esa-sceva/satcom-chunk-collection huggingface dataset:
# Green's Function, Earthquakes, and a Fast Ambient Noise Tomography Methodology
###### Abstract  
Green's function plays an important role in the relationship of a future strong earthquake epicenter to the average earthquake potential score. In the frame of the latter, the fractal dimension of the unified scaling law for earthquakes naturally arises. Here it is also shown to be a cornerstone for the development of a new ambient noise tomography methodology, which is applied for example to the west coast of Central Greece. In particular, we show that a fast and reliable 3D shear velocity model extraction is possible without the need for a large amount of data, great-circle propagation assumptions, or the intermediate step of inverting for group velocity maps. The tomography results are consistent with previous studies conducted in the neighboring region.  
Green's function; earthquakes; surface wave tomography; shear velocity model; inversion 1  
P.K.; [PERSON], [PERSON].  
14 December 202314  
virtual source-receiver pair. It has been shown in various theoretical studies [18] that this cross-correlation can reproduce the surface waves of the Earth's impulse response--or the Green's function--as if triggered by a point source. The experimental results of [PERSON] and [PERSON] [21], which verified the validity of this methodology by extracting the surface wave Green's functions between pairs of seismic stations in Mexico with the cross-correlation of coda waves, are considered a milestone in the field of ambient noise methods. By measuring the dispersion relation of these surface waves between multiple pairs of stations, surface wave tomography is possible [22].  
Below, a new ambient noise tomography (ANT) methodology will be presented. As an example, it will be applied to the area of western Greece depicted in Figure 1, namely the region enclosed by the stations shown at latitudes 38-39\({}^{\circ}\) N and longitudes 20-22\({}^{\circ}\) E, to derive a reliable 3D shear wave speed model.
# Novel Extensions to Enhance Scalability and Reliability of the IEEE 802.15.4-DSME Protocol
###### Abstract  
The Deterministic and Synchronous Multichannel Extension (DSME) of the IEEE 802.15.4 standard was designed to fulfill the requirements of commercial and industrial applications. DSME overcomes the IEEE 802.15.4 limitation on the maximum number of Guaranteed Time Slots (GTS) in a superframe and it also exploits channel diversity to increase the communication reliability. However, DSME suffers from scalability problems, as its multi-superframe structure does not efficiently handle GTS in networks with a high number of nodes and periodic flows. This paper proposes the enhanceD DSME (D-DSME), which consists of two extensions that improve the DSME scalability and reliability exploiting a GTS within the multi-superframe to accommodate multiple flows or multiple retransmissions of the same flow. The paper describes the proposed extensions and the performance results of both OMNeT simulations and experiments with real devices implementing the D-DSME.  
Keywords:IEEE 802.15.4; DSME; Real-time networks; Industrial Wireless Sensor Networks +
Footnote †: journal: Electronics
## 4 Results and Discussions
### Experimental Results  
This section compares our proposed model with several state-of-the-art methods and the 2016 PHM Data Challenge winner. In the experiments, we used the training data provided by the competition and further split them into a training set and a validation set.  
The splitting ratio for the two sets was 9:1. Then, we used the training set to train the model and the validation set to tune the model parameters.  
In our proposed model, the dropout probability \(p\) was 0.1 during the training phase. In addition, we used the Adam optimizer with an initial learning rate of 0.01 and a weight decay of 0.01. Setting the learning rate is a key step in deep learning and this work used a learning rate scheduler to control the learning rate, where the learning rate decreased by a factor of 10 when there was no improvement for five epochs. The batch size was 16 and the number of epochs was 100 for the fusion network in all experiments. The hyperparameters used by our proposed model are listed in Table 2. As for the other comparison methods, the best combination of hyperparameters was obtained using the grid search technique.  
Additionally, Figure 5 shows the loss curves of our proposed model in which the \(y\)-axis is the loss value, whereas the \(x\)-axis denotes the number of epochs. It can be seen that both the training loss and the validation loss decreased as the number of epochs increased.  
