esa-sceva/satcom-chunk-collection huggingface dataset:
# A Photon Imaging Detector Model with High Resolution and High Counting Rate
## 1 Introduction  
Over the past few decades, event-counting detectors using a microchannel plate (MCP) and a position-sensitive anode have been widely used in biomedical imaging, X-ray spectroscopic measurement and aerospace, etc. [1; 2; 3; 4; 5; 6; 7; 8]. Among the most recent developments of position-sensitive readouts is the cross strip (XS) anode, which has the advantages of high spatial resolution, good linearity, and low gain requirement [9; 10; 11]. One of the major drawbacks of XS detectors is their limited counting rate capabilities; each event has to be fully processed before the arrival of the next one [12]. Data processing electronics is the main component restricting the counting rate. The current pulse signal output by the anode strip needs to be amplified and shaped into an output pulse whose peak amplitude is proportional to the amount of strip charge. The traditional signal processing method uses a shaping amplifier to convert a negative exponential pulse into a Gaussian pulse [13]. However, the pulse width of the Gaussian signal is too wide, making it easy to cause pulse superposition at a high count rate, resulting in image resolution reduction. In addition, each channel of the XS anode detector needs a special preamplifier and shaping amplifier, which increases the overall mass and cost. Another scheme is to develop a special multi-channel application-specific integrated circuit (ASIC), combining the preamplifier and shaping amplifier [14; 15; 16; 17]. This method can greatly reduce the detector mass and power consumption, but the cost of developing ASIC is high.  
Therefore, the digital trapezoidal filtering scheme, which improves the dynamic range of the detector in a low-cost way, is applied. The system automatically sets the shaping time according to the counting rate of the scene, which greatly increases the flexibility and versatility of the detector. In this paper, we discuss the XS anode imaging detector model, in particular, achieving spatial resolution while maintaining high photon-counting rate.
# Robust CNN Compression Framework for Security-Sensitive Embedded Systems
## 3 Methods
### Optimization
```
Input: a distillation temperature \(t\), a learning rate for the student \(\eta_{s}\), a learning rate for the teacher \(\eta_{t}\), the train dataset \(\{(x_{i},y_{i})\}_{i=1}^{n}\) where \(x_{i}\in\mathbb{R}^{d}\), \(y_{i}\in\{0,1\}^{k}\), the \(\ell_{\infty}\) bound \(\epsilon\) for imperceptibility;  Initialize the student weight vector \(w_{s}\in\mathbb{R}^{p}\);  Initialize the teacher weight vector \(w_{t}\in\mathbb{R}^{p}\); while\(w_{t}\) not convergeddo  Sample a data pair (x,y) from the train dataset;  Compute \(\mathcal{L}(f(x;w_{t}),y)\);  Weight Update: \(w_{t}\gets w_{t}-\eta_{t}\
abla\mathcal{L}(f(x;w_{t}),y)\);  end while while\(w_{s}\) not convergeddo  Sample a data pair (x,y) from the train dataset;  For each pixel of \(x\), generate a uniformly random noise \(\epsilon=(\epsilon_{1},\cdots,\epsilon_{d})\sim\mathcal{U}(-\epsilon,\epsilon)\); \(x_{adv}\gets x+\epsilon\); while\(x_{adv}\) not convergeddo  Update: \(x_{adv}=\Pi_{\mathcal{B}(x,\epsilon)}(x_{adv}+\alpha\cdot\text{sgn}(\
abla_{x_{adv }}\mathcal{L}(f(x_{adv},w_{s}),y)))\) ;  end while  Compute the teacher SoftMax output: \(f^{t}(x;w_{t})\);  Compute the student SoftMax output: \(f^{t}(x_{adv};w_{s})\);  Compute \(\mathcal{L}_{\text{APD}}(w_{s})\) with (8);  Update weight: \(w_{s}=\text{prox}_{\eta_{s}\lambda\|\cdot\|_{0}}(w_{s}-\eta_{s}\
abla\mathcal{L }_{\text{APD}}(w_{s}))\)  end while return\(w_{s}\);
```  
**Algorithm 1**Adversarial Pruning with Distillation (APD)
# Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction
