esa-sceva/satcom-chunk-collection huggingface dataset:
# Gradient-Based Aerodynamic Optimization of an Airfoil with Morphing Leading and Trailing Edges
## 3 Results and Discussion
### Minimization of Drag by Morphing Leading Edge  
The first case involves the optimization shape design of morphing leading edge (mLE) to improve the aerodynamic performance in the cruise stage in terms of drag coefficient at a constant lift coefficient. The wing-box region starts from 10% of the chord to trailing edge, ensuring the morphing only occurs in the front 10% of the airfoil. In this case, the aerodynamic design variables are vertical position and the upper and lower skin radius of the leading edge. Chord-wise position of leading edge point and the remaining subset of CST coefficients are automatically determined through the morphing algorithm proposed in Section 2.2.3.  
Optimization starts from baseline airfoil and obtains the optimal solution after 10 major iterations. Optimization runs in parallel on a hexagon-core processor with frequency 3.2 GHz. It costs around 20 min for one iteration, including solving both flow and adjoint problem. The optimizer may invoke the flow solver several times between each iteration to evaluate the objective function with new design variables. The convergence history is presented in Figure 8a. The drag coefficient converges to 108.74 counts, with 16% drag reduction, while keeping a constant lift coefficient by adjusting the angle of attack from the initial 2.29 degrees to 2.20 degrees. An aerodynamic efficiency improvement of 19% is achieved by evaluating the change of lift to drag ratio. The vertical displacement \(z_{\text{TE}}\) varies from 0 to \(-0.0048\) m, and the corresponding deflection is 2.81 degrees. The upper surface leading edge radius of the optimized airfoil is around 3.11 times that in the baseline airfoil, varying from 0.0086 m to 0.0269 m. The lower skin leading edge radius of optimized airfoil decreases from 0.0087 m to 0.0062 m. Figure 8c compares the optimized mLE shape with the baseline airfoil. Increment of leading edge radius on the upper skin leads to a flatter upper leading edge surface.  
A comparison of the Mach contour for both baseline and optimized airfoil with mLE is displayed in Figure 9; the strong shock wave at around 50% is eliminated and replaced by a smoother shock wave recovery. In the meanwhile, the strongest shock wave appears near the leading edge. Those phenomena in the Mach contour also affect the pressure coefficient distribution in Figure 8b. A stronger sucking force is generated in the leading edge, and the pressure gradient at half chord is alleviated. The pitching moment increases consequently.  
Figure 7: Adjoint surface sensitivity and a comparison of the projected gradient with results obtained from finite difference on RAE 2822 with drag as the objective function. (**a**) Surface sensitivity. (**b**) Gradient. (**c**) A zoom of gradient.
# Enhanced Photocatalytic Performance of Bi2O2 CO3 Loaded Activated Carbon for Toluene Removal in Air
## 2 Materials and Methods
### Characterization  
The X-ray diffraction (XRD) measurements were performed on a diffractometer (D8-Advance, Bruker, Karlsruhe, Germany) with monochromatized Cu K\(\alpha\) (\(\lambda\) = 1.54056 nm) radiation at a scanning speed of 0.15\({}^{\circ}\)/s. The microstructure of the as-obtained samples was characterized using scanning electron microscopy (SEM, JSM-7001F, JEOL, Tokyo, Japan) operated at a voltage of 5 kV. The composition of chemical elements was performed using an X-ray fluorescence (XRF, Thermo Scientific, Niton, UK).  
The N\({}_{2}\) adsorption/desorption measurements were performed on an automated gas sorption analyzer (AutosorbiQ2, Quantachrome, Boynton Beach, FL, USA) at P/Po = 0.99 and the specific surface areas were estimated using the Brunauer-Emmet-Teller (BET) method. Before analysis, all samples were dried at 90 \({}^{\circ}\)C for 1 h and degassed by vacuum at 200 \({}^{\circ}\)C for 24 h.
## 3 Results
### Spatiotemporal Changes of the CLQ  
#### 3.2.1 Spatiotemporal Changes of CLQ in Guangzhou  
The CLQ index in Guangzhou increased steadily from 2010 to 2018 (Figure 4), indicating that the CLQ in Guangzhou continuously improved. From 2010 to 2018, the high-quality cultivated land increased by about 10,000 ha, and the proportion increased by 13.65%. The medium-quality and low-quality cultivated land decreased by 12,000 and 8000 ha, respectively, and their proportions decreased by 8.10% and 5.55%, respectively (Table 4).  