The experimental results are listed in Table 3, indicating that our proposed method outperformed the alternatives. We conclude that the reasons why our proposed model outperformed that of [PERSON] et al. (2018) are twofold. First, we used the data science approach to identify outliers and generate more discriminative features using the feature engineering technique. Second, the proposed model allowed practitioners to incorporate the findings from the data exploration step or knowledge into the network. Subsequently, the proposed fusion network used different routes to deal with shallow and deep features. Combining these two features enabled the model to benefit from the characteristics of deep neural networks to learn discriminative features. It is worth mentioning that the work of [PERSON] et al. (2018) and the other three ensemble methods are based on ensemble learning, whereas the proposed method is a single model.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Description** \\ \hline Dropout probability & 0.1 \\ Optimizer & Adam \\ Initial learning rate & 0.01 \\ Batch size & 16 \\ Number of epochs & 100 \\ Activation function & ReLU \\ Loss function & \(\ell_{1}\) Loss \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters of the Proposed Model.  
Figure 5: The loss curves of our proposed model.
## 3 Parameter Analysis of Object Determination
### K Value  
The mean square error values between the data and models differ greatly due to the different amplitudes of models, and mean square error values at the object are much larger than those at the background, which does not mean that the fitting effect at the background is better. Therefore, it is crucial to unify the mean square error values for the object models of different amplitudes.  
The mean square error represents the degree of deviation between the model and the data, whereas the variance represents the degree of fluctuation of the data. Now, the ratio of the two is defined as  
\[K=\frac{MSE}{D(y)}=\frac{\sum(\widetilde{y}_{k}-y_{k})^{2}/n}{\sum(y_{k}-m)^{ 2}/n-1} \tag{10}\]  
Figure 4: Standard deviation thresholdings with the GS method and SDS method. (**a**) A star image with bright stars, faint stars and object streaks. (**b**) \(\sigma\) change rate with GS method. (**c**) \(\sigma\) change with GS method.(**d**) \(\sigma\) change rate with SDS method.(**e**) \(\sigma\) change with SDS method.  
where \(MSE\) is mean square error, \(D(y)\) is the variance, \(m\) is the mean value of a group of data, \(n\) is the number of data points, and \(K\) means the mean square error between the model and the data under unit fluctuation.  
We analyze \(K\) values in Ob 1 and Ob 2. It is found that \(K\) values are small when the correlation coefficients are large, and there is an obvious linear relation. When r is 0.5, both \(K\) values are less than 1, as shown in Figure 5a,b. For a star image containing more objects, the relation between \(K\) values and correlation coefficients is shown in Figure 5c,d. Some \(K\) values decrease with the increase of r, and \(K\) values of another part increase with the increase of r. Others have no regular distribution, but they are all greater than the \(K\) value when r is 0.5. We think r \(\approx\) 0 means that there is no correlation between the two groups of data, which are orthogonal. At this time, it can be approximately regarded as random noises with the equal mean.  
Finally, we can filter out the false alarms by setting \(K\) thresholding. We focuses on the part where r is greater than 0.5 and \(K\) values decrease with the increase of r, and it reflects the significant Gaussian morphology of the image data and has a high correlation with the model. From Figure 5, \(K\) value is 1 when r is 0.5, and the false alarm pixels are randomly distributed in the places where \(K\) value is greater than 1.5. Therefore, we chose k = 1 (of course, any value between 1 and 1.5) for \(K\) thresholding. Other parts are background noises, bad pixels and the edges of objects. In this way, the objects and stars can be well segmented from the star image.
## 4 Experimental Results
### Evaluation Criteria (mAP)  
The mean average precision (mAP) metric is utilized to estimate the performance of the introduced ReSTiNet and the baseline architectures. The mAP scores are reported for both \"MS COCO\" and \"Pascal VOC\".  
Average Precision (AP): The recall/precision curve is used to assess the output performance for a specific class and task. Precision is the ratio between the relevant and retrieved examples explained in Equation (13).  
\[\textit{precision}=\frac{|\{\textit{relevant instances}\}\cap\{\textit{ retrieved instances}\}|}{|\{\textit{retrieed instances}\}|} \tag{13}\]  
The rate of recall is described as the ratio of the total number of relevant examples to the total positive instances. The average precision is utilized for evaluating precision over multiple equidistant recall levels:  
\[AP=\sum_{k=0}^{k=n-1}[\textit{Recalls}(k)-\textit{Recalls}(k+1)]*\textit{ Precisions}(k) \tag{14}\]  
where \(n\) defines the number of the threshold.  