## 1 Introduction
To address these limitations and improve the performance of passenger flow prediction, we propose a deep learning framework for metro passenger flow prediction, named MRDSCNN (Multiscale Residual Depthwise Separable Convolutional Neural Network). Based on the travel behavior and operational principles of the metro, we extract the historical passenger flow from the smart card data as input. The proposed approach leverages the residual network to capture spatiotemporal dependency details from multiple temporal patterns of passenger flows and utilizes multi-scale convolution to extract inter-station correlations from two graphs. Subsequently, spatiotemporal dependencies and inter-station correlations features are fused and then fed into the AttBiGRU to integrate the comprehensive global information. The passenger inflow and outflow prediction results are obtainedthrough a fully connected layer. The main contributions of this study can be summarized as follows:  
1. We employ the RDSC module to capture spatiotemporal dependencies between stations from various temporal patterns (real-time, daily, and weekly), which is a residual network structure with a channel attention mechanism. 2. We model inter-station interactions through a network structure correlation graph and passenger flow similarity graph and utilize the MDSC module to enhance multi-scale spatial correlations on these graphs. 3. Experimental results based on real data of metro passenger inflow and outflow prove that our approach outperforms other baseline models in terms of prediction performance. The remainder of this study is organized as follows. In Section 2, we define the problem and describe the architecture of the proposed model, providing a detailed overview of the different components of the framework. In Section 3, we elaborate on the experimental details and discuss the results of the case study. The conclusion is given in Section 4.
## 4 Results of the Experiment  
### Drought Classification  
There is no fixed standard for drought classification based on drought monitoring index. It should be specifically analyzed according to the climatic, geographic, and ecological characteristics of the monitored area. The meaning of different drought detection indexes varies and needs to be analyzed and determined accordingly [27].  
[PERSON] et al. [28] used the TVDI value as the basis for the division of the wet zone (TVDI value range 0-0.2), normal zone (TVDI value range 0.2-0.4), light drought zone (TVDI value range 0.4-0.6), medium drought zone (TVDI value range 0.6-0.8), and severe drought zone (TVDI value range 0.8-1.0). In this paper, the drought classification criteria proposed by [PERSON] were adopted for the drought classification of vegetation areas in Laibin City, as shown in Table 3.  
The monthly NDVI and LST data in Laibin City, synthesized during pre-processing, are utilized for calculating the TVDI index. The TVDI values of all image elements in Laibin City are then obtained to derive the monthly drought inversion results of the TVDI index in the city. These results are overlaid with vegetated areas to extract the drought inversion results of the TVDI index specifically for vegetated areas in Laibin City. A higher TVDI value indicates more severe drought in the region. It is evident from Figure 6 that the TVDI value of the central building land area is greater than that of the vegetated areas, aligning with the actual conditions.  
Figure 5: The construction of NDVI–LST feature space in Laibin.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Drought Rating** & **Scope of TVDI** \\ \hline Moist & 0 \(<\) TVDI \(\leq\) 0.2 \\ Normal & 0.2 \(<\) TVDI \(\leq\) 0.4 \\ Mild drought & 0.4 \(<\) TVDI \(\leq\) 0.6 \\ Moderate drought & 0.6 \(<\) TVDI \(\leq\) 0.8 \\ Severe drought & 0.8 \(<\) TVDI \(\leq\) 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Drought level description.
