esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### DEA Malmquist Model  
Besides calculating technical efficiency at a given time, calculating efficiency over time is equally important. The comparison of efficiency levels between different time periods helps researchers to have a clearer view of the changes in efficiency over time, from which evaluating measuring how these changes affect efficiency, as well as partly predicting the fluctuations of efficiency in the future.  
In the DEA method, the estimation of technical efficiency is done based on a defined frontier, and therefore, it is complex to compare the efficiency between two different time periods based on two different frontiers. However, the problem becomes simpler with the help of distance functions. [PERSON] et al. [26] provide a model to determine the change in total factor productivity (TFP) over time, in which any decision-making unit (DMU) will be studied at two different time periods t and t + 1 (corresponding to two different frontiers at time period t and t + 1) and then compare the change in the combined total factor productivity of that DMU [22].  
According to [PERSON] [26], the TFP that was obtained by DEA, called Malmquist Productivity Index (MPI). MPI index of each observation corresponds to the frontier at each period of a certain group of K defined at the technical level in the period t, as follows.  
\[\begin{array}{l}MPI=\frac{\delta^{t+1}\left(\left(x_{0},y_{0}\right)^{t+1} \right)}{\delta^{t}\left(\left(x_{0},y_{0}\right)^{t}\right)}\times\left[ \frac{\delta^{t}\left(\left(x_{0},y_{0}\right)^{t}\right)}{\delta^{t+1}\left( \left(x_{0},y_{0}\right)^{t}\right)}\times\frac{\delta^{t}\left(\left(x_{0},y_ {0}\right)^{t+1}\right)}{\delta^{t+1}\left(\left(x_{0},y_{0}\right)^{t}\right) }\right]^{1/2}\\ =\left[\frac{\delta^{t}\left(\left(x_{0},y_{0}\right)^{t+1}\right)}{\delta^{t }\left(\left(x_{0},y_{0}\right)^{t}\right)}\times\frac{\delta^{t+1}\left(\left( x_{0},y_{0}\right)^{t+1}\right)}{\delta^{t+1}\left(\left(x_{0},y_{0}\right)^{t} \right)}\right]^{1/2}\end{array} \tag{16}\]  
According to [PERSON] [26] the change of TFP can be obtained by the efficiency change and technical change and measured as below formula:  
\[\text{TFPCH}=\text{EFCH}\,^{*}\,\text{TECHCH} \tag{17}\]  
TFPCH: total factor productivity change (MPI)  
EFCH: efficiency change (catch-up)  
TECHCH: technical change (frontier-shift)  
In which:  
\[\text{EFCH}\;=\;\frac{\delta^{t+1}\left(\left(x_{0},y_{0}\right)^{t+1} \right)}{\delta^{t}\left(\left(x_{0},y_{0}\right)^{t}\right)} \tag{18}\]  
And  
\[\text{TECHCH}\;=\;\left[\frac{\delta^{t}\left(\left(x_{0},y_{0}\right)^{t} \right)}{\delta^{t+1}\left(\left(x_{0},y_{0}\right)^{t}\right)}\times\frac{ \delta^{t}\left(\left(x_{0},y_{0}\right)^{t+1}\right)}{\delta^{t+1}\left( \left(x_{0},y_{0}\right)^{t}\right)}\right] \tag{19}\]  
Thus, when TFPCH \(>\) 1, the total factor productivity of considering DMU has increased at time t + 1 compared to time t. TFPCH \(<\)1 indicates the decrease in total factor productivity at time t+1 as compared to time t. TFPCH \(=\) 1 means the total factor productivity remains unchanged.
# Removing Land Subsidence Impact from GPS Horizontal Motion in Tianjin, China
## 2 Materials and Models
### Models
#### 2.2.2 Datasets  
The training and testing of each GRU model require the generation of a corresponding dataset. We used the vertical data as the training data and the target horizontal component motion as the corresponding data features.  
