esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Quantitative Data** & **Qualitative Data** \\ \hline \multirow{3}{*}{Usability} & The average score of the Usability Evaluation System (SUS) was 80, indicating a very satisfactory user experience. & Users expressed high satisfaction with the ease of use of the interface, highlighting the simplicity and clarity of the functions. \\ \hline \multirow{3}{*}{User Experience} & The average User Experience Quality Scale (UEQ-S) score was 1.194, indicating a positive experience in both pragmatic and hedonic aspects. & Users reported an emotionally satisfying experience, highlighting the visual appeal and novelty of the interface. In addition, they found the interface efficient and easy to use in their daily tasks. \\ \hline \multirow{3}{*}{Reliability} & The internal consistency of the pragmatic and hedonic quality scales, assessed by Cronbach’s Alpha coefficient, was high (0.84 for pragmatic quality and 0.81 for hedonic quality), indicating reliability of the results. & Users demonstrated consistency in their responses in both pragmatic and hedonic aspects, which supports the consistency of the findings obtained through different evaluation methods. \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparative analysis of quantitative and qualitative data. it is important to recognize that the perception of usability and user satisfaction may be influenced by subjective and contextual factors that were not fully captured in this study. For example, cultural preferences, level of familiarity with the technology, and individual differences in aesthetic perception could influence the evaluation of the mobile interface.
# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
###### Abstract
* Pre-emption of low priority traffic (can be served dynamically in a best-effort way) upon arrival of higher priority traffic with more stringent delay budgets. * Reduction in power consumption of M2M UEs with DRX-enabled adaptive SPS mechanism without compromising their QoS requirements. The rest of the paper is organized as follows: Section 2 provides the background of this work, Section 3 describes the proposed multi-service adaptive SPS algorithm, Section 4 explains the implementation of DRX within the proposed framework to achieve power saving, Section 5 presents the simulation parameters. In Section 6, we discuss the results and finally, Section 7 draws the conclusion.
## 4 Magnet Stress Simulation and Analysis
### Magnet Coil and Mandrel Stress Results and Analysis  
After determining the preload parameters using the method mentioned earlier, calculate the stress distribution of the magnet under the preload condition. Add generalized stretching modules in non-local coupling to the domain where each coil is located in COMSOL to obtain radial and circumferential stretching vectors. Afterwards, radial and circumferential Lorentz force loads were added to the coil domain in the electromagnetic module, and the radial and circumferential stress distributions for each coil were calculated as shown in Figures 9 and 10. From Figure 9, it can be seen that the maximum radial stress of coils 1 and 3 is \(-\)2.13 \(\times\) 10\({}^{-3}\) MPa, close to 0. At the same time, the maximum radial stress of coil 4 is 5 MPa, which is still lower than the allowable shear stress of epoxy resin of 20 MPa.  
Regarding circumferential stress, the maximum value for NbTi is found to be 79.2 MPa in Figure 10. This value is significantly lower than the allowable stress limit of 250 MPa at 4.2 K. In the case of Nb\({}_{3}\)Sn coils, the engineering standard is often gauged by the ultimate yield strain, which is 0.2% of the material's strength [19]. As illustrated in Figure 10, the maximum cyclic strain observed is 0.0462%, which falls well below the permissible strain value for the material at 4.2 K.  
Figure 11 shows the radial stress changes of magnet coils 1-3 before and after adding preload force. It can be seen from the figure that after adding coil pre-tightening force, the peak radial stress of each coil dropped from the previous 11.94 MPa, 2.89 MPa, and 1.81 MPa to negative values, respectively.  
Figure 8: (**a**) P1P2 factor response surface; (**b**) P2P3 factor response surface; (**c**) P1P3 factor response surface.  
the mandrel assembly part reaches 232 MPa. This value is lower than the permissible stress threshold of 316L stainless steel, which is 286 MPa at 4.2 K. The maximum stress predominantly occurs at the central region of the mandrel, specifically in the middle and upper parts of the connecting block.  
Through calculation and simulation electromagnetic and stress analysis, the following conclusions are drawn: the magnet can be designed to operate stably in theory, and it is feasible to manufacture large-aperture high-field strong-gradient magnets with the structure described in this article.