# The Summertime Diurnal Cycle of Precipitation Derived from IMERG
## 3 Results
### Fitting Errors on the Amplitude and Phase of the Summertime Diurnal Cycle  
Fitting errors on the diurnal cycle of accumulation are shown in Figure 7. For this analysis, harmonic amplitudes or phases with errors that exceed 50% or 3 h are considered unreliable. Errors are found to be small over land; typically they are less than 15% (25%) for the amplitude and less than 0.75 h (0.75 h) for the 24 h (12 h) harmonic. Over ocean, errors in the amplitude and phase of the harmonics are typically higher, especially in the coastal and 45\({}^{\circ}\)-60\({}^{\circ}\)N/S regions. In fact, 24 h amplitudes are mostly found to be unreliable in the 45\({}^{\circ}\)-60\({}^{\circ}\)N/S oceanic regions, whilst 12 h amplitudes are found to be mostly unreliable over the ocean. Overall, harmonic parameters for land are mostly reliable, whilst phases are mostly reliable over ocean in the 45\({}^{\circ}\)N-45\({}^{\circ}\)S region and amplitudes are generally unreliable over the ocean.  
With regards to the fitting errors for precipitation occurrence and intensity (not shown), the distribution of unreliable amplitudes and phases is generally worse than for accumulation. For occurrence, reliable 24 h amplitudes and phases are found over land and north of 30\({}^{\circ}\)N over ocean, whilst 12 h amplitudes (phases) are generally unreliable (reliable) globally. For intensity, the 24 h amplitudes are unreliable almost everywhere (apart from patches of land), whilst the 24 h phases are unreliable for patches across most of the globe (though less so over land). The 12 h amplitudes are unreliable over ocean and patches of land, and the 12 h phases are reliable almost everywhere apart from some coastal regions of the ocean.
# Quad-Beam 4 \(\times\) 2 Array Antenna for Millimeter-Wave 5G Applications
## 4 Results and Discussion
### Performance Comparison
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Ref.** & **Dim in \(\mathbf{\lambda^{3}}\)** & **Res. GHz** & **Design Technique** & **Array Conf.** & **BW in GHz** & **HPBW in xz-plane (deg)** & **X-pol. Plane Plane (dB)** & **HPBW in xz-plane (deg)** & **X-pol. Plane Plane (deg)** & **Ave. Tot. Eff. (\%)** \\ \hline
[16] & \(\begin{array}{c}1.96\times 3.74\times 0.15\end{array}\) & 28 &
\begin{tabular}[]{c} Planar array with hybrid hybrid feeding \\ \end{array} & \(2\times 6\) & 27–28.4 & 57\({}^{\circ}\) & NAV & 20\({}^{\circ}\) & NAV & 11.5 & NAV \\ \hline
[26] & \(\begin{array}{c}4.2\times 4.2\times 1.93\end{array}\) & 28 &
\begin{tabular}[]{c} Multilayer with a groove-gap waveguide \\ \end{array} & \(2\times 2\) & \(\begin{array}{c}26.9-\\ 33.1\end{array}\) & \(16^{\circ}\) & NAV & 16\({}^{\circ}\) & NAV & 22 & NAV \\ \hline
[27] & \(\begin{array}{c}5\times 3\times 0.017\end{array}\) & 30 &
\begin{tabular}[]{c} Magneto-electric \\ dipole with a parallel feed \\ \end{array} & \(4\times 4\) & 19–51 & 17\({}^{\circ}\) & \(-20\) & 16\({}^{\circ}\) & 18 & 18 & 82 \\ \hline
[28] & \(\begin{array}{c}7.25\times 4.3\times 0.07\end{array}\) & 60 &
\begin{tabular}[]{c} Planar array with parallel feed \\ \end{array} & \(1\times 4\) & 50–65 & 65\({}^{\circ}\) & NAV & Omni. & NAV & 5.37 & 65.5 \\ \hline
[29] & \(\begin{array}{c}4.8\times 1.72\times 0.07\end{array}\) & 28 &
\begin{tabular}[]{c} Planar array with hybrid feed \\ \end{array} & \(6\times 2\) & \(\begin{array}{c}27.06-\\ 28.54\end{array}\) & 58\({}^{\circ}\) & NAV & Omni. & NAV & 16 & 83 \\ \hline