First, data preprocessing was performed. The daily time series of the target horizontal component and the vertical component of the same station from 2014 to 2023 were converted into formats, aligned, and normalized. Then, the dataset samples were generated by using the sliding window, with the window length of each sample set to 2 years (730 days); the vertical time series was used as the sample data; and the annual linear rate of the time series within the window length of the horizontal component was used as the sample label. Multiple labeled data samples were generated on the preprocessed time series with a sliding window of days as a step, and the total number of samples in each dataset was around 2900. Finally, dataset partitioning was performed, where the generated dataset was divided into a training set (80%) and a test set (20%) according to a certain percentage. The training set was used for the training of the model, and the test set was used to evaluate the performance of the model.
## 3 Calibration of the Measurement System
### Alignment of the High Aspect Ratio Aspheric Workpiece  
For the alignment of the high aspect ratio aspheric workpiece, there are tilt misalignment errors with a roll angle \(\theta_{\delta}\) about the \(X\)-axis and a pitch angle \(\theta_{\alpha}\) about the \(Y\)-axis. The lateral gauge mounted on the \(Z\)-slide is applied to measure the sidewall of the inner profile to calibrate the misalignment error of the workpiece, as shown in Figure 8. The basic concept is to identify the point with the largest distance from the central point of the workpiece at two different heights of \(z_{1}\) and \(z_{2}\), respectively. As shown in Figure 8a, the lateral gauge is moved from \(X_{0}\) along the negative \(X\)-axis to identify the largest distance point from the central point of the workpiece when the output of the lateral gauge reaches the maximum. To evaluate the pitch angle \(\theta_{\alpha}\) of the workpiece, the first step is to mark the point \(P_{1}{}^{\prime}(x_{1}{}^{\prime},y_{1}{}^{\prime},z_{1}{}^{\prime})\) with the largest distance from the central point of the workpiece at the height of \(z_{1}{}^{\prime}\) while moving the lateral gauge along \(X\)-axis. Then the point \(P_{2}{}^{\prime}(x_{2}{}^{\prime},y_{2}{}^{\prime},z_{2}{}^{\prime})\) with the largest distance from the central point of the workpiece at the height of \(z_{2}{}^{\prime}\) can also be identified based on a similar procedure. According to the geometric relationship shown in Figure 8b, the pitch angle \(\theta_{\alpha}\) of the high aspect ratio aspheric workpiece can be evaluated through the following equation:  
\[\theta_{\alpha}=\arctan\frac{x_{1}{}^{\prime}-x_{2}{}^{\prime}}{z_{2}{}^{ \prime}-z_{1}{}^{\prime}} \tag{32}\]  
In the same way, the roll angle \(\theta_{\beta}\) of the high aspect ratio aspheric workpiece can be obtained by:  
\[\theta_{\beta}=\arctan\frac{y_{3}{}^{\prime}-y_{4}{}^{\prime}}{z_{4}{}^{ \prime}-z_{3}{}^{\prime}} \tag{33}\]  
where \(P_{3}{}^{\prime}(x_{3}{}^{\prime},y_{3}{}^{\prime},z_{3}{}^{\prime})\) is the largest distance point from the center of the workpiece at the height of \(z_{3}{}^{\prime}\), and \(P_{4}{}^{\prime}(x_{4}{}^{\prime},y_{4}{}^{
## 1 Introduction  
Over the past ten years, continuous glucose monitors (CGMs) have become readily available for use by type 1 diabetic patients, and insurance companies are beginning to accept them as a legitimate part of managing diabetes. As the use of CGMs increases, both physicians and patients are faced with the question of how to make sense of the tidal wave of data generated by these devices. Unlike the measuring of blood sugar four to six times a day through a finger-prick or the determination four times a year of \({\rm{A_{1}}_{C}}\) (a measure of the average blood glucose level over the past 3-4 months), current CGMs generate a reading every five minutes, leading to nearly 9000 readings and approximately thirty graphs of data per month, if the patient wears the monitor all the time. What can be done to mine this data and learn new facts about a patient's blood glucose management?  
This paper presents an analysis of this data via an experimental, unsupervised learning algorithm that uses data generated by a wavelet analysis of a patient's daily CGM data. The results indicate that the shape of a graph corresponding to a day's worth of CGM data may be an important attribute in diabetes care. The shape is intimately connected to the variability of blood glucose readings, which past research suggests is a fundamental part of diabetes care [1]. Using _ad hoc_ clustering methods we suggest one possible interpretation of the results of the wavelet-based algorithm, a single measurement which we call the PLA index. The PLA index quantifies the shape by capturing the number of trend changes in blood glucose in a given day and is subsequently described. Although several simpler measures of variability exist (such as computing the variance of first differences), we have chosen a wavelet-based analysis due to the previous success in shape-related applications (cf. [2] and [3]).