# An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
## 4 Discussion
### The Transferability of the Proposed Method  
The repeat cycle of Sentinel-2 satellites is five days, which means that PWV data cannot be retrieved using Sentinel-2 images on most days. Therefore, it is necessary to analyze the transferability of applying this method to other satellites to generate complementary PWV products. The key to PWV inversion is that the satellite sensor needs at least one water vapor absorption channel and an adjacent atmospheric window channel. GaoFen-5 (02) is one of the satellites of China's GaoFen Special Project, and the multi-view polarization camera Directional Polarimetric Camera (DPC) is one of the main payloads. Since DPC has a water vapor absorption channel at 910 nm and an atmospheric window channel at 865 cm, the proposed method can be applied to PWV inversion with DPC. Zhuhai-1 is the first satellite constellation built and operated by a privately listed company in China. The hyperspectral satellite has a water vapor absorption channel (B32 centered at 940 nm) and an atmospheric window channel (B27 centered at 866 nm). The method presented in this paper can also be used to retrieve PWV with this satellite. In addition, this method works not only with high-resolution satellites, but also medium-resolution satellites, such as FY-3D MERSI-2, which has one atmospheric window channel (B4 centered at 865 nm) and three water vapor absorption channels (B16 centered at 905 nm, B17 centered at 936 nm and B18 centered at 940 nm, respectively). Each water vapor absorption channel has a different sensitivity to the same atmospheric conditions. For example, B16 is most sensitive to humid conditions due to its weak absorption, while B17 has strong absorption and is most sensitive to dry conditions [37]. Depending on the study area's characteristics, different channel ratios can be selected, or the PWV of the three channel ratios can be weighted and summed to obtain comprehensive results.
# Laser Inter-Satellite Link Visibility and Topology Optimization for Mega Constellation
## 4 Results and Discussion
### Comparison of Coverage Performance  
This paper establishes four groups of coverage performance comparisons and provides the global coverage of the Starlink and OneWeb constellation. Figure 7(a) is the Starlink constellation with F = 1, Figure 7(b) represents the Starlink constellation with F = 1, Figure 7(c) represents the OneWeb constellation with F = 1, and Figure 7(d) represents the OneWeb constellation with F = 9. From the graph analysis, the following conclusions can be drawn:  
* OneWeb's constellation of 720 satellites essentially provides global coverage, with polar regions receiving optimal coverage. Between 60\({}^{\circ}\) north and south latitude, when F = 1, the constellation N Asset Coverage is up to 85; when F = 9, the constellation N Asset Coverage is up to 90. In polar regions, when F = 1, the constellation N Asset Coverage is up to 110; when F = 9, the constellation N Asset Coverage is up to 130.
* The 1584 satellites in the first phase of Starlink have not yet covered all polar regions; optimal coverage can be achieved between 30\({}^{\circ}\)-50\({}^{\circ}\) north and south latitude. Between 60\({}^{\circ}\) north and south latitudes, when F = 1, the constellation N Asset Coverage is up to 85; when F = 11, the constellation N Asset Coverage is up to 130. The 0-fold coverage in the region above 60\({}^{\circ}\) north-south latitude is determined by the orbital inclination, and the coverage will be enhanced by the deployment of an orbital surface with an inclination of 97.5\({}^{\circ}\).  
Figure 8: _Cont._Comparatively, it can be noted that setting the optimal phasing factor can significantly improve the constellation coverage performance, and for the majority of users distributed between 60\({}^{\circ}\), Starlink showcases superior coverage performance than OneWeb.  
Due to the superiority of the Starlink constellation coverage performance, the following analysis is mainly performed on the Starlink constellation with F = 11. The distance between any two satellites in the generated constellation model is observed to show the visibility relationship between any satellites in the form of a graph, and to describe the inter-stellar link AER characteristics, which is the key and basis for analyzing the constellation topology.
# Pitfall of the Strongest Cells in Static Random Access Memory Physical Unclonable Functions
## 2 Related Work
### Structure of SRAM PUFs  
The power-on value of a 6T SRAM cell is basically determined by manufacturing process deviation [24], but it is also disturbed by other environmental factors, such as temperature and the ramp-up time of the power supply [25]. This means the power-on value has slight noise. To ensure the key can be correctly extracted from the SRAM PUF each time, a fuzzy extractor is needed to process the noisy SRAM responses, correct the noise by using the helper data generated in the enrollment step, and then, recover the key. Figure 3 shows the basic structure of SRAM PUFs. Error correction code in fuzzy extractors is usually concatenated with two codes. The repetition code \(C_{rep}(n,k,t)\)[16], where \(n\) is the length of the input data, \(k\) is the dimension of the input data and \(t\) is the number of errors it can correct, can be implemented with only \(2\times(n-1)\) exclusive OR (XOR) gates. Therefore, repetition code is very efficient for use in PUFs as one of the error correction codes.  
