esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation  
The simulation flow chart is shown in Figure 3. Firstly, the forces are calculated using the initial status or the simulation results of the previous time step. Then, the accelerations, velocities, and displacements are obtained by solving the force balance equations. After that, the gaps and their change rates are calculated using the displacements and velocities, respectively. Finally, the calculation process above is repeated until the simulation termination time.  
At the beginning of the simulation, the wet multi-disc clutch is in the disengagement status (D), then goes through the engaging process (A), the engagement status (B), and the disengaging process (C), and finally goes back to the disengagement status under the control of hydraulic oil. The variation of hydraulic pressure \(P_{app}\) is shown in Figure 4. From 0 s to 0.3 s, the hydraulic pressure increases linearly from 0 MPa to 0.36 MPa. From 0.3 s to 0.5 s, the hydraulic pressure continues to increase linearly from 0.36 MPa to 1.8 MPa and then keeps constant. The disengaging process starts when the hydraulic pressure starts to decline at 1.5 s. From 1.5 s to 1.6 s, the hydraulic pressure declines rapidly from 1.8 MPa to 0.05 MPa and then declines slowly to 0 MPa at 5 s.  
Figure 3: Flow chart of the simulation.  
When the ATF temperature is 80 \({}^{\circ}\)C, the variation of rotating speed of separator discs \(n_{\text{f1}}\) is shown in Figure 4, the initial and final values of which are 0 rpm. Moreover, the rotating speed of friction discs \(n_{\text{f2}}\) is simplified to keep constant at 1000 rpm. To show the change of the gap between the piston and the first separator disc, the initial value of which is set to 0.05 mm, and the initial gap of the first friction pair is set to 0.45 mm. The initial gap of other friction pairs is set to 0.5 mm, which is equal to the ideal uniform disengaging gap [18]. Other input parameters of the simulation are shown in Table 1. To study the influence of the ATF temperature on the disengaging dynamic characteristics, it is equally divided into five levels from 60 \({}^{\circ}\)C to 140 \({}^{\circ}\)C, and named from T1 to T5.
## 2 Dark Current Noise Correction Method Based on Dark Pixels
### Dark Current Noise Correction Model  
#### 2.4.1 QWIP Photoelectric Conversion Model  
The detector's total response voltage consists of the response of the target object's photocurrent, the dewar's internal background radiation, the optical system's background radiation, the dark current, the readout circuit's bias voltage, and the information acquisition circuit, as shown in Figure 5. The total response voltage of the detector is expressed as:  
\[V_{\text{detector}}=V_{\text{target}}+V_{\text{target\_bg}}+V_{\text{dewar \_bg}}+V_{\text{optical}}+V_{\text{dark}}+V_{\text{bias}} \tag{1}\]  
where \(V_{\text{detector}}\) is the total detector response voltage; \(V_{\text{target}}\) is the response of the target object; \(V_{\text{target\_bg}}\) is the response of the background outside the target; \(V_{\text{dewar\_bg}}\) is the background light response inside the dewar; \(V_{\text{optical}}\) is the background response of the optical system; \(V_{\text{dark}}\) is the dark current response; and \(V_{\text{bias}}\) is the bias response of the readout and information acquisition circuits [34]. In the case of a constant detection target, acquisition environment, and operating mode, only changes in dark current response (\(V_{\text{dark}}\)) can cause the total detector response (\(V_{\text{detector}}\)) to change. The dark current response is expressed as:  
\[V_{\text{dark}}=T_{\text{int}}\cdot I_{\text{dark}}/C_{\text{int}} \tag{2}\]  
where \(T_{\text{int}}\) is the integration time; \(C_{\text{int}}\) is the integration capacitance; and \(I_{\text{dark}}\) is the dark current. \(T_{\text{int}}\) and \(C_{\text{int}}\) were constants in this study and were part of the operating mode.  
Figure 4: LWIR QWIP detection system.  
