esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Trajectory Optimization Method Based on Improved Twin Delayed Deep Deterministic Policy Gradient
### Improved Twin Delayed Deep Deterministic Policy Gradient Algorithm
However, the action after increasing the noise is not necessarily better; it may make the action after increasing the noise obtain a lower reward value, thus storing a poor experience in the experience pool, which is not conducive to the algorithm learning a better strategy. To solve this problem, we propose an improved twin delayed deep deterministic policy gradient algorithm, which dynamically changes the scale of noise to be added by evaluating the reward value of the selected action in advance. This method can effectively reduce the negative impact of noise uncertainty on strategy exploration, and make full use of excellent strategies. Figure 5: Schematic diagram of the Improved Twin Delayed Deep Deterministic Policy Gradient algorithm. The action \(\mu(S_{t})\) selected in the \(S_{t}\) state will first obtain the reward value \(r_{predict}\) of environmental feedback without adding any noise, so as to pre-evaluate the value of the action. The purpose of pre-evaluation is to judge whether the action is an excellent strategy. If the action has a high value, reduce the scale of noise and maintain this strategy as much as possible. If the action has a low value, the scale of noise should be increased to explore better strategies. The variance \(\sigma\) of Gaussian noise is determined by \(r_{predict}\). The higher the reward \(r_{predict}\) is, the smaller the variance \(\sigma\) of noise is, the lower the reward \(r_{predict}\) is, and the more the variance \(\sigma\) of noise is. In this work, the maximum value of \(\sigma\) is limited to 2, and the minimum value is 0.4. If the real-time reward value is less than 0, the exploration intensity should be increased, so \(\sigma\) of negative action is 2. When the real-time reward value is greater than 0, \(\sigma\) decreases along the positive direction of the \(x\)-axis, and the variation amplitude of \(\sigma\) gradually decreases, but \(\sigma\) cannot be reduced to 0, and a small amount of noise still needs to be retained to maintain the exploration. The agent uses action \(a_{t}\) after adding noise to explore. This makes better use of the good strategies explored by the agents and, to some extent, avoids storing the worse explored steps in the experience pool. In (12), the scale of variance \(\sigma\) is selected according to the pre-evaluated reward value. The improved TD3 algorithm is described in Algorithm 1, where the additional step 6 reflects our proposed modification:  
\[\sigma=\left\{ \begin{array}{l}\frac{1}{0S_{predict}+0.5},if\ r_{predict}>0\\ 2,otherwise\end{array} \right.
# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 5 Video Single-Pixel Imaging and Video Snapshot Compressive Imaging
### Video Snapshot Compressive Imaging
However, the main drawback of BIRNAT is its impractical computational time in the training phase (weeks to train a model of size \(256\times 256\times 8\)[82]) and its huge GPU memory consumption that make it unsuitable for large-scale SCI applications especially with the high-resolution videos used in real life. Figure 11: Model of Snapshot Compressive Imaging. The GPU memory storage problem in the training phase is ameliorated in RevSCInet [82] by introducing a reversible CNN network to free the memory from the middle activation generated by each layer of the network. This technique enables to reduce the memory cost from \(O(N)\) to \(O(1)\) (where N is the number of layers). RevSCI-Net rely on an end-to-end CNN model exploring the temporal and spatial correlations of the original video. In addition to the speed issue, some deep learning based reconstruction algorithms, such as BIRNAT, suffer from flexibility and adaptability problems which affect their performances. Therefore, [PERSON] et al. [83] introduced a Meta Modulated Convolutional Network (MetaSCI) as a new scalable and adaptive reconstruction model. MetaSCI is a fully CNN approach that exploits the fast adaption encoding paradigm in order to efficiently reconstruct the video frames in terms of memory consumption. Recently, an ensemble learning based algorithm is proposed in [84], originally exploited in inverse problems, to enhance the scalability of video SCI reconstruction approaches. [PERSON] et al. [85] still work on combining iterative algorithms and deep neural networks. An online Plug-and-play algorithm is proposed to adaptively update the model's parameters using the PnP iteration, which enhance the network's noise resistance. The second part of the paper focus on color SCI videos. The authors present an ADMM optimization and deep neural network to improve the output quality. Finally, a deep equilibrium-based model is proposed in [86] that combines data-driven regularization and stable convergence to deal with the problems of memory requirement and unstable reconstruction in some exiting approaches. Obviously, both categories have their advantages and drawbacks, which make this research direction challenging and very promising for the future if we aim to come up with a memory friendly model that consume less computational cost for our daily life applications.