There are two phases in PUF applications: the enrollment phase and reconstruction phase. The helper data, which is generated in the enrollment phase, is used to recover the secret key in the reconstruction phase. The functions of repetition code in two phases are as follows:  
_Enrollment phase_: Challenge the SRAM array, and measure the response \(R\), and calculate the helper data \(H=R\ mod\ G\), where \(G\) is the generator polynomial. The helper data is stored in public non-volatile memory and can be accessed by hardware and/or software.  
_Reconstruction phase_: Challenge the SRAM array, and measure the response \(R^{\prime}\), perform error correction process to obtain \(R_{ecc}=(R^{\prime}\ mod\ G)\oplus H=E_{rr}\ mod\ G\), where \(E_{rr}\) is the error vector, recover \(E_{rr}\) from \(R_{ecc}\), and then calculate the corrected \(R^{\prime\prime}=E_{rr}\oplus R^{\prime}\). The error-corrected response \(R^{\prime\prime}\) is ideally equal to \(R\).
## 3 Results and Discussion
### Pharmacophore Modeling  
The nature and three-dimensional conformation of compound functionalities in ligands essential for molecular interactions with macromolecular targets can be ascertained using 3D pharmacophores. Compounds' activities must be categorized into more general pharmacophore features, such as hydrogen donors, hydrophobic areas, aromatic ring systems, acceptors of hydrogen bonds, and negatively and positively ionizable groups [49].  
Pharmacophore modeling requires a thorough comparison of all conjugate structures with a well-selected database of substances that have been scientifically proven to have an inhibitory effect against ER\(\alpha\). For filtering and reference, this database--taken from the Database of Useful Decoys: Enhanced with ESR1 code--serves as the standard [50].  
Validation is required for the pharmacophore model created from the active compound database. This validation highlights the validity of the pharmacophore-based screening approach in identifying putative bioactive compounds among the glycine-conjugated \(\alpha\)-mangostins that target ER\(\alpha\) receptors [39]. An AUC\({}_{100}\) value of 0.83 was found in the validation results (Figure 3), demonstrating the suitability of the used methodology. The AUC\({}_{100}\) value's closeness to one indicates a good screening strategy and a thorough screening process.  
Figure 2: Predictive pharmacokinetics of \(\alpha\)-mangostin and its glycine conjugates.  
The results from the pharmacophore model showed that a model drug needed to have two hydrophobic groups (yellow spheres), one hydrogen bond accepting group (red sphere), and one hydrogen bond donor group (green sphere) (Figure 4).  
After establishing the pharmacophore model, all glycine-conjugated \(\alpha\)-mangostins were thoroughly screened to find putative ER\(\alpha\) antagonist ligands. All the conjugates that complied with the model pharmacophore are shown in Figure 5.  
Following that, the pharmacophore model (Figure 4) obtained from the active databases and the proposed conjugate model were aligned. Aligned conjugates showed that only Am3 Gly was not able to properly align with the pharmacophore model by interacting with hydrogen bond donors and acceptors, as well as with two hydrophobic moieties (Figure 5). This result highlights the capacity of Am1 Gly and Am2 Gly to act as ER\(\alpha\) antagonist ligands in accordance with the defined pharmacophore parameters and support continued screening to molecular docking.  
Figure 4: Pharmacophore model against ER\(\alpha\) derived from active databases. Hydrogen bond donor (green sphere), hydrogen bond acceptor (red sphere), and hydrophobic area (yellow spheres) are the properties of the model.  
Figure 3: Pharmacophore database validation.
## 2 Proposed Method
### Network Details
#### 2.2.1 Input Module
Considering the fact that the load at current timestamp \(t_{now}\), \(f(t_{now})\), would affect the response at \(t_{now}\) and those of the future timestamps \(t_{now}+\Delta t,t_{now}+2\Delta t,\cdots\), where \(\Delta t\) is the sampling time interval, responses in a time window \([t_{now},t_{now}+M\Delta t]\) are used for the identification of \(f(t_{now})\), where \(M\) is the size of the time window. We assume that the contribution of the response outside the time window, e.g., response at time \(t_{now}+(M+1)\Delta t\) would be minor. Therefore, the dimension of the input module would be \((9\times N,M)\), where \(N\) is the number of response measurement points, \(M\) the size of time window.