In the experiment, dark pixels could not pick up background radiation from the target outside the window or inside the dewar. As surface filters on dark pixels were in the low-temperature region, its background radiation was ignored. Dark pixels were set on the focal plane to exclude the influence of noise from other radiation sources; they could directly respond to the dark current level in the focal plane instead of being estimated by mathematical methods. The response of dark pixels is expressed as:  
\[V_{\text{pe\_dark}}=V_{\text{dark}}+V_{\text{bias}} \tag{3}\]  
where \(V_{\text{bias}}\) is the bias response of the readout circuit, the information acquisition circuit, pixel non-uniformity, and external bias. In this study, the detector signal was read from the same channel and the bias response of each pixel did not vary with time.
# Address Space Layout Randomization Next Generation
## 6 ASLR Constraints and Considerations  
The straightforward solution to solving most of the previous weaknesses is to randomize each object independently over the full VM range. Although this idea is quite intuitive [12], multiple practical issues must be addressed properly, in order to achieve a realistic ASLR design. ASLR-NG has been designed by taking into account the following issues:  
**Fragmentation:**: although, from the point of view of security, having objects spread all over the full VM space is the best choice, in some cases it introduces prohibitive fragmentation, which is especially severe in 32-bit systems. Applications that request large objects or make a lot of requests may fail randomly, so it is mandatory to have a mechanism to address this fragmentation.
**Page table footprint:**: a very important aspect that is underestimated is the size of the process page table, because the more the objects are spread, the bigger the page table. This is particularly important in systems with low memory or with a high number of processes. Since each application could have a different level of security, the ASLR design should allow for tuning the page table size versus object spreading.
**Growable areas:**: unfortunately, most applications still use growable areas in some objects. In order to be compatible with these applications, an ASLR must guarantee some form of compatible behavior.
**Homogeneous entropy:**: all objects should have the same amount of entropy, in particular objects of the same type (for example, stacks); otherwise, attackers will focus on the weakest link. Unfortunately, none of the current designs meets this requirement.
**Uniformly distributed:**: all objects should be uniformly distributed; otherwise, attackers can design more effective attacks by focusing on the most frequent addresses.
**ASLR compatibility:**: the ASLR design should be backward-compatible with existing applications. That is, if there is a trade off between security and compatibility, then the design should allow for tuning the application framework to meet application's needs.
###### Abstract  
Farmland changes have a profound impact on agricultural ecosystems and global food security, making the timely and accurate detection of these changes crucial. Remote sensing image change detection provides an effective tool for monitoring farmland dynamics, but existing methods often struggle with high-resolution images due to complex scenes and insufficient multi-scale information capture, particularly in terms of missed detections. Missed detections can lead to underestimating land changes, which affects key areas such as resource allocation, agricultural decision-making, and environmental management. Traditional CNN-based models are limited in extracting global contextual information. To address this, we propose a CNN-Transformer-based Multi-Scale Attention Siamese Network (MT-SiamNet), with a focus on reducing missed detections. The model first extracts multi-scale local features using a CNN, then aggregates global contextual information through a Transformer module, and incorporates an attention mechanism to increase focus on key change areas, thereby effectively reducing missed detections. Experimental results demonstrate that MT-SiamNet achieves superior performance across multiple change detection datasets. Specifically, our method achieves an F1 score of 65.48% on the HRSCD dataset and 75.02% on the CLCD dataset, significantly reducing missed detections and improving the reliability of farmland change detection, thereby providing strong support for agricultural decision-making and environmental management.  
CNN; Transformer; farmland change detection; remote sensing 2020 IEEE/RSJ International Conference on Intelligent Management of Industrial Systems (IEEE), Vol., pp. 221-222, 2010.