# Intelligent Optimization of Process Parameters in Limit Gauge Hot Strip Rolling
## 3 Neural Network Modeling
### Activation Function Selection  
In the neural network, because the full connection layer only makes affine transformation on the data, and the superposition of multiple affine transformations is still an affine transformation, the non-linear transformation of activation function is introduced to improve the approximation ability of the network model [26]. The selection of activation function requires the following properties:  
1. Continuous and differentiable (non differentiable on a few points is allowed). The derivable activation function can directly use the numerical optimization method to learn the network parameters;
2. The activation function and its derivatives should be as simple as possible, which is conducive to improving the efficiency of network computing;
3. The value range of the derivative of the activation function should be in a suitable range, which should not be too large or too small, otherwise it will affect the efficiency and stability of training.  
In this study, rectified linear unit (ReLU) function is used as the activation function (see Figure 3). ReLU is the hard limit when \(x<0\), and the first derivative is \(1\) when \(x>0\). Therefore, the function can maintain the gradient without attenuation, alleviate the gradient disappearance problem, accelerate the convergence speed, and have the ability of network sparse expression.
# The Rise of Quantum Information and Communication Technologies
## 4 Next Steps  
### Hybrid Security (QKD and PQC)  
As mentioned in previous sections, QKD is a physical layer method that allows an unconditional secure distribution of random keys between remote users. For instance,  
Figure 7: QKDN topology discovery procedure (source: ITU-T Y.3804).  
QKD is a method that exploits the physical properties of photons to create and distribute secret keys that can then be used by existing ciphers. In the area of cybersecurity, on the other hand, the goal of PQC is to develop cryptographic systems that are secure against both quantum and classical computers and can interoperate with existing communication protocols and networks. In other words, unlike QKD, PQC relies on algorithms that are too complex for quantum computers to crack. PQC is still in active development, and it is currently undergoing standardization by NIST. Also, the model of the Post Quantum Telco Network is still under definition and development, through several initiatives [17].  
There are still limitations in both approaches, and overcoming the current limitations requires a long-term investment in developing QCTs. Nevertheless, it is likely that a combination of QKD and PQC for a so-called hybrid security scenario might be very interesting, offering the most secure approaches to data encryption.  
In general, a hybrid mechanism is based on a combination of a recognized pre-quantum public key algorithm and an additional algorithm that is post-quantum-secure. This makes the hybrid mechanism secure thanks to the resistance of the first algorithm against classical attackers and that of the second algorithm against quantum attackers [17]. Specifically, examples of issues under consideration for QKD-PQC hybrid security include understanding which generic standards on hybrid schemes are compatible with QKD-PQC technologies; what kind of QKD-PQC integration can also take place on the authentication level; technical challenges vs. business opportunities. These are for further study.
