, topology estimation, grid state estimation, fault location, energy leakage, efficiency, etc. The synchronization of waveform measurements gives rise to waveform measurement units (WMUs), which are increasingly being used in transient analysis. A typical parameter reported by WMUs is relative waveform difference (RVWD), which can be used for event detection and classification in transmission lines. Finally, the voltage and current measured is used to derive the power which is actually reported and extensively used for different applications in smart metering. The different types of power parameters calculated are the reactive power, load factor, true power factor, displacement power factor, load profiles, etc., which are employed in grid estimation, demand-side load management, peak shaving, energy forecasting applications, etc.
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 2 Basic Principles  
### Convolutional Neural Network  
A convolutional neural network (CNN) is a multilayer feed-forward neural network. A typical CNN model consists of an input layer, convolutional layer, activation layer, pooling layer, fully connected layer, and output layer [15]. The convolutional and pooling layers are often arranged in an alternating pattern. The structure is shown in Figure 1, and the main functions of the layers are described in the following paragraphs.
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## 7 Conclusions  
In this paper, we proposed a PCAC access control scheme that is suitable for power cloud environment. The PCAC scheme achieved access control from the data collection to data obtainment. A new action audit phase based on zero-knowledge proof, was also proposed, which verifies the user's identity without infringing on the privacy of Us. Finally, the storage and computation overhead of the PCAC were analyzed through both theoretical and experimental analyses. Compared to the existing CP-ABE access control schemes, the length of the master secret key in our scheme was shorter and the storage pressure of the power cloud was lower, and the time occupied by action audit was relatively low. Therefore, the PCAC scheme could satisfy high-frequency access control requirement in the power cloud. However, there were still some shortcomings in our scheme. In the access structure generation phase, our trapdoor also had a lot of limitations. Our access structure could not achieve \"NOT\" door and comparable attributes. Therefore, our future work will focus on the construction of these access structure.  
[PERSON] and [PERSON] designed the framework and wrote the manuscript. [PERSON] and [PERSON] collected the data. [PERSON] and [PERSON] mainly responsible for the formula analysis. [PERSON] verified the results of our work and conceived the experiments together. [PERSON] and [PERSON] discussed the results and contributed to the final manuscript.  
**Funding:** This work was supported by the Fundamental Research Funds for the Central Universities (2018 ZD06).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: Time Consumption in the Act_Audit Phase.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Attributes** & **Number of Us** & **100,000** & **300,000** & **500,000** & **1,000,000** \\ \hline
10 & 122 MB & 366 MB & 615 MB & 1.18 GB \\
15 & 168 MB & 505 MB & 842 MB & 1.65 GB \\
20 & 216 MB & 644 MB & 1.04 GB & 2.07 GB \\
25 & 260 MB & 789 MB & 1.28 GB & 2.57 GB \\
30 & 309 MB & 930 MB & 1.51 GB & 2.99 GB \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data Size Saved in the CKA.
# Biosynthesis of Rubbellins in _Ramularia collo-cysni_--Genetic Basis and Pathway Proposition
## 3 Discussion
### Proposed Pathway for Rubellin Biosynthesis  
The anthraquinone scaffold that forms the backbone of all rubellins has been previously suggested to be biosynthesised via a polyketide pathway, while the previously mentioned _recG_ NR-PKS in _R. collo-cygni_ may be responsible for the biosynthesis of this anthraquinone scaffold (Figure 6). InterProScan analysis [50] showed the presence of a nonreducing, iterative type I PKS with MdpG- or ClaG-like domains: ACP--acyl carrier protein; PT--product template; AT--acyl transferase; KS--keto synthase and SAT--starter unit acyl carrier protein transacylase. The next step in the pathway that leads to atrochrysone carboxylic acid **10** could be catalysed by RccF, an MdpF-like \(\beta\)-lactamase, which exhibits hydrolase activity especially considering that no termination thioesterase domain is present in RccG. Subsequently, atrochrysone carboxylic acid **10** would undergo decarboxylation to chrysophanol **7** by the decarboxylase analogue RccH. A tetrahydrooxynapthalene reductase T4 HN or the MdpC short-chain dehydrogenase (SDR) homologue, RccC, could then dearomatise one of the chrysophanol **7** units. Two chrysophanol **7** units could be conjugated by the unique cytochrome P450, RccM, which could be responsible for the dimerisation of these two anthraquinone units. The proposed candidate, RccM, exhibits 42% identity to ClaM, a fungal anthraquinone dimerisation P450 [23]; however, to the best of our knowledge, no double dimerising fungal oxidase is known to date. The putative rubellin biosynthetic pathway branches at this point in two directions leading to rubellins C **3** and D **4**. The anthraquinone unit of dimer **11** could be hydroxylated by RccD, a FAD-dependent monooxygenase-like protein. Both intermediates **11** and **12** would undergo oxidation to a seven-membered lactone ring by the rare BVMO, RccL. RccL shares 43% identity to other known BVMOs, such as MdpL as well as to AgnL3 in the agnstein biosynthetic pathway based on reciprocal BLAST (e-value \(<\) e\({}^{-99}\)) [42]. The last steps of both branches of this pathway may be spontaneous and lead to rubellin C **3**, D **4**, E **5**, and F **6**. It is important to note that the formation of previously observed 14-dehydro rubellin D **9** could be catalysed by the dehydatase-like protein, RccB.