## 2 Turn Short and D-Q Transformation
### D-Q Transformation  
Computation is complex for direct control of three-phase voltage currents, and it is difficult to apply control algorithms. Therefore, the D-Q transformation technique is commonly used for accessing three-phase voltage and current as a direct current voltage. Although in this D-Q transformation, when the ideal three-phase power is supplied, the peak value of the phase voltage and zero are produced as outputs, actual phase power includes noise and harmonics, and phase unbalance exists. The D-Q transformation is divided into a static coordinate system, which converts three-phase current or voltage into two-phase current or voltage as shown in Figure 2a and a synchronous coordinate system, which rotates the stationary coordinate system according to angular speed to produce a DC output as shown in Figure 2b.  
These can be expressed in equations as follows.  
\[\left[\begin{array}{c}I_{d}^{s}\\ I_{q}^{s}\end{array}\right]=\frac{2}{3}\left[\begin{array}{ccc}1&-\frac{1}{2} &\frac{1}{2}\\ 0&\frac{\sqrt{3}}{2}&-\frac{\sqrt{3}}{2}\\ 1&1&1\end{array}\right]\begin{array}{c}I_{a}\\ I_{b}\\ I_{c}\end{array}\right] \tag{1}\]  
where \(I_{a}\) is the current in phase a, \(I_{b}\) is the current in phase b, \(I_{c}\) is the current in phase c, \(I_{d}^{s}\) is the d-axis current by D-Q transformation, and \(I_{q}^{s}\) is the current in phase q, which is called the static coordinate system. If the three phases are in parallel, \(I_{d}^{s}=I_{a}\).  
Figure 1: Short circuit in slots.  
At this time, to find the position of the rotor, or the reference magnetic flux \(\theta\) and match the magnetic flux component to it, it must be rotated about the origin by \(\theta\), and this can be calculated using Equation (2).  
\[\left[\begin{array}{c}I_{d}\\ I_{q}\end{array}\right]=\left[\begin{array}{cc}cos\theta&sin\theta\\ -sin\theta&cos\theta\end{array}\right]\left[\begin{array}{c}I_{d}^{s}\\ I_{q}^{s}\end{array}\right] \tag{2}\]  
\(I_{d}\) and \(I_{q}\), which are obtained by Equation (2), are called d-q synchronous coordinate systems. Ideally, \(I_{d}\) will show a DC component, and \(I_{q}\) will always produce zero.  
The output waveforms obtained through this process are shown in Figure 3.  
Figure 3 shows the change in the current owing to D-Q transformation. Figure 3a shows a three-phase input current, Figure 3b shows a D-Q stationary coordinate system from three-phase to two-phase, and Figure 3c shows a D-Q synchronous coordinate system. As shown in Figure 3c, the synchronous coordinate system can be expressed by DC components when it is ideal.  
However, the actual waveforms are irregular and have periodic pulses instead of producing outputs with DC components due to noise and harmonics.
