esa-sceva/satcom-chunk-collection huggingface dataset:
# Dichotomy Graph Sketch: Summarizing Graph Streams with High Accuracy Based on Deep Learning +
## 2 Related Work
### Data Stream Sketches
proposed Stair Sketch which organized the memory used by different time periods in the shape of stairs in order to memorizing recent events with higher accuracy. In addition, [PERSON] et al. designed a generic framework, Sliding sketches [23], which could be applied to many existing solutions for membership query, frequency query and heavy hitter query, and enabled them to support queries in sliding windows. [PERSON] devised HoppingSketch [24] which improved the original persistent bloom filter [25], and focused on answering temporal membership query. Besides designing novel data stream sketches, researchers also tried to devise some generic optimization method in order to improve the performance of the existing data stream sketches. For example, [PERSON] et al. proposed SF-Sketch [26] which consisted of a Fat-subsketch and a Slim-subsketch. The Fat-subsketch was used for updating and periodically producing the Slim-subsketch, which was then transferred to the remote collector for answering queries quickly and accurately. [PERSON] et al. proposed SketchConf [27], an automatic sketch configuration framework, which efficiently generated memory-optimalconfigurations, and could be applied to order-independent sketches. Stingy Sketch [28] was a new sketch framework which utilized bit-pinching counter tree and prophet queue to optimize both the accuracy and speed for frequency estimation task. To enable the sketch fit into the on-chip memory, [PERSON] et al. proposed a generic technique, self-adaptive counters [29; 30] (SA Counter) which could be applied to typical sketches to improve their accuracy. With the rapid development of the internet, researchers have also applied data stream sketch to _network measurement_. [PERSON] et al. proposed UnivMon [31] which was able to simultaneously completed several measurement tasks including heavy hitter detection, DDos detection, heavy changer detection and entropy estimation. Then, [PERSON] et al. proposed SketchVisor [32], which was a robust network measurement framework for software packet processing. It augmented sketch-based measurement in the data plane with a fast path, which was activated under high traffic load to provide high-performance local measurement with slight accuracy degradations. [PERSON] et al. designed MV-sketch [33] which tracked candidate heavy items inside the sketch data structure via the idea of majority voting. In addtion, [PERSON] et al. proposed Elastic Sketch [34; 35] which was adaptive to various traffic characteristics including available bandwidth, packet rate, and flow size distribution. [PERSON] et al. claimed that sketches incurred significant computation overhead in software switches, and presented the design and implementation of NitroSketch [36], a sketching framework that systematically addresses the performance bottlenecks of sketches without sacrificing robustness and generality.
## 5 Secure Integration in the Smart Grid (SG) Network
Researchers have also introduced new architectures for the V2G system by considering the impact of energy and mobility of vehicles on integrated V2G interfaces. In this direction, recently, [PERSON] _et al._[118] introduced a core system architecture for an end-to-end integration of V2G communications interface under EU co-funded FP7 project \"PowerUp\" and discussed the critical SG integration aspects for each protocol layer within the V2G communications protocol stack. The impact of energy and mobility with respect to the supporting service and a dedicated infrastructure network is highlighted by [PERSON] [119]. It suggests that an e-mobility management provider should interface energy supply stations with the vehicle drivers through an ICT system to coordinate the charging request or the energy supply opportunity with the grid status. A concrete implementation of a charging management system (including stakeholders' requirements, V2G interface, integration of vehicles, and virtual power plant management) is evaluated by [PERSON] _et al._, in [120]. Further, an overview was presented by [PERSON] and [PERSON] [121] over already investigated problems and developed solutions in order to draw a general map of grid integration and the utilization of the EVs flexibility. In summary, the data integrity is focused by the researchers' schemes in the SG as well as V2G networks. However, the secure integrated communications among different control modules and entitieshave not been explored and evaluated in detail. These integration points in the SG as well as V2G networks can be a point of attack by the adversary, if sufficient care is not taken.