# Enhancing Resilience in Digital Twins: ASCON-Based Security Solutions for Industry 4.0
## 3 Proposed Solution  
Low-power and resource-constrained IIoT devices are incapable of running traditional cryptographic schemes, such as AES, SHA-256, and RSA. Regardless, these devices are widely used for various Industry 4.0 applications across a range of industry sectors, such as manufacturing, transportation, health, and power grids. Moreover, with the emergence of DT in Industry 4.0, Industrial Internet of Things sensors are an integral part of Digital Twin technology, in which they are used to collect and send data over wired or wireless channels. Hence, it is crucial to secure the communication between DTs and the Industrial Internet of Things, taking into consideration the limited resources they have. In light of the challenges and the gap mentioned in Section 2.2, our proposed solution delves into the heart of this matter, recognizing the urgency to establish a secure communication channel that bridges the physical devices in IIoT applications with their mapped Digital Twins. This section presents innovative approaches, focusing on lightweight algorithms tailored to preserve data confidentiality, integrity, and authenticity. By addressing this critical need, we aim to not only enhance the security posture of IIoT applications but also unlock the full potential of Digital Twin technology. In this work, we proposed a resource-efficient communication scheme based on lightweight cryptographic authenticated encryption to enhance the security of the communication channel between the Digital Twin, Eclipse Ditto [20], and its physical components over the Message Queuing Telemetry Transport (MQTT) protocol using a technique called payload encryption.  
In the following subsections, we first describe the design considerations and the requirements (Section 3.1) we considered, the general description of the implementation approach (Section 3.2), and the experimentation setup steps we took, starting from setting up Eclipse Ditto (Section 3.3) and Mosquito MQTT broker (Section 3.4), implementing and integrating the selected AEAD algorithms (Sections 3.5 and 3.6), and ending by demonstrating the functioning experimentation set-up (Section 3.7).  
End-To-End Payload Encryption and Authentication: Our communication scheme is based on payload authenticated encryption using one of the AEAD (authenticated encryption with associated data) algorithms over the MQTT protocol.
## 2 HDD Trajectory Designs
### Mechanical Design Considering Drilling Mud Pressure  
Taking into account the collapse and instability of the drill paths, we tested, step by step, the feasibility and rationality of each geometrically possible trajectory based on drilling mud pressure theory. The engineering geological conditions of the crossing area for calculation are simplified in this study. The drilling fluid is a non-Newtonian fluid mixed HDD process. It is necessary to exceed the minimum required mud pressure (\(MRP\)) to maintain the recirculation of the drilling mud. The minimum mud pressure consists of the static drilling mud pressure \(Ps\) and the drilling mud pressure loss \(Pl\)[27; 28], as shown in Equation (9) [27; 28].  
\[MRP=Ps+Pl \tag{9}\]Simultaneously, the injected pressure of the drilling fluid must be controlled below the maximum allowable drilling mud pressure (_MAP_) to avoid cavity shear failure (blowout) of the drill path [21]. The _MAP_ is calculated by the Delft equation [27; 29], which assumes that the soil surrounding the borehole experiences perfectly elastic deformation and that the far-field stress around the borehole is isotropic. Once the deformation reaches the plastic threshold defined by the Mohr-Coulomb criterion (the internal pressure reaches the limited pressure), shear failure of the borehole will occur as a blowout. Although the Delft equation has been continuously revised to obtain more accurate analytical solutions [30], Delft equation-based formulations are still the most commonly accepted formulations and are therefore adopted in this study, Equation (10) [29].  
\[\textit{MAP}=\textit{Plim} \tag{10}\]  
Once the geometrically possible trajectory is determined, the _MRP_ and _MAP_ of the whole construction can be calculated predictably for the mechanical considerations. For all of the steps, the maximum allowable pressure (_MAP_) of the fluid is predicted to avoid the triggered blowout.
## 3 Synthesis and Analysis of Planar SALT Trajectories
### Translunar Injection Maneuver Design  
To calculate the departing leg, one has to select the translunar injection burn magnitude and the point at a given low-Earth parking orbit where the TLI impulse is applied. The range the impulse magnitude should belong to is defined by the Jacobi integral range of candidate exterior legs. For example, in the 200 km parking orbit, the TLI burn ensuring \(J\in[0.95,2.41]\) has a magnitude between 3.13 km/s and 3.20 km/s. The location of performing the TLI burn in the parking orbit can be parameterized by, say, the phase angle \(\theta\), counting from the \(x\)-axis of the Earth-Moon rotating frame. Alternatively, the angle \(\alpha\), counted from the \(x^{\prime}\)-axis of the Sun-barycenter frame, may be used. These angles are related to each other by the expression \(\theta=\alpha+\theta_{S}-\pi\mod 2\pi\) (see Figure 5). Note that both angles can be more conveniently defined with respect to the geocentric (rather than barycentric) rotating frame and the above relationship still keeps valid--the angles simply decrease by the same amount, whereas the solar phase \(\theta_{S}\) almost does not change (see Appendix B). Below, this convention is assumed.  