# Mathematical Foundations and Implementation of CONIKS Key Transparency
## 2 Related Work
### Security Assumptions and Models
#### 2.3.4 Code Implementing  
The implementation of CONIKS in Python involves coding both the core algorithmic components and the cryptographic operations that are necessary for key transparency. At the heart of the implementation is the Merkle tree, a data structure that ensures the integrity and transparency of key management. The Merkle tree is constructed by hashing the leaf nodes, which represent the raw key data, and then recursively hashing parent nodes to build the tree upwards to the root. This hierarchical structure allows for the efficient verification of data integrity, as any change in the leaf nodes will propagate through the tree, altering the root hash. The provided Python code snippet demonstrates the creation of a _MerkleTree_ class (Figure 2) that builds the tree from a list of leaves and computes hash values using the SHA-256 hashing algorithm.  
In addition to Merkle tree construction, digital signature functionality is implemented to authenticate and verify data. The _cryptography_ library in Python is used to handle key generation, signing, and verification processes. The _generate_key_pair_ function creates a pair of RSA keys, while the _sign_message_ function generates a digital signature for a given message using the private key (Appendix A). Verification is handled by the _verify_signature_ function, which checks the signature against the message using the public key.  
This ensures that the data have not been altered and that the signature is valid. Together, these components form the basis of the CONIKS implementation, providing both the cryptographic assurances and the practical tools needed for secure key management.  
Figure 2: Merkle tree construction.
###### Abstract  
The utilization of low-concentration coalbed gas as an energy source in coal mines has garnered significant attention due to the intensifying global warming trend. Achieving a uniform gas mixture is essential for the effectiveness of various gas utilization technologies. In this study, we conducted a series of numerical studies on the pressure loss and mixing uniformity of a swirl-type static low-concentration coalbed methane (SLCCM) mixer with spiral blades as turbulence elements. The results indicate that installing spiral blades at the low-concentration gas inlet can significantly improve gas mixing uniformity, while installing spiral blades at the air inlet has a relatively small impact on gas mixing uniformity. When spiral blades are installed at both the low-concentration gas and air inlets, and the spiral direction of the two sets of blades is the same, the pressure loss of the mixer is smaller than that of the mixer with opposite spiral directions. Moreover, the mixing uniformity of the former is better than that of the latter when the length of the mixing zone is shorter. However, as the length of the mixing zone increases, the mixing uniformity of the mixer with opposite spiral directions of the two sets of blades will be significantly improved and better than that of the mixer with the same spiral direction. When a set of spirals with opposite directions to the blades at the low-concentration gas inlet is added at the forefront of the mixing zone, the mixing uniformity at the outlet of the mixer can be greatly improved, but it will increase the mixing pressure loss of the system. When the two spirals are in the same direction, the impact on the system pressure loss and mixing uniformity is relatively small. The numerical simulation results show that by setting a set of spiral blades with the same rotation directions at the inlet of low-concentration gas and at the forefront of the mixing zone (i.e., b2-type structure), the spatial non-uniformity of the mixing is about 5.24%, and the system pressure loss is less than 400 Pa, which is significantly better than the requirement of less than 15% spatial non-uniformity for engineering requirements. Moreover, with the increase in the length of the mixing zone, the spatial non-uniformity still tends to decrease.  
Keywords:rotational flow; static gas mixer; mixed characteristics; pressure loss +
Footnote †: journal: Applied sciences
# An Attention-Based CNN-LSTM Method for Effluent Wastewater Quality Prediction
## 3 Proposed Method
### Attention Mechanism  
As stated in Section 3.2, WWTPs adopt a push-flow design to flow the wastewater, so the quality of the effluent wastewater at the adjacent sampling time should be highly correlated. As shown in Figure 4, we calculated the PACF (Partial Autocorrelation Function) for four effluent wastewater quality indicators.  
In the figure, the zero point of the horizontal axis represents the current time \(t_{i}\), and the remaining points represent the previous times \(t_{i-1},t_{i-2},\ldots t_{i-n}\). The vertical axis represents the correlation between the value of the current time and the value of the previous time. The larger the value, the stronger the correlation, and the points outside the blue area represent significant correlation.  