Spatially, from 2010 to 2018, the high-quality cultivated land in the eastern and northern parts of Guangzhou expanded continuously, and the medium- and low-quality cultivated lands decreased significantly, which indicated that the CLQ in these two regions continuously improved. In the western and southern parts of Guangzhou, the high-quality cultivated land initially decreased and then increased. Correspondingly, the medium- and low-quality cultivated land initially increased and then decreased. The CLQ in these two regions improved slightly overall. The CLQ in the central region was low, and there was no significant change during the study period (Figure 5).
## 4 Results
### Examining the Improvement of Accessibility Equality  
The disparities and distributions of the optimized as well as actual healthcare accessibility were further estimated and compared to examine whether and how the equality in accessibility is improved. The healthcare accessibility based on the actual distribution of healthcare facilities was estimated by using the Gaussian-based 2 SFCA method with the same parameters as in the above optimization model. The disparity in accessibility was measured by coefficient of variation (CV), which ranges from 0 to 1, with a larger  
Figure 6: Optimized distribution of secondary healthcare facilities.  
Figure 7: Size adjustments of existing secondary healthcare facilities according to optimization.  
CV representing larger disparity. CV was selected as the measure of disparity because it can make the measures at two levels comparable. In the calculation of CV, the standard deviation of accessibility is divided by the mean.  
As shown in Table 3, the CVs of actual and optimized distributions of healthcare accessibility to tertiary facilities were 0.53 and 0.32, respectively. Similarly, the respective CVs for actual and optimized secondary facilities were 0.58 and 0.36. After optimization, the disparities in healthcare accessibility to tertiary and secondary facilities decreased by 40% and 38%, respectively. In other words, the optimization improved the equality in the spatial accessibility to the tertiary and secondary healthcare facilities by 40% and 38%, respectively.  
The accessibility was calculated first for discrete community locations, and then extrapolated into continuous distribution with the inverse distance weighted spatial interpolation method. As shown in Figure 8, after optimization, the healthcare accessibility to the tertiary facilities ranging from 0.0005 to 0.0010 is relatively evenly distributed in Shenzhen. The distribution of higher accessibility was found to be positively related to population density distribution. Low accessibility could only be observed in few marginal areas.  
As shown in Figure 9, the optimized healthcare accessibility to the secondary facilities in most areas was found to range from 0.0004 to 0.008 in Shenzhen. However, the distributions of higher and lower accessibility were more dispersed than that of the tertiary facilities. This corresponds to the fact that the distribution of higher-level facilities is generally more concentrated. This proves that the optimization resulted in relatively equal healthcare accessibility to both the tertiary and secondary facilities.  
Figure 8: The distribution of optimized accessibility to tertiary healthcare facilities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Facility Level** & **Actual** & **Optimized** & **Improvement** \\ \hline Secondary facilities & 0.58 & 0.36 & 38\% \\ Tertiary facilities & 0.53 & 0.32 & 40\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The coefficients of variation in actual and optimized healthcare accessibility.
## 4 Proposed Neural Networks for GEQ Design  
This section introduces the proposed neural networks used to simplify and speed up the EQ filter gain optimization of SGE3 and SGEB. The neurally-controlled versions of these GEQs are called NSGE3 and NSGEB, respectively. A network structure (_M_-_2M_-_M_-_M_) similar to that utilized in the previous work [20] was used for the NSGE3 and NSGEB designs, shown in Figure 3, where each layer size was determined based on the number of EQ filters \(M\). For the third-octave design NSGE3, the network structure was 31-62-31-31, and for the Bark design NSGEB, it was 24-48-24-24. Both neural networks were trained as before in [20] by using MATLAB's fitnet function with a Bayesian regularization backpropagation algorithm [29].  
The benefit of Bayesian regularization is that, in addition to minimizing the mean squared error, it also simultaneously minimizes the network weights, thus preventing overfitting and ensuring good generalization to new data. With Bayesian regularization, the network training should not stop until the neural network converges. A large value of the Levenberg-Marquardt parameter \(\mu\)[30] is a good indicator of convergence. Thus, the other stopping conditions, such as the number of epochs and the performance goal (MSE error), were set to a high and a low value, respectively, so that the training of the network did not stop before converging. The maximum number of epochs was set to 10,000 and the performance goal to 0.0001. These values were never attained during the training.