## 3 Results and Discussion
### Breakthrough Experiments under Dry Conditions
As expected, the regeneration efficiency for the fifth cycle increased at 50 \({}^{\circ}\)C compared with that at 25 \({}^{\circ}\)C (81.6% and 69.6%, respectively). However, the pressure applied during desorption (24.9 \(\pm\) 0.7 kPa) was still too low to ensure the complete regeneration of the AC_WS_500_P adsorbent, which showed a considerably lower mesopore volume compared to that of the AC_VS_600 material. \begin{table}
\begin{tabular}{c c c} \hline  & \multicolumn{2}{c}{**Activated Biochar**} \\ \cline{2-3}  & \multicolumn{1}{c}{_AC\_VS\_600_} & _AC\_WS\_500\_P_ \\ \hline Mass of adsorbent (g) & 15.1 & 27.8 \\ Absolute pressure in adsorption steps (kPa) & 113.6 \(\pm\) 0.6 & 113.3 \(\pm\) 1.4 \\ Absolute pressure in desorption steps (kPa) & 23.5 \(\pm\) 0.5 & 24.9 \(\pm\) 0.7 \\ cycle 1 & 0.543 & 0.624 \\ cycle 2 & 0.570 & 0.583 \\ \(q_{\text{CO2}}\) (mmol g\({}^{-1}\)) & cycle 3 & 0.565 & 0.555 \\ cycle 4 & 0.573 & 0.550 \\ cycle 5 & 0.571 & 0.511 \\ cycle 1 & 75.3 & 68.0 \\ Percentage of total CO\({}_{2}\) & cycle 2 & 73.1 & 71.5 \\ uptake at breakthrough time & cycle 3 & 73.2 & 71.4 \\ cycle 4 & 73.4 & 69.0 \\ cycle 5 & 76.5 & 69.7 \\ cycle 1 & – & – \\ cycle 2 & 105.0 & 93.3 \\ Regeneration efficiency (\%) & cycle 3 & 104.2 & 88.9 \\ cycle 4 & 105.6 & 88.2 \\ cycle 5 & 105.3 & 81.8 \\ cycle 1 & 40.8 & 38.2 \\ cycle 2 & 39.1 & 43.1 \\ cycle 3 & 37.4 & 43.2 \\ cycle 4 & 34.6 & 35.0 \\ cycle 5 & 38.7 & 38.3 \\ \hline \end{tabular}
\end{table} 
Table 6: Summary of results from the breakthrough experiments (dry conditions) conducted at 50 \({}^{\circ}\)C.
## 4 Experiments and Discussion
### Evaluation in Familiar Environments  
To evaluate the performance of the approaches in familiar environments, the trained models are tested 100 times across env1 to env4, and the results are presented in Table 2 and Figure 7. We can find that our FGRL and FL-TD3 achieve the success rate close to 100%, while TD3 achieves about 95%. This indicates that the combination of federated learning can indeed improve the success rate of obstacle avoidance in navigation tasks. Moreover,  
Figure 6: Average reward during the training episodes.  
Figure 7b shows that TD3 has the largest average successful steps, indicating that during successful runs, it takes longer steps to reach the target location in comparison with other approaches. The average successful steps of our FGRL are slightly smaller than those of FL-TD3. Table 2 shows that our FGRL can decrease the average successful steps by 5.7% compared to TD3, and by 1.7% compared to FL-TD3 across the four environments.
# Goal Oriented Time Adaptivity Using Local Error Estimates
## 3 Time Adaptivity Based on Local Error Estimates
### Goal Oriented Error Estimates
#### 4.1.2 Quadrature Error Estimate  
We now consider the quadrature error  
\[|\mathcal{J}(\mathbf{u})-\mathcal{J}_{h}(\mathbf{u})|\leq\sum_{n=0}^{N-1}\left|\int_{ t_{n}}^{t_{n+1}}j(t,\mathbf{u}(t))\mathrm{d}t-\Delta t_{n}\sum_{k=0}^{s}\sigma_{k}j \left(t_{n}^{(k)},\mathbf{u}(t_{n}^{(k)})\right)\right|.\]  
For a time-interval \([t_{n},t_{n+1}]\) we use the notation  
\[\mathcal{J}_{n}(\mathbf{u}):=\int_{t_{n}}^{t_{n+1}}j(t,\mathbf{u}(t))\mathrm{d}t\quad \text{and}\quad\mathcal{J}^{r}_{h,\,n}(\mathbf{u}):=\Delta t_{n}\sum_{k=0}^{s} \sigma_{k}j\left(t_{n}^{(k)},\mathbf{u}(t_{n}^{(k)})\right),\]for an \(r\)-th order quadrature scheme. We denote quadrature for the numerical solution by \(\hat{\mathcal{J}}^{r}_{h,n}(\mathbf{u}_{h})\) and assume there is a discrete solution available in all relevant points or given by interpolation. Given sufficient smoothness of \(j\) and \(\mathbf{u}\), the quadrature error in one timestep is  