# A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts
## 5 Multi-Identity Full Homomorphic Encryption Scheme
### Our MIBFHE Construction
\] \[=\mathbf{u}_{id_{j}}^{T}-\mathbf{t}_{id_{i}}^{T}\mathbf{A}^{T}+ \mathbf{u}_{id_{i}}^{T}-\mathbf{t}_{id_{j}}^{T}\mathbf{A}^{T}\] \[=-\mathbf{e}_{app_{j}}-\mathbf{e}_{app_{j}};\]  
In our scheme, the homomorphic operations are as follows:  
Homomorphic Addition \(\hat{\mathbf{t}}\hat{\mathbf{C}}^{+}\):  
\[\hat{\mathbf{t}}\hat{\mathbf{C}}^{+} =\hat{\mathbf{t}}\big{(}\hat{\mathbf{C}}_{id_{1}}+\hat{\mathbf{C}}_ {id_{2}}\big{)}\] \[=\hat{\mathbf{t}}\big{(}\mathbf{X}_{id_{1}}+\mathbf{X}_{id_{2}} \big{)}+(msg_{1}+msg_{2})\hat{\mathbf{t}}\mathbf{G}_{Dm^{\prime}}\] \[=(msg_{1}+msg_{2})\hat{\mathbf{t}}\mathbf{G}_{Dm^{\prime}}-\big{(} \hat{\mathbf{e}}_{app_{1}}+\hat{\mathbf{e}}_{app_{2}}\big{)}\]  
Homomorphic Multiplication \(\hat{\mathbf{t}}\hat{\mathbf{C}}^{\times}\):  
\[\hat{\mathbf{t}}\hat{\mathbf{C}}^{\times} =\hat{\mathbf{t}}\hat{\mathbf{C}}_{id_{1}}\cdot\mathbf{G}_{Dm^{ \prime}}^{-1}\big{(}\hat{\mathbf{C}}_{id_{2}}\big{)}\] \[=\hat{\mathbf{t}}\Big{(}\mathbf{X}_{id_{1}}\mathbf{G}_{Dm^{ \prime}}^{-1}\big{(}\hat{\mathbf{C}}_{id_{2}}\big{)}+msg_{1}\mathbf{X}_{id_{2 }}\big{)}+msg_{1}msg_{2}\hat{\mathbf{t}}\mathbf{G}_{Dm^{\prime}}\] \[=msg_{1}msg_{2}\hat{\mathbf{t}}\mathbf{G}_{Dm^{\prime}}-\big{(} \hat{\mathbf{e}}_{app_{1}}\mathbf{G}_{Dm^{\prime}}^{-1}\big{(}\hat{\mathbf{C }}_{id_{2}}\big{)}+msg_{1}\hat{\mathbf{e}}_{app_{2}}\big{)}\]  
Homomorphic NAND \(\hat{\mathbf{t}}\hat{\mathbf{C}}^{NAND}\):  
\[\hat{\mathbf{t}}\hat{\mathbf{C}}^{NAND} =(1-msg_{1}msg_{2})\hat{\mathbf{t}}\mathbf{G}_{Dm^{\prime}}- \hat{\mathbf{t}}\Big{(}\mathbf{X}_{id_{1}}\mathbf{G}_{Dm^{\prime}}^{-1}\big{(} \hat{\mathbf{C}}_{id_{2}}\big{)}+msg_{1}\mathbf{X}_{id_{2}}\Big{)}\] \[=(1-msg_{1}msg_{2})\hat{\mathbf{t}}\mathbf{G}_{Dm^{\prime}}- \big{(}\hat{\mathbf{e}}_{app_{1}}\mathbf{G}_{Dm^{\prime}}^{-1}\big{(}\hat{ \mathbf{C}}_{id_{2}}\big{)}+msg_{1}\hat{\mathbf{e}}_{app_{2}}\big{)}\]  
(5) \(\mathbf{MIBFHE.Dec}\Big{(}MPK,\hat{\mathbf{s}}\hat{\mathbf{k}}_{id},\hat{ \mathbf{C}}_{id_{i}}\Big{)}\): Input \(MPK\), the concatenation of \(D\) keys \(\hat{\mathbf{s}}\hat{\mathbf{k}}_{id}\), and the extended ciphertext \(\hat{\mathbf{C}}_{id_{i}}\). Set the vector \(\widetilde{\mathbf{v}}=\Big{(}msg\cdot\Big{[}\frac{Q}{2}\Big{]},0,\cdots,0\Big{)} ^{T}\in\mathbb{Z}_{\mathbb{Q}}^{Dm^{\prime}}\), and compute:  
\[=-\mathbf{e}_{app_{j}}-\mathbf{e}_{app_{j}};\]0 \[=-\mathbf{e}_{app_{j}}-\mathbf{e}_{app_{j}};\]1 \[=-\mathbf{e}_{app_{j}}-\mathbf{e}_{app_{j}};\]2  
If \(\Big{\|}msg^{{}^{\prime}}-\Big{[}\frac{Q}{2}\Big{]}\Big{\|}\leq\frac{Q}{4}\), output \(msg^{{}^{\prime}}=1\). If \(\Big{\|}msg^{{}^{\prime}}\Big{\|}\leq\frac{Q}{4}\), output \(msg^{{}^{\prime}}=0\).