[30] & \(\begin{array}{c}1.64\times 3.6\times 0.03\end{array}\) & 28 &
\begin{tabular}[]{c} Planar array with parallel feed \\ \end{array} & \(2\times 4\) & \(\begin{array}{c}27.75-\\ 28.8\end{array}\) & \(54^{\circ}\) & NAV & 20\({}^{\circ}\) & NAV & 11 & NAV \\ \hline
[31] & \(\begin{array}{c}10.55\times 0.02\end{array}\) & \
\begin{tabular}[]{c} Planar dipole \\ array \\ \end{tabular} & 8 &
\begin{tabular}[]{c} 28-29.1 \\ \end{array} & Omni. & NAV & Omni. & NAV & 5 & 77 \\ \hline
[32] & \(\begin{array}{c}1.93\times 0.05\end{array}\) & 28 &
\begin{tabular}[]{c} Planar array with parallel feed \\ \end{array} & \(1\times 2\) & 28–32.5 & 60\({}^{\circ}\) & NAV & 30\({}^{\circ}\) & NAV & 10.8 & NAV \\ \hline
**Prop.** & \(\begin{array}{c}2\times 1.8\times 0.02\end{array}\) & 30 &
\begin{tabular}[]{c} Planar array with parallel feed \\ \end{array} & \(4\times 2\) & \(\begin{array}{c}28.5-\\ 31.1\end{array}\) & \(30^{\circ}\) & \(-20.2\) & \(35^{\circ}\) & \(-26\) & 10.6 & 77 \\ \hline \hline \end{tabular} Note: Array Conf., array configuration; NAV, not available, The dimension of [31] with * is circular structure.  
\end{table} 
Table 1: Comparison of the proposed array antenna with state-of-the-art designs.
## 3 Results and Discussion
### Drying Kinetics
On the other hand, convective (CV) drying took the longest time, which correlates with the lowest average drying rate (Figure 5a). It can be noticed that the most energy-intensive process was the pure convective one (CV), which resulted due to a long operation time (Figure 5b). Application of ultrasound caused a slight decrease in this parameter, despite a higher power requirement (or instantaneous energy consumption) resulting from the additional energy consumer, which is the ultrasound generation system (AUS). Apparent changes were observed for microwave schedules in which SEC reduction in comparison to CV attained 67%, 76%, and 62% for CVMW1, CVMW2, and CVMWUS, respectively (Figure 5b). So many benefits, despite the use of additional power consumers (ultrasonic and microwave generators), resulted from a significant shortening of the drying time. From an economical point of view, this is a very positive effect of ultrasound or microwave enhancement. Microwave and ultrasound technologies have been investigated for their influence on energy consumption during drying. Studies have shown that microwave drying can lead to a decrease in specific energy consumption, with higher microwave power levels resulting in lower energy consumption [28]. Additionally, the use of intermittent microwave drying has been found to reduce drying time and decrease energy consumption [29]. Ultrasound treatment has also been shown to reduce energy consumption during drying, as shorter exposure to elevated temperatures can improve the quality of the dried product [30]. Furthermore, the combination of ultrasound with intermittent microwave and low-temperature, hot-air drying has been found to decrease specific energy consumption,  
Figure 5: (**a**) Average drying rate (DR) and drying time (DT) for certain processes and (**b**) specific energy consumption (SEC) for particular schedules of drying. Different letters above the bars indicate statistically significant differences at \(p<0.05\), according to Tukey post-hoc test. making it a suitable alternative for industrial applications [31; 32]. Overall, both microwave and ultrasound technologies have the potential to reduce energy consumption during drying processes.