We have propagated more than a hundred thousand potential departing legs by taking different values of the TLI burn magnitude and phase (from the estimated interval and from \([0,2\pi]\), respectively). A departing leg was considered patched with some candidate exterior leg if, leaving the Earth-Moon RoP, it has almost the same phase state (the norm difference of \(10^{-2}\) was tolerated). As a result, we succeeded in patching more than 3000 pairs of exterior and departing legs. The corresponding \"survived\" points of Figure 10 are displayed in Figure 11.
# Follow-Up Control and Image Recognition of Neck Level for Standard Metal Gauge
## 2 Experimental Design  
### The Water Calibration System for the Pipe Prover  
The water verification system is illustrated in Figure 1 and can be described as follows. In the project, a 6000 L pipe prover needs be validated. Two 1000 L standard metal gauges were used to alternately measure the volume Vs of water flowing through the standard tube section of a pipe prover. The volume Vps at the calibrated temperature and pressure was then converted to Standard Temperature and Pressure (STP, 20 \({}^{\circ}\)C and 101.325 kPa) and used to represent the standard volume of the pipe prover. This process can be expressed as:  
\[V_{s}=\sum V_{si} \tag{1}\]  
\[V_{si}=V_{0}+h-h_{0}s \tag{2}\]  
Figure 1: A schematic diagram of the water calibration system used in the pipe prover. Labels include: 1—pool, 2—liquid level collection system, 3—pump, 4—ball push device, 5—volume tube, 6—infrared photoelectric detection switch, (V1–V8)—solenoid valves, (D1 and D2)—detection switches, (P1 and P2)—pressure transmitters, (T1 and T2)—temperature transmitters, C1—commutator, (R1 and R2)—standard metal gauges, and M1—flowmeter.  
\(V_{si}\)--The volume of water measured by the standard metal gauge, L; \(V_{s}\)--The volume of water measured by the accumulative standard metal gauge, L; \(V_{0}\)--Standard metal gauge capacity, L; \(h\)--Liquid level height in the tube, mm; \(h_{0}\)--Liquid level height corresponding to the rated volume, mm; \(s\)--Volume per unit height, L/mm.  
The liquid level height in the tube is the only unknown parameter used for calculating the standard metal gauge volume \(V_{si}\). The accuracy of readings in the standard measurement neck determines the accuracy of water volume calculations in the metal measuring device. As such, measurement precision for liquid levels must be within 0.2 mm in the calibration system. Verification regulations also require that liquid level readings be completed within a specific timeframe. The static setting time (after filling) can be denoted as T\({}_{1}\), the reading time for the liquid level is represented by T\({}_{2}\), the water release time for the bottom valve is T\({}_{3}\), and the time required for a standard metal gauge to be filled is indicated by T\({}_{4}\). As such, the condition T\({}_{1}\) + T\({}_{2}\) + T\({}_{3}\) < T\({}_{4}\) must be satisfied. Liquid level readings for standard metal gauges are typically completed within 20 s, after the gauge is placed in the water and bubbles are allowed to dissipate.  
This study proposes a new level measurement technique, based on image recognition, to improve the accuracy and speed of liquid height readings. The methodology includes two components: acquisition of liquid images and automated recognition of liquid levels.
## 2 Materials and Methodology
### Effect of Temperature (23 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C)
#### 3.1.2 Recycled Concrete Aggregates (RCAs)  
Figure 4 shows the weight loss of the RCAs due to decarbonation before and after forced carbonation at 23, 40, and 60 \({}^{\circ}\)C, considering the time at which the maximum CO\({}_{2}\) uptake of RCA was reached.  
The results of RCA-RP1 have some similarity with those of the MRA. Looking at the Rc, RCA-RP1 has an Rc value closer to that of MRA, explaining the similar uptake. At 23 \({}^{\circ}\)C, the highest amount of CO\({}_{2}\) captured was 13.3% and occurred after 12 h; at 40 \({}^{\circ}\)C, it occurred after 8 h; however, the value decreased to 8%; at 60 \({}^{\circ}\)C, the maximum CO\({}_{2}\) uptake was 11.9% after 3 h, lower than the value obtained at 23 \({}^{\circ}\)C. As per the MRA, the increase in temperature led to a faster carbonation; thus, less exposure time was required to achieve the maximum values of CO\({}_{2}\) capture [45].  