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas
### Natural RS-Based Proxies
Similarly, low levels of permeability of surfaces increase the vulnerability of the area to water-related hazards [160]. Disasters destroy the natural environment directly through landslide and flooding, or indirectly by human activities, such as intense construction work after a disaster in the recovery stage [34]. Consequently, monitoring land cover provides a proxy information on the natural environment recovery, e.g., land cover change from tree/forest to building as a result of constructions during the recovery processes [49; 161]. Land cover change assessments also contribute to monitoring land degradation, erosion, and deforestation in the recovery processes [162; 163]. Land cover and land use types are also used to indicate the vulnerability of the natural environment [8; 106; 117; 108; 116]. For example, expansion of urbanized areas, cultivation development, and decreasing of natural forest lands increase the eco-environmental vulnerability to hazards [116]. In another example, [PERSON] and [PERSON] [158] associate built-up and bare land with high vulnerability ranks and forest land--with low natural vulnerability rank [158]. In addition, a high proportion of managed natural land cover indicates greater vulnerability because vegetation and crop yield are negatively influenced by environmental degradation [165]. Evapotranspiration is a combined process of evaporation and transpiration and is an important factor in the hydrological cycle. Consequently, it plays a role of a proxy to evaluate flood vulnerability through extracting vegetation cover and type [166]. [PERSON], et al. [167] use gross primary production (GPP) as a proxy for ecosystem-wellness derived from RS data, and assess the effect of resilience of ecological capital in DRM, particularly in recovery processes. GPP represents the photosynthetic capacity of vegetation, which can be used to estimate the vegetation productive capability. [PERSON], et al. [168] employ it also for damage and recovery assessments. Ecological RS studies offer robust proxies that are applicable for urban DRM. For example, high plants productivity is directly related to good soil moisture and nutrient retention. The amount of soil organic carbon is a proxy to assess them and can be extracted using RS data [169; 170]. Therefore, areas with higher soil organic carbon are more suitable for agriculture and consequently more resilient to disasters, particularly those that are linked to climate change [171]. \begin{table}
\begin{tabular}{l l l l l l l} \hline
# Outage Performance of SWIPT-D2D-Based Hybrid Satellite-Terrestrial Networks
## 4 Numerical Results
It can be observed that the outage performance gradually increases as the PS coefficient increases from 0 to 0.5 and significantly decreases as the PS coefficient increases from 0.5 to 1. The increase in outage performance from 0 to 0.5 is owing to the fact that the outage performance gain resulting from using more energy for ID is greater than the loss caused by reducing the transmit power. The decrease in outage performance from 0.5 to 1 is due to the fact that the outage performance gain generated by using more energy for ID is less than the loss caused by reducing the transmit power. Significantly, \(U_{2}\) can achieve the optimal outage performance with \(\rho=0.5\).
## 3 Results and Discussion  
### Stress-Strain Plot  
Figures 1-4 show the stress-strain curves in the direct tension test process for AC-13 and SMA-13 with different aging degrees at 10 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, respectively.  
As shown in Figures 1-4, the overall shape of these plots can all be divided into three similar stages and the plot of AC13-UA in Figure 3 is taken as an example for the illustration. (1) Elastic stage: the plot in the initial loading phase is approximately a straight line, and the stress linearly increases with the increase in strain. (2) Strain-hardening stage: with the elevation of strain, the plot begins to deviate from the straight line in the former stage and to stretch as an approximate arc. With the elevation of strain, stress continues to nonlinearly climb to a peak value with a speed slower than that in the former stage. (3) Strain-softening stage: with the continuous elevation of strain, stress shows a remarkable downward trend as soon as stress reaches over the peak value.  