\[e^{\mathcal{J}_{\mathcal{Q}}}_{n}:=|\hat{\mathcal{J}}^{r}_{h,n}(\mathbf{u})- \mathcal{J}_{n}(\mathbf{u})|=|\Delta t^{r+1}_{n}\varphi(t_{n},\mathbf{u}(t_{n}))+ \mathcal{O}(\Delta t^{r+2}_{n})|.\]  
Analogous to time-integration, \(\varphi(t,\mathbf{u})\) is the _principal error function_. Following the previous approach from time-integration, we employ an additional scheme of order \(\hat{r}<r\) to estimate  
\[|\hat{\mathcal{J}}^{\hat{r}}_{h,n}(\mathbf{u})-\mathcal{J}_{n}(\mathbf{u})|\approx| \hat{\mathcal{J}}^{\hat{r}}_{h,n}(\mathbf{u})-\hat{\mathcal{J}}^{r}_{h,n}(\mathbf{u}) |=|\Delta t^{\hat{r}+1}_{n}\varphi_{-}(t_{n},\mathbf{u}(t_{n}))+\mathcal{O}(\Delta t ^{\hat{r}+2}_{n})|. \tag{22}\]  
For a practical error estimate we, however, need to use the numerical solution \(\mathbf{u}_{h}\). By expanding \(j(t^{(k)}_{n},(\mathbf{u}_{h})^{(k)}_{n})\) in \((t^{(k)}_{n},\mathbf{u}(t^{(k)}_{n}))\), we get  
\[\hat{\ell}^{\mathcal{J}_{\mathcal{Q}}}_{n}:=|\hat{\mathcal{J}}^{\hat{r}}_{h,n }(\mathbf{u}_{h})-\hat{\mathcal{J}}^{r}_{h,n}(\mathbf{u})|=|\hat{\mathcal{J}}^{\hat{r }}_{h,n}(\mathbf{u})-\hat{\mathcal{J}}^{r}_{h,n}(\mathbf{u})|+\mathcal{O}(\Delta t_{ n}\mathbf{e}_{n}), \tag{23}\]  
assuming \(\mathcal{O}(\mathbf{e}^{(k)}_{n})=\mathcal{O}(\mathbf{e}_{n})\), where \(\mathbf{e}^{(k)}_{n}:=(\mathbf{u}_{h})^{(k)}_{n}-\mathbf{u}(t^{(k)}_{n})\), i.e., sufficiently high order solutions in all quadrature evaluation points. We then have  
\[\hat{\ell}^{\mathcal{J}_{\mathcal{Q}}}_{n}\stackrel{{\eqref{eq: q_n}}}{{=}}|\Delta t^{\hat{r}+1}_{n}\varphi_{-}(t_{n},\mathbf{u}(t_{n}))|+ \mathcal{O}(\Delta t^{\hat{r}+2}_{n})+\mathcal{O}(\Delta t_{n}\mathbf{e}_{n}). \tag{24}\]
## 3 Proposed Methodology
### Deployment of Gateway Nodes  
The Surface Gateways (SG) and Cluster head make up the underwater network (CH). SGs are static nodes fastened to surface-based buoys. They have electromagnetic and acoustic interfaces, respectively. Through an electromagnetic interface, SGs link the underwater network to the Internet. SG uses an acoustic interface to transmit and receive packets to the underwater network. One or more CHs may be connected to each SG. Torelay packets from SGs to the active AUVs at the ocean floor and vice versa, CHs are positioned underwater at various depths is shown in Figure 2.  
Due to the characteristics of an underwater communication network, the energy consumption scheme of UWSNs sets it apart from the energy-consuming strategy of WSNs. Equation (1) illustrates the numerical solution.  
\[E(\text{distance},\ fc)=\text{EnergyTh}\ (\text{distance},\ fa) \tag{1}\]  
I here stands for a node's frequency. It is equivalent to Equations (2) and (3)  
\[fa=10^{fa(fc)/10} \tag{2}\]  
\[fa(fc)=\left[0.11*\left((fc)^{2}*\left(\frac{1}{(1+fc)}\right)^{2}\right)+0.22 \left((fc)^{2}*\left(\frac{1}{(1+fc)}\right)^{2}\right)\cdots+n\left((fc)^{n}* \left(\frac{1}{(1+fc)}\right)^{n}\right)\right. \tag{3}\]  
Since the exposure zone of every sensing device is boundary-based, Equation (4) may be used to describe private network radial distance \(NR\), node density \(ND\), sector width RingW, hop limit MaxH, and the total number of participants \(Np\).  
\[Np\ \ =\sum_{i=0}^{n}NR\ *\ ND\ *\ MaxH\ *\ \text{Ring}\ *\ W \tag{4}\]  
Figure 2: Node deployment in the network.  