## 7 Conclusions  
To the best of our knowledge, only the work of [PERSON] _et al._ and SELDA incorporate secure routing schemes into their secure data aggregation protocols. In their protocols, the secure routing algorithms are only activated when the network nodes do not work honestly. Their secure routing protocols work on-demand or reactively. This means that there has been no work that lays secure routing protocol as the foundation for the secure data aggregation protocol. We argue that the role of a secure routing would be rendered useless if the data aggregation scheme built on it is not secure. Conversely, the secure data aggregation protocol needs a robust underlying routing protocol as its foundation in order to be effectively optimal.  
In an attempt to provide a solution to this problem, we propose a secure routing protocol that considers secure data aggregation. We combine a LEACH-based security protocol with ESPDA, a secure data aggregation protocol suitable for cluster-based WSNs. The protocol design has been presented with evaluations on security, communication and computational complexity analysis. There is always trade-off between security and energy-efficiency. Minimizing the gap between them is a challenging task for WSN protocol design. Our protocol spends a lot of its energy-efficient features for facilitating security compared to the other protocols under evaluation. The point we would like to highlight is the importance of considering secure routing and secure data aggregation together in designing a protocol for WSNs in order to achieve energy-aware yet secure protocols.
# A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing Condition
## 2 Theoretical Foundation
### Prediction Model  
Two popular forecasting models, ARMA and ANN, will be introduced in this section, which also will be used to predict the IMFs in our research.
## 2 Materials and Methods
### Modeling Approach
This approach does not account for the joint relationship between the response at two different tensions, such as \(\theta(\psi_{i})\), and \(\theta(\psi_{j})\), which is important if the user wishes to calculate the response difference to estimate TAW or macroporosity. In this work, it was a requirement to generate a PTF that estimates both the marginal and joint soil-water response such that:  
1. The soil-water response is non-increasing with increasing tension value;
2. Estimates of derived properties (e.g., TAW, macroporosity) are bounded below by zero and above by 1;
3. Lower and upper uncertainty estimates for the soil-water response and derived properties are bounded below by zero and above by one. The previous model for water response [1] used logistic regression for a tension of \(-1500\) kPa and logistic regression to model scaled response difference values. This approach was adopted after comparing several different approaches. The difficulty with that approach was that incorporating new explanatory variables (in this paper, sample depth) made the regression model very complicated. In the present paper, a similar approach to that used in [1] is employed, but logistic regression is replaced by Beta regression [20] within the GAM framework. The principal advantage of the GAM approach is that the nature of the complicated relationship between (say) soil depth and soil-water response can be determined from the data semi-parametrically, thus providing flexibility in the nature of the relationship. The disadvantages are that considerable computational effort is required to determine the form of the smooth function [21], and the complexity of the resulting model is far greater than a simple parametric model. The choice of parametric effects in the GAM was determined largely by experience using the previous model [1]. However, the increased number of samples available for the GAM meant that additional interactions between explanatory variables could be considered, while also incorporating automatic smoothing. In many cases, interactions between variables were not feasible; for example, not all drainage and rock classes of the fines class levels are present for all soil orders of the NZSC and, therefore, were not considered.
## 2 Materials and Methods
### Physical-Layer Security Analysis: Non-Colluding Eavesdroppers  
This section considers the second scenario, in which the eavesdroppers are expected to process the intercepted information independently without the messages being jointly processed. In this context, the eavesdroppers' locations are assumed to be random according to a homogeneous Poisson point process (HPPP) with a density of \(\lambda_{e}\). We assume that the eavesdroppers are distributed in an unbounded Euclidean space of dimension \(U\). The eavesdroppers' information regarding the positions related to Alice can be obtained by assuming that the eavesdroppers are users in the network but are untrusted and do not have the authorization to access the channel [35; 36]. Our analyses are based on selecting the \(i^{th}\) closest eavesdropper to the transmitter Alice, once the distances between Alice and the eavesdroppers have been ordered in an ascending manner [19].
## 3 Results
### Performance of the Multivariate Forward Model  
The top portion of Figure 3A depicts the speech STRF for channel Cz, calculated using detrended cross-correlation and ridge regression. These two methods produced highly correlated STRFs in the lower frequency range (up to approximately 3300 Hz) with a mean \(\pm\) standard deviation Pearson correlation coefficient of 0.723 \(\pm\) 0.109. The correlation decreased in the higher frequency range, with a Pearson correlation coefficient of 0.564 \(\pm\) 0.116 (Figure 3A, far-right panel).  