The mAP (\"mean average precision\") is employed to calculate the C class's average precision:  
\[mAP=\frac{1}{C}\sum_{i\in\{0,1,2,\ldots,C\}}AP(c_{i}) \tag{15}\]  
where \(AP(c_{i})\) defines the average precision for the class of \(c_{i}\).
# How Can We Achieve Query Keyword Frequency Analysis in Privacy-Preserving Situations?
## 6 Performance Evaluation
### Theoretical Analysis  
We show some feature comparisons between KFA-PEKS and the existing schemes in Table 2. In past research, SE schemes have been classified into four types as follows. The single-writer/single-reader (SW/SR) setting [16] is often SSE, wherein the writer and the reader act as one actor, i.e., only the writer is allowed to initiate the query. The multi-writer/single-reader (MW/SR) setting [4; 24; 36] is a scheme in which multiple writers can generate searchable ciphertexts for querying by a specific reader, as opposed to the single-writer/multi-reader (SW/MR) setting [18]. The multi-writer/multi-reader (MW/MR) setting [22] allows multiple users to encrypt and upload data while being able to search all users' stored encrypted data.  
In the single-keyword schemes [4; 24; 36], the reader needs to generate multiple different query trapdoors for multiple different query keywords, i.e., the number of trapdoors is linearly related to the number of keywords. Similarly, the number of searchable ciphertexts is influenced by the number of keywords in the relevant document. The size of the trapdoor and searchable ciphertext are significant factors affecting the efficiency of keyword-searchable encryption schemes, compared to single-keyword schemes; this is related to the \(|W_{id}|\) in [16]. In [18], the writer establishes the corresponding indexes for the documents to be uploaded (and the keywords contained in them) in advance, so the size of ciphertext is linearly related to \(|W_{id}|\). Similarly, the search trapdoors generated by the reader when making a query are all possible indexing relationships between the query keywords and associated documents, so the number of trapdoors is linearly related to both \(|DS|\) and \(|SQ|\). Both the trapdoors and ciphertexts of our study and of [22] are homomorphic encryptions of a decimal integer of constant size.  
Figure 7: The keyword recovery times of ours and [24].  
In PEKS, the searchable ciphertext is processed by the writer using the public key of the target reader for the query keyword; therefore, the attacker can perform KGA on the query trapdoor by forging the ciphertext. Ref. [16] is an SSE scheme in which the generation of the ciphertext and trapdoor require the user's key, so the attacker cannot generate the test ciphertext used to perform KGA. The reader in [36] shares a secret value with the writer, and the attacker cannot generate a test ciphertext to execute the KGA since they do not know the secret value. Ref. [18] is an SW/MR setting, which means that only the writer can generate the ciphertext. Our scheme and that of [22] use the partial private keys of the MS and KFS as the input for the _Test_ algorithm, which means that the attacker is unable to execute the _Test_ algorithm freely. Furthermore, among the above schemes, only ours and that of [24] can complete a keyword frequency analysis of the query keywords. In conclusion, the performance of our solution exceeds that of related works.
# Exploring Structural Changes in Ge-Te Amorphous Films Through Small-Angle Neutron Scattering
## 4 Discussion
### High-Q Region: Diffraction Region
#### 4.2.1 Peak Positions  
The first peak (FSDP), located at 1 A\({}^{-1}\), corresponds to IRO in the glass structure. Its position in reciprocal space relates to a longer characteristic distance in real space (\(2\pi/Q_{\text{FSDP}}\approx 6.28\) A). The overall decrease of about 15% in this peak's position as the germanium content increases from \(x\approx 0.2\) to \(x\approx 0.30\) (red symbols in Figure 4) indicates an expansion or modification of the intermediate atomic arrangement. This expansion is much more noticeable when \(x\) approaches 0.50. The fact that the intensity of this peak does not change significantly implies that, while the connectivity is being altered, the basic intermediate structural bonds remain relatively intact in terms of their overall number or density. From the perspective of dividing the composition range into three zones, we can say that the position of the FSDP increases in the first zone, whereas it decreases in the third zone.  