From inside to outside, the UWSN's connectivity is separated into various little ring sections or Ar1 to Arn. RingW/OT, where RingW is the network radius and OT is the ideal connection path length threshold for sensing devices, can be used to identify the maximum number of ring regions. Dlmax is a representation of the route's maximum delay. The frequency of \(Yij\) is equal to 5 whenever nodes \(i\) and \(j\) are connected. If not, \(Yij\)'s total value is 0. We view the search for multi-hop routes as a multi-objective optimization problem, with the sole objective of locating the best route at the most reasonable price. Following Equations (5)-(7) shows how an objective formula (Fobj) is obtained. The proposed system architecture is shown in Figure 3.  
\[\text{Min Fobj}=\sum_{i=1}^{n}\sum_{j=1}^{n}Y_{ij\leftrightarrow Dij} \tag{5}\]  
\[Dij\ =\ Dtij\ *\text{MinPower}\ *\ Kij\ +\ Drj \tag{6}\]  
\[\sum_{i=1}^{n}\sum_{j=1}^{n}Dij\ \text{Yij}<\text{Dlmax} \tag{7}\]
## 3 Numerical Experiment
### Wedge-Shaped Fault Model  
As shown in Figure 7, a wedge-shaped fault model with a wide top and a narrow bottom is set up, with a radius of 35 m and a length of 140 m. The front interface of the fault is vertical, 35 m away from the tunnel face, the rear interface is inclined with a dip angle of 70\({}^{\circ}\), the widest part of the upper part at the fault is 30 m, and the narrowest part at the lower part is 10 m. The fault is filled with soft soil. The rest of the model settings are the same as those used in the karst.  
From Figure 8, it can be seen that the wedge-shaped fault model also produces reflected waves twice. \(PP1\) reaches the receivers at 32 ms and \(SS1\) is received at 48 ms. The first reflected wave is completely vertical, whereas the second reflected waves are inclined, and there is a certain time difference when they reach the upper and lower sidewall. When the dominant frequency of the source remains constant, because the \(P\)-wave velocity is larger, the wavelength of the reflected \(P\)-wave is greater than the that of reflected \(S\)-wave, so the resolution of the \(S\)-wave is higher, and it is more suitable for detecting small-scale structures.  
For simplicity, only the seismic profile from the upper and lower sidewalls in \(v_{r}\) component will be illustrated. As shown in Figure 9, comparing the \(P\)-waveform and \(S\)-waveform in the seismic profiles of the upper and lower sidewalls, the following differences can be summarized: (1) In the wedge model, the first interface is vertical and the second interface is tilted, causing the reflection waves \(PP1\) and \(SS1\) generated by the first interface to be the same when they arrive at the upper and lower sidewalls. The second reflection waves \(PP2\) and \(SS2\) in the upper wall survey line arrives later than that from the lower sidewall, and the time delay of the \(S\)-wave is more obvious; (2) Since the first interface of the wedge model is closer to the tunnel face, the energy of reflected waves is stronger than that in aforementioned model with a single-reflective interface; (3) The energy of reflected waves in the seismic profile from the lower sidewall is greater than that from the upper  
Figure 7: The model with wedge fault.  
sidewall; (4) The energy of \(PP2\) is less than that of \(PP1\). The energy of \(SS2\) is slightly larger than that of \(SS1\). When the interface inclination angle is large, the \(S\)-wave is less affected by the change of inclination angle, and the reflected \(S\)-wave energy gradually increases as the inclination angle decreases. However, the \(P\)-wave is greatly affected by the change of the inclination angle, the energy of the reflected \(P\)-wave will decrease first, and the decrease on the survey line on upper sidewall is more obvious.  
Figure 8: 3D \(P\)- and \(S\)-wavefields at different times of the wedge fault model. (**a**) 3D wavefield snapshot of \(P\)-wavefield at different times. (**b**) 3D wavefield snapshot of \(S\)-wavefield at different times.