Figure 3B shows the plots of predicted EEG and original EEG with their similarity measurement in terms of the Pearson correlation coefficient at Cz. The EEG prediction accuracies at Cz were comparable between the detrended cross-correlation function (0.382) and ridge regression (0.389). The topographical distribution of the Pearson correlation coefficients indicated high performance in the central, right frontal, and left temporal lobes for both the detrended cross-correlation function and ridge regression (Figure 3C). However, the topography of the ridge regression revealed more detailed information regarding the performance of individual channels than the detrended cross-correlation function.  
Figure 3: Performance of the multivariate model computed using the detrended cross-correlation function and ridge regression. (**A**) The grand-averaged spectral–temporal receptive field (STRF) estimated using the detrended cross-correlation and ridge regression at the Cz channel. On the far right of the panel, the Pearson correlation between the temporal response function (TRF) of the detrended cross-correlation and ridge regression at each frequency band is indicated. (**B**) The example of original and predicted EEG at the Cz channel. The Pearson correlation between the model’s prediction and original EEG signal is denoted at the top of each figure. (**C**) The EEG prediction performance on a test set across the 64 channels. a.u. denotes amplitude unit. D, R, P, and O denote detrended cross-correlation function, ridge regression, predicted EEG, and original EEG, respectively.
# Development and Application of Intelligent Temperature Control System for Large Aqueduct
## 3 Engineering Application of Intelligent Temperature Control System for Large Aqueduct
### Model Experiments
#### 3.3.2 Measuring and Control Unit  
The measurement and control unit includes pump drive control device 6, pump command control device 7, solenoid valve command control device 8, flow collection module 9 and temperature collection module 10. The temperature sensor in the aqueduct, the temperature sensors 5 at the inlet and return ports and the flow meter 4 are connected to temperature collection module 10 and flow collection module 9, respectively, through concrete signal transmission lines; the pump command control device 7 and the pump drive control device 6 are connected to pump 2 through signal transmission lines and the solenoid valve command control device 8 is connected to solenoid valve 3 through signal transmission lines. The flow collection module 9 and the temperature collection module 10 collect information in real time and transmit the collected environmental parameters, concrete parameters and cooling water parameters to the information processing and decision-making module. The information processing and decision-making module issues control commands for the cooling water and changes the switching of the water pump and the flow rate of the cooling water through the pump command control of device 7 and the solenoid valve command control of device 8.
## 6 Conclusions and Future Works  
This paper studies the bearing-only localization from the improvement of target localization method and the reasonable planning of a sensor trajectory strategy. Under the condition of Gaussian noise, a potential relationship exists between the determinant of the extended coefficient matrix and the noise variance in the angle measurement equation. When the determinant of the extended coefficient matrix is zero, the variance of zero-mean Gaussian noise can be derived. Based on this estimated variance, the bias compensation localization of the target on the 3D space is realized. The estimator can be further refined by the BC-WIV method. In addition, a trajectory planning algorithm encapsulating two trajectory planning strategies are proposed to improve localization accuracy under several constraints. The next moment sensor position is given by optimizing quantified FIM determinant increment as the objective function that evaluates all the historical measurements. The optimal solution always lies on the constraint boundary. Simulation results show that Trajectory 1 leads to a higher localization accuracy at the cost of more computation time while Trajectory 2 has a comparable result with a mild computation burden.  
In the future, we plan to extend our current approach to multiple sensors localization. The research work in this paper is only carried out at the theoretical level, the next research will test the method proposed in the practical communication environment. A team of three Pioneer robots is expected to be deployed in a rectangular area, with one Pioneer robot as the target and the other two Pioneers as tracking sensors to locate the target by the obtained bearing information.
## 4 Calibration and Characterization
### Calibration Procedure
#### 4.2.2 Tipping Curves  
The tipping curves are a series of measurements of the sky at different zenith angles, which are adopted for convenience in the calibration of ground-based radiometers, assuming a horizontally stratified atmosphere. With the information of the sky opacity, it is possible to determine the brightness temperature of the sky at any zenith direction which can be used as a cold calibration target [34; 35].  