# An LSTM-Based Deep Learning Approach for Classifying Malicious Traffic at the Packet Level
## 4 Evaluation Results
### Time Efficiency  
Time efficiency is also an important metric measurement in our experiment. The time efficiency involves two cases: training time and detection time. The training time is defined by the total time to complete the training on the selected dataset. This time depends on the data-preprocessing, the training model (number of layers and dims) and server capacity. Therefore, it would not be fair to draw comparisons within this respect, due to differences in the hardware and software used. Through our evaluation, due to considering at the deeper level (the packet level), the amount of data (after transferring from packet information) used for training in our method is expected to be higher than the flow-based approaches. As a result, the time required for training will be higher. The time for offline training, i.e., ours, could be up to 17 h with USTC-TFC2016 at 200 epochs, in order to obtain the detection performance results as mentioned above. The bigger dataset it is, the more training time it costs. However, our method has advantages of detection time. The detection time in this study is defined as the total time (elapsed time) to perform the classification on a given testing sample, i.e., the testing dataset. Given a testing file 108 MB and our server capacity (mentioned above), our system only takes fewer than 2 s to complete the classification, no matter what type of traffic is. This achievement emboldens our approach for online monitoring since we believe that this speed can satisfy most on-demand applications and in medium networks. For the core networks, we may need more efforts to integrate this system for potential deployment, since the network speed at such nodes can be up to hundreds of gigabits per second. Note that the system at the core networks can be equipped with the much more powerful servers.
## 3 Sustainability
### Alternative Solutions--What Can Be Done?  
#### 3.3.1 Modelling and Simulation  
The use of digital tests and evaluations could be beneficial to both generating a comprehensive database consisting of material responses to assist in providing additional data sets and reducing the amount of waste and water usage. This would require verification and validation prior to wider use to ensure that results are accurate. This is traditionally carried out using Hugoniot equations of state to generate data for hydrocodes, which power the model [52]. In recent years, a shift has been made towards including digital modelling for material qualification [53] and the use of digital twins to replicate equipment, which can allow for cheaper and faster results [54].  
Although this sounds like a good idea in theory, there are multiple factors to consider, including the quality of the model, its fidelity, and the model's outputs aligning with current UK standards, which is paramount in the technical space to provide an acceptable means of compliance. This is further complicated by the energy required to power such models, which may be excessive, and therefore, what you reduce in waste and water usage,you use in electricity. This could be alleviated with the use of green energy alternatives. The generation of electricity from windfarms, solar, or tidal (dependant on location) could be used to reduce the infrastructure's reliance on traditional energy sources.  
Initially, to generate a database of material responses, experimental studies will be required to explore soft tissue analogue responses to impacts of various geometries, sizes, and weights at varying velocities. This is a gigantic task that will take a significant amount of time. Noting the Net Zero target of 2050, further alternatives should be sought to allow time for this solution to mature.
# Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication
## 2 Background and Motivation  
### Encrypted Deduplication  
**Basics:** Deduplication is a data reduction technique that eliminates duplicate data in storage systems. In this paper, we mainly focus on chunk-based deduplication, which generally splits data files into variable-size (an average of 8 KB [9; 10; 11] for deduplication efficiency) chunks (called chunking) based on the boundary points detected by the Rabin algorithm (a rolling hash algorithm [12]). Each chunk is uniquely identified by a hash fingerprint (called fingerprinting) computed using a cryptographic hash function (e.g., SHA-1, SHA-256). Then, the fingerprints are indexed and only one physical copy of the duplicate data chunks is stored (called indexing), so as to save storage space.  
Encrypted deduplication extends traditional data deduplication with encryption to provide both data confidentiality and storage efficiency for outsourced storage systems. Specifically, the client first performs chunking to divide a data file into multiple plaintext chunks. Then, the client encrypts each plaintext chunk with the encryption key and stores all ciphertext chunks in the cloud. Deduplication is then performed in the cloud to eliminate the duplicate ciphertext chunks. For file reconstruction, the client creates a file recipe that lists the fingerprints, sizes, and keys of the ciphertext chunks. The file recipe is encrypted by the master secret key of the client itself and the encrypted file recipe is stored in the cloud.  