For both AC-13 and SMA-13, the stress-strain plots exhibit similar a change with the increase in degree of aging at the same test temperature. The plots of AC13-UA, AC13-STOA, AC13-LTOA2d, AC13-LTOA5d and AC13-LTOA8d in Figure 1 are as shown in Figure: With the increase in aging degree, each plot shifts upward and is left as a whole. Particularly, the slope of the straight line in the elastic stage becomes steeper, which means that the elevation of the degree of aging can increase the rigidity of the asphalt mixture.  
The arc length and the radius of curvature of the hardening stage curve decrease. At the same time, the downward trend of the strain-softening stage becomes sharper.  
For both AC-13 and SMA-13, the stress-strain plots present a few changes with the elevation of test temperature under the same degree of aging. The plots of SMA13-UA in Figures 1-4 are used as an example. Each entity generally tends to shift downward and to the right. Specifically, the slope of the elastic stage decreases, which indicates that the rigidity of the asphalt mixture can be weakened with the increase in test temperature. The deviation of the strain-hardening stage curve from the former straight line tends to be more pronounced. Furthermore, the downward trend of the strain-softening stage curve becomes gentler.  
There are also differences between stress-strain curves of AC-13 and SMA-13 when the same aging degree and test temperature are considered. The plots of AC13-LTOA2d and SMA13-LTOA2d in Figure 4 are taken as an example. The coverage height of the plot of SMA-13 is lower than that of AC-13. However, the coverage width of the plot of SMA-13 is remarkably greater than that of AC-13, which causes the whole plot of SMA-13 to be plumper.  
Figure 1: Stress–strain plots of AC-13 and SMA-13 under different aging degrees, plotted at 10 \({}^{\circ}\)C.  
Figure 2: Stress–strain plots of AC-13 and SMA-13 under different aging degrees, plotted at 20 \({}^{\circ}\)C.
# xLSTM-Based Urban Traffic Flow Prediction for Intelligent Transportation Governance
## 3 Results
### Case Study: Real-Time Congestion Prediction and Police Deployment
Figure 6: Forecasted versus actual lane occupancy rates, illustrating the xLSTM model’s sensitivity to localized traffic density changes and congestion buildup. Using the xLSTM forecasting model, traffic conditions were predicted 30 min ahead. At 09:30, the model forecasted a significant alleviation in congestion, with an anticipated average speed increase to 26.2 km/h, an occupancy rate of 35.6%, and a corresponding decrease in vehicle volume to 48 cars (Figure 8). This real-time forecast provided critical lead time for police and traffic control units to dynamically adjust patrol allocations and signal control strategies, optimizing resource usage without unnecessary deployments during the natural dissipation of congestion. Such predictive capability enhances operational efficiency and improves traveler experience by minimizing secondary disruptions.
# High-Aspect-Ratio Silicon Metasurfaces: Design, Fabrication, and Characterization. _Applied_
## 3 Fabrication Process for High-AR Silicon Metasurfaces
To compensate such an effect and provide better control over the etch depth, we reduce the number of working cycles to lower the substrate temperature. Finally, we established an optimized multi-stepped etching process with a gradual decrease in the number of working cycles and a gradual increase in platen power to fabricate tri-layered meta-devices with HAR, as shown in Figure 6.  
In this subsection, we demonstrate the importance of the balance between the etch and passivation in the etching process to obtain the straight sidewall profile, and we proposed a multi-stepped etching process with adjusted working cycles and platen power to help us control the quality of the fabricated silicon metasurfaces in both the lateral and vertical directions. It is worth noting that our proposed fabrication techniques can be applicable to realize dielectric metasurfaces working at infrared even visible regimes with smaller etch feature sizes. Recently, the cryogenic reactive ion etching techniques [52] and modified three-step Bosch process [53] have been adopted to achieve the silicon structures with an AR reaching to 50:1 [54].