The second peak (PP), at approximately 2 A\({}^{-1}\), corresponds to SRO. This peak relates to a shorter characteristic distance in real space compared to the first peak (\(2\pi/Q_{\text{PP}}\approx 3.14\) A). The slight increase in the position of this peak, albeit 6 times less than the decrease in the FSDP, may suggest a gradual adjustment in the short-range structural arrangement as germanium is added. This is consistent with observations from simulations of partial correlations using the RMC method [24]. These simulations show that Ge-Ge distances are 5% shorter than Ge-Te distances, which explains the shift in the PP towards higher \(Q\) values. Again, from the perspective of compositional zoning, we observe that in the first zone, the position of this peak remains almost unchanged, consistent with RMC simulations, which indicate that only Ge-Te bonds are present for these compositions in the PP. In contrast, in zone 3, the increasing presence of Ge-Ge bonds causes a shift in the reciprocal space position towards higher values.  
Figure 4: Positions of the FSDP (left scale) and PP (right scale) as a function of the Ge content \(x\). Dashed lines are smoothed splines that only serve as a guide for the eye. The red-, white-, and blue-shaded areas correspond to the different zones observed as a function of the Ge content.  
To finish with the peak positions, it is worth noticing the opposite behaviour of the two peaks: when the FSDP moves to lower positions, the PP moves to higher positions, and vice versa.
# Improving the Solving of Optimization Problems:
## 5 Quantum Circuit Model-Based Solvers
### Qaoa
Regarding the number of time steps \(p\), a larger value should better approximate the adiabatic evolution. However, this also increases the quantum circuit depth, which can negatively impact the accuracy and reliability of NISQ hardware [59]. QAOA can directly solve PUBO problems, as a monomial of any degree can be encoded in a quantum state. The polynomial Hamiltonian can be reformulated as a quantum Hamiltonian, as shown in Equation (11). Calling \(L\) the set of qubits involved, a  
Figure 1: Example of QAOA quantum circuit to optimize the Ising Hamiltonian \(-2s_{1}s_{2}+1s_{0}\). generic polynomial term is composed of the tensor product of \(|L|\)\(\sigma^{z}\) matrices. Therefore, two different cases can still be distinguished:  
* If the number of qubits in the state \(|1\rangle\) is even, the operator is \(e^{i\gamma_{m}L_{L}}\);
* If the number of qubits in the state \(|1\rangle\) is odd, the operator is \(e^{-i\gamma_{m}L_{L}}\). This transformation is then a parity check on the qubits in \(L\), followed by a \(R_{Z}(-2\gamma_{m}l_{L})\) gate, ensuring that the phase shift is negative only when the parity is even. Parity can be determined by applying a CNOT gate for each qubit \(l\) in \(L\) except for the last one, using \(l\) as the control qubit and the last as the target (an example is reported in Figure 2). For formulations that naturally belong to the PUBO category, results in [60] evidence that using a PUBO function not only avoids the need for auxiliary variables and improves the cost function profile, but also improves the accuracy of results and reduces the optimization time of angle parameters in QAOA, when using the same depth and hyperparameters for the classical optimization algorithm. However, this comes at the expense of increasing the depth of the quantum circuit, which grows linearly compared to the QUBO case. Recently, improvements to the algorithm have been proposed in the literature. Among them, the so-called Warm-Start QAOA [61] is worth mentioning, which exploits as the initialization state the solution of the continuous relaxation of the QUBO problem--which can be easily obtained since it is a convex problem--instead of the uniform superposition. Moreover, the mixed Hamiltonian is modified to be coherent with the initialization state.