Figure 3: CNN-LSTM structure.  
As can be seen from the figure above, for the four wastewater quality indicators, the wastewater quality data at the current moment are significantly correlated with the wastewater quality data at the previous two moments. With increasing time, the correlation of the data gradually becomes weaker. To extract the correlation between the wastewater quality data at the current moment and the wastewater quality data at the previous moment, as shown in Figure 5, we propose an attention mechanism for wastewater quality prediction.  
Figure 4: (**a**) PACF of effluent COD with 10 lags; (**b**) PACF of effluent NH3-N with 10 lags; (**c**) PACF of effluent TP with 10 lags; (**d**) PACF of effluent TN with 10 lags.  
Figure 5: Attention mechanism used in CLATT.  
Ref. [23] proposed an attention mechanism to aggregates global information. The attention mechanism used in this paper aggregates the information at adjacent moments, and the structure of the attention mechanism is similar to a Dense layer with several weight parameters corresponding to the time dimension of the output from LSTM. The weight is accumulated after being multiplied by the output from LSTM, which produces the predicted effluent water quality. The attention mechanism can be computed as follows:  
\[\mathrm{k_{i}}=\sum_{t}(\mathrm{h_{t}}\mathrm{h_{i}}) \tag{6}\]  
\[\mathrm{w_{i}}=\exp(\mathrm{k_{i}})/\exp\left(\sum_{t}(\mathrm{k_{t}})\right) \tag{7}\]  
\[\mathrm{Y_{t+1}}=\sum_{i}\mathrm{w_{i}}\mathrm{h_{i}} \tag{8}\]  
where \(\mathrm{h_{i}}\) represents the output from LSTM, \(\mathrm{w_{i}}\) is the weight of the corresponding output, and \(\mathrm{Y_{t+1}}\) refers to the predicted effluent wastewater quality.
## 4 Results
### Assessment of Exposed Assets  
Land flooding impacts both natural and anthropic environments, in ways that may be difficult to predict. In the case of ecosystems, complex feedback mechanisms and highly articulated interrelations may occur [115; 116]. In the case of critical infrastructures (CIs), coastal defences (e.g., jetties and groins), and confined disposal facilities (CDFs) of harbours, which generally show a gradual tendency to sink into the seabed, the time evolution of the ground motion may be nonlinear, and therefore difficult to predict using linear extrapolations of the present trends. Nevertheless, planning and adaptation strategies can be guided by the knowledge of the main present characteristics of the vulnerable areas [77]. Using the Corine Land Cover, we determined the assets exposed to inundation within the five coastal areas (see Table 5 and Figure 13). Cropland, Open Space with Little or No Vegetation (where present, like in the study area of Alghero-Fertilia-) are the most impacted ecosystems, followed by Wetland (in Follonica and Pontina's plains) and Water. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Study Area** & \multicolumn{3}{c}{**Piombino-Follonica**} & \multicolumn{3}{c}{**Marina di Campo**} & \multicolumn{3}{c}{**Alghero-Fertilia**} \\
**Year** & **2040** & **2070** & **2099** & **2040** & **2070** & **2099** & **2040** & **2070** & **2099** \\ \hline RSLR (cm) & 21.44 & 44.86 & 71.22 & 23.5 & 48.73 & 76.84 & 22.39 & 46.91 & 74.42 \\ SULS (km\({}^{2}\)) & 1.36 & 2.72 & 4.57 & 0.04 & 0.06 & 0.09 & 0.99 & 1.13 & 1.44 \\ \hline
**Study Area** & \multicolumn{3}{c}{**Rome**} & \multicolumn{3}{c}{**Latina-Sabaudia**} & \multicolumn{3}{c}{} \\
**Year** & **2040** & **2070** & **2099** & **2040** & **2070** & **2099** & & \\ \hline RSLR (cm) & 23.62 & 48.87 & 76.98 & 25.05 & 51.62 & 81.26 & & \\ SULS (km\({}^{2}\)) & 1.99 & 3.31 & 5.84 & 9.43 & 12.05 & 14.84 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Inundation data in reference years. Relative Sea Level Rise (RSLR) is a representative value that consider SLR and an average value of GM within the studied area. Surface Under Sea Level (SUSL) refers to the baseline digital terrain model (DTM) of 2010.