# Low Tensor Rank Constrained Image Inpainting Using a Novel Arrangement Scheme
###### Abstract  
Employing low tensor rank decomposition in image inpainting has attracted increasing attention. This study exploited novel tensor arrangement schemes to transform an image (a low-order tensor) to a higher-order tensor without changing the total number of pixels. The developed arrangement schemes enhanced the low rankness of images under three tensor decomposition methods: matrix SVD, tensor train (TT) decomposition, and tensor singular value decomposition (t-SVD). By exploiting the schemes, we solved the image inpainting problem with three low-rank constrained models that use the matrix rank, TT rank, and tubal rank as constrained priors. The tensor tubal rank and tensor train multi-rank were developed from t-SVD and TT decomposition, respectively. Then, ADMM algorithms were efficiently exploited for solving the three models. Experimental results demonstrate that our methods are effective for image inpainting and superior to numerous close methods.  
image inpainting; tensor decomposition; rearrangement scheme; unfolding matrix; alternating direction multiplier method Article
## 7 Evaluation
### Timing Attack Resilience  
In this section, we investigate the timing attack resilience of WSNs with a random delay countermeasure. We evaluate the mitigation effectiveness of the countermeasure in terms of the number of additional measurements an attacker needs to make to achieve a certain percentage of successful guesses about the cluster key.  
The attacker sends to the gateway node two messages with a certain bit equal to zero and one, respectively (as the two messages \(g\) and \(g_{h}i\) in [11]), while the rest of bits are the same. After the gateway node processes each received message, random delays are padded before sending the responses. Different numbers of timing samples are taken from the attacker's results. When the attacker can tell the time difference accurately from statistical analysis of the samples, we call it a successful guess, and we use the percentage of successful guesses to represent the mitigation influence upon timing attacks exercised by the random padding countermeasure.  
We choose Weibull distributed delay as the mitigation against timing attacks because it is easy to adjust the mean and the variance of Weibull distribution [36] by tuning the parameters, and the Weibull distribution is widely used in reliability engineering and failure analysis. We perform an experiment with different parameter sets for the Weibull distributed delays (as shown in Table 2). The experiment is conducted by changing the Weibull distribution shape parameter \(k\in\{0.4,0.37,0.35,0.34\}\) while keeping the scale parameter \(\eta=0.05\) and making the situation when no random paddings are added as a bench mark. The result is depicted in Figure 4. One can see that when the shape parameter \(k\) is becoming larger, the attacking client needs more samples to guess the cluster key. As a consequence, the effectiveness of the mitigating countermeasure is becoming greater. We assume that 90 percent right guesses are adequate for a successful attack. It shows that the attacker only needs about 375 samples to get 90 percent successful guesses when no random delay is padded. While when Weibull distributed delays have the scale parameter as 0.34, 1750 samples are needed for the attacker to conduct a successful timing attack. We believe that this is due to the variance of the Weibull random delay growing with the shape parameter \(k\) as:  
\[var(X)=\eta^{2}\left[\Gamma\left(1+\frac{2}{k}\right)-\left(\Gamma\left(1+ \frac{1}{k}\right)\right)^{2}\right]. \tag{17}\]
# Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
## 5 Performance Evaluation
### Evaluation Results
#### 5.2.3 Network Lifetime  
In this section, the mobile sink can launch multiple data collection tasks anywhere in network randomly, and we conduct simulations to evaluate the performance of NL according to the number of death nodes, of which the NL is defined as the lifetime for 25 percent nodes that exhaust their energy in network.  
In Reference [36], the authors indicated that the death of 25 percent nodes in network could block the WSNs operation greatly, in terms of both the imminent topological disruptions and the decrease of the sensor field's coverage, which could further hinder the data delivering heavily.  
Figure 17 shows the number of death nodes increases by time. In this work, the mobile sink moves randomly in network and launches a data collection task with a period set to 600 s. When the task is completed, the sink could move to other locations in network randomly to continue another data collection.  