Taking into account the radiative transfer equation under the Rayleigh-Jeans approximation and the assumption of a single layer atmosphere, the antenna temperature under a zenith angle \(\theta\) is given by  
\[T_{A}(\theta)=T_{cos}\,e^{-\tau(\theta)}+T_{m}(\theta)\left(1-e^{-\tau(\theta )}\right), \tag{19}\]  
where \(T_{cos}\) is the cosmic background radiation (\(\sim\)2.7 K), \(T_{m}(\theta)\) is the average temperature of the troposphere, and \(\tau(\theta)\) is the total slant opacity. According to [34], the slight angular dependence of \(T_{m}(\theta)\) can be ignored for low opacity channels such as the ones selected for MERITXELL, and its value can be estimated from the ground temperature \(T_{gr}\) as \(T_{m}(\theta)\approx T_{m}(0)\approx T_{gr}-10\) K. Moreover, the total slant opacity can be defined as  
\[\tau(\theta)=\tau(0)\sec(\theta), \tag{20}\]  
where \(\sec(\theta)\) is equivalent to the number of atmospheres that contribute to the antenna temperature, and then, from (19)  
\[T_{A}(\theta)=T_{m}+\left(T_{cos}-T_{m}\right)\left(L_{atm}\right)^{\sec( \theta)}, \tag{21}\]  
where \(L_{atm}=e^{-\tau(0)}\) is the total atmosphere attenuation at zenith direction.  
Equation (21) defines the tipping curves for a ground-based microwave radiometer, which allow to determine the atmospheric opacity at zenith \(\tau(0)\), and then to obtain the \(T_{A}(\theta)\) values to be used as cold temperature points in the hot-cold calibration. Eventually, (21) can be combined with the hot-cold calibration procedure (13), in order to obtain the tipping cures in terms of measured power as  
\[P_{cold}(\theta)=a\left(\left(T_{m}-T_{hot}\right)+\left(T_{cos}-T_{m}\right) \left(L_{atm}\right)^{\sec(\theta)}\right)+P_{hot}. \tag{22}\]
## 3 An Integral Sliding Mode Compensation Method with Weakened Integrals
### Stability Analysis  
To judge whether the system satisfies the sliding mode surface reachability and stability, the Lyapunov function was selected in accordance with the Lyapunov stability theory:  
\[V=\frac{S^{2}}{2} \tag{21}\]\[\overset{\bullet}{V}=\overset{\bullet}{SS}=S(\frac{u_{1}}{L_{0}}+f+m\overset{ \bullet}{\rho})=S(f-\varepsilon|S|\text{sgn}(S)-\alpha S) \tag{22}\]  
When \(S>0\), we made \(\overset{\bullet}{V}=S(f-\varepsilon S-\alpha S)<0\) and obtained \(\varepsilon>\frac{f}{5}-\alpha\).  
When \(S<0\), we made \(\overset{\bullet}{V}=S(f-\varepsilon S-\alpha S)<0\) and obtained \(\varepsilon>\frac{f}{5}-\alpha\).  
In summary, \(\overset{\bullet}{V}\) is always negative when \(\varepsilon>\frac{f}{5}-\alpha\), and \(\overset{\bullet}{V}=0\) only when \(S=0\), which proves that the system is stable. Even if the system parameters are mismatched or disturbed by other factors, this reaching law enables the system to reach the surface for sliding mode motion and makes the system stable.
## 4 Mathematical Validation
### Validation (10 Hz)
#### 4.2.1 Without Noise  
Firstly, the feasibility of WPPE without ambient noise is tested by simulation. The excitation load in Figure 8 was input into the finite element model shown in Figure 6, and the trimmed vibration acceleration response was obtained, as shown in Figure 9a-c. Figure 9a, Figure 9b, and Figure 9c, respectively, show the acceleration time history curve of the position of sensor #2-4 under three damage conditions.  
Figure 10 shows the sub-band energy ratio of the vibration response signals at the measured location (2-4) under three working conditions after wavelet packet decomposition and normalization. The component with the highest normalized energy is selected for reconstruction.  