Encrypted deduplication uses Message-Locked Encryption (MLE) to derive the symmetric secret key (i.e., the same key is used for both the encryption and decryption) based on the chunk's content. For example, in MLE, a typical instantiation Convergent Encryption (CE) [13] uses the cryptographic hash of a plaintext chunk as the MLE key, such that the identical chunks will have the same encryption key, resulting in identical ciphertext chunks that can be deduplicated after MLE encryption.  
**Server-aided MLE:** However, CE is vulnerable to offline brute-force attacks since its MLE key can be publicly derived [14], and an adversary can infer the input plaintext chunk from a target ciphertext chunk without knowing the MLE key. Specifically, the adversary can enumerate all possible plaintext chunks and check if any resulting ciphertext chunk of those plaintext chunks matches the target ciphertext chunk. Server-aided MLE [1; 2; 4; 5], which deploys a dedicated key server for MLE key generation, enhances the security of encrypted deduplication against offline brute-force attacks. When the client encrypts a plaintext chunk, a fingerprint of the plaintext chunk is first sent to the key server. The key server then generates and returns the corresponding MLE key based on this fingerprint and a global key maintained by the key server. If this global key is secure, the adversary cannot feasibly launch offline brute-force attacks. Otherwise, the security will degrade to ordinary MLE.
## 2 Materials and Methods
### Characterization  
The phase purity (crystallinity) of all the samples was determined by measuring powder X-ray diffraction (pXRD) patterns on a Rigaku instrument with model MiniFlex 600, having Cu-target emitting K\({}_{\alpha}\) radiations (wavelength = 1.54060 A). The scan speed for obtaining diffraction patterns was fixed at 0.01\({}^{\circ}\)/s over a wide range of 2\(\theta\) angle from 10 to 90\({}^{\circ}\). The refinement of the crystal structure was performed using FullProf Suite software (Version 8.00) [13]. The diffuse reflectance spectra were measured in the UV-Visible wavelength range of 200-900 nm, on a Shimadzu spectrophotometer, Kyoto, Japan model 2600i. A Halogen lamp (Model No. 64604) and a deuterium lamp (Model No. L6380) were used for their light emission in the visible and UV regions within spectrophotometer, respectively. An integrating sphere and BaSO\({}_{4}\) were used for baseline correction, with measurements performed in reflectance mode. The micrographs of the as-prepared samples were studied using a field emission scanning electron microscope (FE-SEM) from Hitachi Japan Instruments with model no. SU-8010. The purity and elemental composition of the as-prepared samples (A02 and A10) were verified by energy dispersive X-ray spectroscopic analysis (EDS; Quantax XFlash\({}^{\circ}\)6 130, Bruker Quantax). A NicoletTM Summit LITE spectrometer from Thermo Fisher Scientific (Waltham, MA, USA), was used to measure Fourier transform infrared (FTIR) spectra of different samples in the wavenumber range of 400-4000 cm\({}^{-1}\). The samples were mixed with KBr to prepare pellets of 8 mm diameter. The PL excitation and emission spectra were measured on a Fluoromax Plus-C spectrofluorometer from Horiba Scientific (Kyoto, Japan). The spectra were measured using an R928P photon counting photomultiplier tube with a wavelength detection limit of 185-850 nm. The data were corrected for the lamp input and optics used. Luminescence decay measurements were performed on a time-correlated single photon counting-multichannel scaling system (TCSPC-MCS) with pulsed excitation using a DeltadiodeTM DD-260 model (Serial No. 18591) with an emission maximum at a wavelength of 265 nm. Thermoluminescence (TL) and PersL were measured on Nucleonix TL1009 reader (Telangana, India). The TL measurements were performed at a linear heating rate of 1 K/s, with excitation performed using an 8 W 254 nm UV tube procured from Phillips (Amsterdam, The Netherlands).