In POFA, the node dies faster than that in the proposed scheme, ExOR and RPRDC. The main reason is that it always seeks to improve the reliability of data transmission in each hop, but at the expense of energy consumption, which speeds up the death of nodes. In addition, the time of the death of 200 nodes (25 percent nodes in network) in the proposed scheme is longer than that in ExOR, POFA and RPRDC. The reason is that, despite the nodes on the optimal paths may take more data forwarding tasks, which can reduce their lifetime and furthermore decreases the network connectivity. However, by calculating the optimal path for nodes in advance to forward the data, and employing the flexible routing protocol that could change the data forwarding path adaptively in the low connectivity network, the proposed scheme consumes less energy of nodes, which is also the reason that as the number of the data collection tasks launched by the mobile sink increases, the time for the death of the same number of nodes in the proposed scheme is getting longer than that in ExOR, POFA and RPRDC.  
Figure 17: The number of death nodes varies with time, in addition, \(R_{l}=90\) m, the number of nodes (N) is 800.
## 3 Results and Discussion  
### Influence of Operating Parameters on Droplet Movement  
#### 3.1.1 Spray Pressure  
When the crosswind wind speed was 4 m/s and the V-shaped wind speed was 20 m/s, and the spray pressure was changed from 0.3 MPa to 0.8 MPa at an equal interval of 0.1 MPa, these parameters were substituted into the Equation (3) to obtain the droplet displacements. The cross-section parallel to the \(X\)-\(Z\) direction and 0.30 m from the origin was regarded as the reference surface. The droplet distributions on the surface are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Cross Wind Speed (m/s)** & & **1** & & & **2** & & & **3** & \\ \hline
**V-Shaped Wind Speed (m/s)** & **1** & **2** & **3** & **1** & **2** & **3** & **1** & **2** & **3** \\ \hline
**Spray Pressure (MPa)** & & & & & & & & & & \\ \hline
1 & A\({}_{11}\) & A\({}_{21}\) & A\({}_{31}\) & B\({}_{11}\) & B\({}_{21}\) & B\({}_{31}\) & C\({}_{11}\) & C\({}_{21}\) & C\({}_{31}\) \\
2 & A\({}_{12}\) & A\({}_{22}\) & A\({}_{32}\) & B\({}_{12}\) & B\({}_{22}\) & B\({}_{32}\) & C\({}_{12}\) & C\({}_{22}\) & C\({}_{32}\) \\
3 & A\({}_{13}\) & A\({}_{23}\) & A\({}_{33}\) & B\({}_{13}\) & B\({}_{23}\) & B\({}_{33}\) & C\({}_{13}\) & C\({}_{23}\) & C\({}_{33}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design of three-level simulation experiments of droplet deposition.  
Figure 5: Diagram of the experimental devices: 1. FS-75 industrial fan; 2. Anti-drift spray device in multi-airflow synergy; 3. Droplet collecting plate; 4. Test tube.  
As shown in Figure 6, when the spray pressure is the same, the smaller the droplet diameter is, the further its positions are [43], which indicates that the droplet displacement is positively correlated with its diameter. When the spray pressure is different, the bigger the spray pressure is, the further the overall \(X\)-direction displacement of the droplet group is, which indicates that droplet drift is more serious. The percentage of droplet numbers with \(X\)-direction displacement greater than 10 cm was defined as the proportion of drift droplets. They are shown in Figure 7.  
As shown in Figure 7, when the spray pressure increases from 0.3 MPa to 0.6 MPa, the proportion of drifting droplets increases slowly. However, when the spray pressure continues to increase, the proportion increases sharply, which indicates that the droplet drift is serious. When the spray pressure is 0.8 MPa, the proportion of drifting droplets is about 49.6%. Therefore, in order to reduce droplets drifting, it is necessary to reduce the spray pressure in the working machine process. However, if the spray pressure is too small, the droplets are too large to reach the canopy. Therefore, the range of the spray pressure is from 0.4 MPa to 0.6 MPa.  
Figure 6: Droplet distributions on the reference surface with different spray pressure: 0.3 MPa (a); 0.4 MPa (b); 0.5 MPa (c); 0.6 MPa (d); 0.7 MPa (e); 0.8 MPa (f).  
Figure 7: Proportion of drift droplets with different spray pressure.