In order to achieve quantitative visualization of the structural state, PE values are used to characterize the state of the structure. The PE value of the signal can be obtained by substituting the reconstructed acceleration response signal into Equations (2)-(6). Table 2 shows the PE values of each acceleration sensor under three damage conditions. It can be seen that the PE value of the signal can reflect the state of the structure. Then, the PE value in the damaged state and the PE value in the nondestructive state of the structure are substituted into Equation (7), and the PED value can be obtained (Figure 11). Consistently with the results of the real experiment, in the simulation experiment, when the steel bridge deck is damaged, the PED value near the damaged location will mutate, and the degree of change is relatively large. This means that the degree of damage at the damaged location can be identified by the indicators obtained after processing the vibration data at the location of the sensor in Figure 3b using WPPE technology.  
Figure 8: Dynamic load input.  
Figure 9: _Cont._Figure 10: The proportion of energy in each frequency band.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Case** & **\#2-1** & **\#2-2** & **\#2-3** & **\#2-4** & **\#2-5** & **\#2-6** & **\#2-7** & **\#2-8** \\ \hline Case 1 & 1.1552 & 1.1552 & 1.1552 & 1.1552 & 1.1552 & 1.1552 & 1.1552 & 1.1552 \\ Case 2 & 1.1551 & 1.1551 & 1.1551 & 1.1507 & 1.1509 & 1.1551 & 1.1551 & 1.1551 \\ \(\text{PED}_{\text{case 1,case 2}}\) & 0.0001 & 0.0001 & 0.0001 & 0.0045 & 0.0043 & 0.0001 & 0.0001 & 0.0001 \\ Case 3 & 1.1550 & 1.1550 & 1.1551 & 1.1492 & 1.1493 & 1.1551 & 1.1551 & 1.1550 \\ \(\text{PED}_{\text{case 1,case 3}}\) & 0.0002 & 0.0002 & 0.0001 & 0.0060 & 0.0059 & 0.0001 & 0.0001 & 0.0002 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PE value under the three conditions.  
Figure 9: Vibration data: (**a–c**) the time domain vibration data under Conditions 1, 2, and 3, respectively; (**d–f**) show the spectra plots under Conditions 1, 2, and 3, respectively.
# Pharmacokinetics of Pullulan-Dexamethasone Conjugates in Retinal Drug Delivery
## 2 Materials and Methods
### Ex Vivo Retinal Studies  
#### 2.4.1 Ex Vivo Mouse Retinal Organ Culture  
Six day old (PN6) wild-type mice (C57 BL/6) were used in this study. The mice were housed and bred under standard white cyclic lighting with free access to food and water. All mouse procedures were approved by the Tubingen University committee on animal protection (Mitteilung nach SS4 Abs. 3 TierSchG Nr. AK 03/20 M) and performed in compliance with the Association for Research in Vision and Ophthalmology ARVO Statement.  
The preparation of organotypic retinal culture and maintenance of the retinal explants were performed according to published protocols [38; 39; 40; 41] (for more details see SI-2). The tissues were randomly assigned to the following treatment groups: Cy3-pullulan (1.7 mg/mL), Cy3-pullulan-DEX (0.7, 1.4, and 1.9 mg/mL) and untreated control. The treatments were carefully applied on the top of the retinal tissues (on the ganglion cell layer) using volumes of 15 \(\upmu\)L. Six retinal tissues were used for each group. The complete medium (1 mL) under the retina was changed every 48 h and maintained in a humidified atmosphere of 5% CO\({}_{2}\) at 37 \({}^{\circ}\)C for six days.  
The dying cells in the retinal explants were monitored using TUNEL assay with in situ cell death detection kit based on conjugated fluorescein isothiamate [42]. The percentage of positive cells was derived by dividing the number of positive cells by the total number of  
Figure 1: Chemical structures of fluorescently labelled pullulan conjugates. Synthetic details can be found in a recent publication [26] and in Supporting Information SI-1.  
outer nuclear layer (ONL) or inner nuclear layer (INL) cells. One-way ANOVA method and [PERSON]'s multiple comparisons test were selected for the statistical analyses, ****p < 0.0001.