## 3 Results and Discussion
### Micro-Differential Scanning Calorimetry  
The heat-induced denaturation of collagens was surveyed by micro-DSC measurements (Figure 3C). The _COLRp_I_ micro-DSC curve displayed two endothermal peaks, one at 23.1 \({}^{\circ}\)C probably relative to the collagen transitional conformation, similar to the observed with the Pacific whiting fish (21.7 \({}^{\circ}\)C) [72], and another peak relative to denaturation of the collagen at 57.6 \({}^{\circ}\)C. _COLRp_II_ and _COLRp_III_ collagens showed only one peak at 45.5 \({}^{\circ}\)C and 75.9 \({}^{\circ}\)C, respectively. According to other studies, endothermal peaks of marine collagen in barramundi (_Lates calarifer_), tilapia (_Oreochromis miloticus_), and sturgeon fish (_Hus huso_) have shown high thermal behaviors with peak temperatures of 109.6 \({}^{\circ}\)C, 113.7 \({}^{\circ}\)C, and 92.03 \({}^{\circ}\)C, respectively, being associated with the cleavage of peptide chains [51; 57; 73]. These high thermal behaviors and positive effects on structural collagen stability are beneficial for the development of marine biomaterials as substitutes for mammalian materials. Conversely, marine collagens are commonly reported to show lower thermal stability compared to mammalian origin [26]. Studies have been performed to improve the thermal stability of marine collagen [74; 75; 76], showing that the properties of collagen are dependent on pH, temperature, and ionic strength that can enable collagen self-assembly into fibrils and consequently enhance the denaturation temperature to a value higher than 40 \({}^{\circ}\)C [77; 78; 79]. Nevertheless, the interpretation of the thermal behavior of collagens is not yet fully understood, with the used experimental conditions playing an important role, namely humidity, besides the ones abovementioned, from which the comparison of transition temperatures obtained in different studies should be made carefully.
## 5 Main Results
### Offline Regression for Unknown Dynamics of UAVs  
As shown in Figure 1, the process of modeling unknown dynamics model is divided into two steps, including flight testing and model training. Flight testing is to collect enough data via designing maneuvers [12], while model training is to establish GP model. It is noting that \(f^{\prime}_{i1},f^{\prime}_{i2}\) in the framework contains the disturbance, and we need to minimize it as much as possible.  
Assume that we obtain the training data set \(\mathcal{D}=\{\mathbf{X},\mathbf{Y}\}\) through flight testing before learning unknown dynamic function \(\mathbf{f}_{i}=(f_{i1},f_{i2})^{\top}\) of \(i\)-th UAV, where \(\mathbf{X}=\left\{(v_{i},\theta_{i})^{(1)},(v_{i},\theta_{i})^{(2)},\cdots,(v_{i}, \theta_{i})^{(M)}\right\}\), \(\mathbf{Y}=\left\{f_{i}^{(1)},f_{i}^{(2)},\cdots,f_{i}^{(M)}\right\}\). Based on the data set, we choose Equation (8) as kernel function. According to Bayesian principles, the optimal hyperparameters to the observed data are obtained by maximizing the likelihood [34]  
\[\mathbf{\psi}^{*} =\arg\max_{\mathbf{\psi}}\log p(\mathbf{Y}\mid\mathbf{X},\mathbf{\psi}),\] \[\log p(\mathbf{Y}\mid\mathbf{X},\mathbf{\psi}) =\frac{1}{2}\Big{(}\mathbf{Y}^{T}\mathbf{X}\mathbf{Y}-\log\det\mathbf{K}-M\log(2 \pi)\Big{)}.\]  
where \(\mathbf{\psi}\) is the set of hyperparameters which need to be optimized.  
Then we can make use of Equation (9) to obtain posterior mean function \(\mu_{ik}(v_{i},\theta_{i})\), \(k=1,2\), which is the prediction value of \(f_{ik}(v_{i},\theta_{i})\).  
Under Assumption 2 and Assumption 3, one can obtain that the estimation error of \(f_{ik}(v_{i},\theta_{i})\) is limited in a certain bound with probability \(1-\delta\)  
\[p\Big{\{}\Delta\tau_{ik}=|f_{ik}(v_{i},\theta_{i})-\mu_{ik}(v_{i},\theta_{i})| \leq\sqrt{\beta_{ik}(\rho,\delta)}\sigma_{ik}(v_{i},\theta_{i})+\gamma_{ik}( \rho),\forall(v_{i},\theta_{i})^{T}\in\mathbb{X}\Big{\}}\geq 1-\delta, \tag{20}\]  
which is the basis of design of robust control law, and provides a premise for the proof of stability. Figure 2 illustrates the flowchart of the GP regression and its application.  
Figure 1: The framework of the process of modeling unknown dynamics model.