## 3 Results and Discussion
### Surface Functional Characteristics and Elemental Analysis  
The surface functional properties have been investigated using Fourier transform infrared spectroscopy (FTIR). Figure 2 illustrates that the absorption maxima at 3190 cm\({}^{-1}\) and 2990 cm\({}^{-1}\) correspond to the stretching vibrations of O-H and N-H bonds, respectively. The absorption peak at 2150 cm\({}^{-1}\) is ascribed to the stretching vibration of the triple bond v(C\(\equiv\)C). Additionally, the absorption peaks observed at 1670 cm\({}^{-1}\), 1560 cm\({}^{-1}\), and 1380 cm\({}^{-1}\) correspond to the stretching vibrations of carbonyl (C=O), C=C, and C-N bonds, respectively. The absorption peak at 1060 cm\({}^{-1}\) is ascribed to the symmetrical stretching vibration of the ether bond (R-O-C). The shoulder peak in the 650 cm\({}^{-1}\) area is attributed to the out-of-plane bending vibration of unsaturated 6(C-H) in the benzene ring and the out-of-plane rocking vibration absorption peak of NH\({}_{2}\) groups in secondary amides. By integrating these data with the stretching vibration attributes of N-H, C=O, and C-N bonds, it can be deduced that N-CQDs molecules exhibit the amide structure (-CO-NH), signifying successful nitrogen doping in the N-CQDs [29].  
The elemental composition of N-CQDs was analyzed using X-ray photoelectron spectroscopy (XPS). Figure 3a illustrates three distinct peaks at 285 eV, 401 eV, and 533 eV, corresponding to C1s, N1s, and O1s, respectively [19]. A minimal quantity of nitrogen elements persists on the surface of N-CQDs, resulting from the reaction between NH\({}_{3}\cdot\)H\({}_{2}\)O and H\({}_{2}\)O\({}_{2}\) during the degradation of bulk carbon materials. Additionally, Figure 3b displays the peak-fitting results of the high-resolution C1s spectrum, indicating five discrete peaks associated with sp2-hybridized carbon (C=C/C-C) [30], sp3-hybridized carbon (C-O/C-N) [27], imine carbon (C=N) [31], carbonyl carbon (C=O) [23], and carboxyl carbon (-COOH) [32]. The N 1s spectrum illustrated in Figure 3c can be deconvoluted into three different peaks, representing amine nitrogen, amino nitrogen, and pyrrole nitrogen [23; 30; 33]. In the O1s spectrum (Figure 3d), three peaks are discernible: C=O, C-O [33], and N=O [23], with the N=O peak displaying the greatest strength.  
Figure 1: **(a)** TEM image of N-CQDs; **(b)** particle size distribution; **(c)** high-resolution transmission electron microscopy (HRTEM) image of N-CQDs; **(d)** XRD pattern of N-CQDs.  
The findings from FTIR and XPS demonstrate the successful execution of amination and carbonization processes for the creation of nitrogen-doped and extremely hydrophilic N-CQDs.
# A Fluid-Structure Coupling Analysis of a Far-Field Flat Mirror for AliCPT-1 Telescope Calibration
## 3 Fluid Field Calculation
### Boundary Conditions
Turbulent intensity measures the strength of turbulent fluctuations, while the turbulent viscosity ratio is defined as the ratio of turbulent viscosity to fluid viscosity. Normal turbulent intensities typically range from 1% to 5%. For external flows, a turbulent intensity of 1% or lower and a turbulent viscosity ratio of 1-10 are generally recommended. In consideration of the flow characteristics of this study, a turbulent intensity of 5% and a turbulent viscosity ratio of 10 are applied. The flow surface adopts a pressure outlet boundary condition with a pressure of 0 Pa. The reflector surface adopts a no-slip wall boundary condition, while the top and both side surfaces adopt free-slip wall boundary conditions. The coupled algorithm was utilized for pressure-velocity coupling, treating pressure and velocity as two unknowns and imposing simultaneous constraints on their relationship to derive analytical solutions. Concurrently, a coupled analysis was conducted on fluid parameters such as velocity, pressure, density, and temperature to depict fluid behavior accurately. A mixed initialization approach was adopted to solve the Laplace equation, using distinct interpolation methods for different boundary conditions to initialize the pressure and velocity fields of the computational domain.