The RCA-RP2 and RCA-IW1 samples presented the highest content of Rc fractions of all RAs in this study, as indicated in Table 4. However, they showed different behaviours with increasing temperature. For RCA-RP2, the increase in temperature led to an increase in CO\({}_{2}\) capture: 5.2%, 15.5%, and 25.0% at 23 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C, respectively. The maximum CO\({}_{2}\) capture at 23 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C occurred at 12 h (34.8%), 5 h (20.4%), and 3 h (22.1%), respectively. As mentioned, this aggregate contains the highest amount of portlandite (Table 4), which is a consequence of its reduced exposure time to natural carbonation. Hence, RCA-IW1 has the highest CO\({}_{2}\) uptake potential. In this case, the increase in the reaction kinetics due to the increase in temperature promotes a more favourable environment for the creation of superficial \"carbonate barriers\" [10, 15]. These barriers, in turn, act by reducing the CO\({}_{2}\) diffusion rate, leading to a lower CO\({}_{2}\) capture. This phenomenon was negligible in all aggregates, except for RCA-IW1, because of its higher amount of portlandite. Therefore, the maximum carbonation temperature depends on the amount of available carbonatable compounds.  
The results for RCA-RP1, RCA-RP2, and RCA-IW1 are coherent with the MRA results, where higher temperatures led to lower carbonation periods. The same effect was presented in the works of [PERSON] et al. [39] and [PERSON] et al. [46].  
Figure 3: Weight loss due to decarbonation before and after forced carbonation, considering the time at which maximum CO\({}_{2}\) uptake was achieved for MRAs at 23, 40, and 60 \({}^{\circ}\)C.
## Materials and Methods
### Characterization  
A transmission electron microscope (TEM, H-8100, Hitachi, Tokyo, Japan) and a field-emission scanning electron microscope (FE-SEM, JEOL-JSM820, JEOL Ltd., Akishima-shi, Japan) were used to investigate the morphology of the obtained samples. X-ray photoelectron spectroscopy (XPS, K-alpha, Thermo Fisher, Waltham, MA, USA) measurements were carried out using monochromatic AlK\(\alpha\) radiation (hv = 1486.6 eV). Contact angle measurements with electrolyte (1.0 M Na\({}_{2}\)SO\({}_{4}\)) droplets were performed to investigate the wettability of the q-MC//CC electrode using an Attena Theta Optical Tensiometer (Biolin Scientific AB, Stockholm, Sweden). Fourier transform infrared (FTIR) spectra were collected using a Nicolet IR 200 FT-IR spectrometer (Thermo Scientific, Waltham, MA, USA), and Raman spectroscopy (DXR Raman spectrometer, Thermo Scientific, Madison, WI, USA) was performed with a 532 nm excitation source.
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 3 SatScope Model Framework
### Network Simulator  
For Traceroute paths utilizing satellite routing, SatScope selects appropriate routing strategies based on the visible access satellites from both terminal users and ground stations. The user access scheme for Starlink is illustrated in Figure 8, where user terminals connect to satellite nodes, which then route traffic through the satellite network to a ground station for internet access.  
Figure 8: Example of ground node accessing users. End-users access the internet via satellites through ground stations.  
This section focuses on several key issues: how ground nodes establish connections with satellites, how routing tasks are allocated, and how the fidelity of continuous simulations can be enhanced.
## 3 Result and Discussion
### Power Capacity Measurement
#### 3.5.4 Power Storage Testing  
This test was conducted for a series of 2 unit 5.5 V 5.0 F supercapacitors equipped on the sensor node. The test examined the charge and discharge time of the supercapacitor is one cycle of CO\({}_{2}\) measurement. The energy capacity required by the sensor node in one cycle of CO\({}_{2}\) measurement was 0.4436 mWh.  
The charging time was obtained at 111 s to achieve the voltage of 4.37 V from 3.57 V, and the discharging time was obtained at 108 s from the voltage of 3.64 V to 3.11 V. Therefore, the power storage can be used well. The result is shown in Figure 16.
## 2 Materials and Methods
### Data Logging and Sensors  
Temperature sensors (T1-T4)/(PT-100) were positioned at the outlet of each tube. A temperature and relative humidity sensor was positioned in the greenhouse (T5, RH5)/([PERSON], HD9009 TRR, Padova- Italy). The temperature that was measured by this sensor was considered the drying tube inlet air temperature. The ambient outdoor temperature and relative humidity were measured using a combined temperature and relative humidity sensor (T6, RH6)/(HD9009 TRR). The air velocity in each tube was measured periodically by an ultrasonic anemometer (W1)/(Windsonic Ultrasonic Wind meter- Gill Instruments, Lymington, Hampshire, UK), which can measure low air velocities to validate the calculated values. The wind speed (9 m above ground) outside the greenhouse was measured with an identical ultrasonic anemometer used for the tube's air velocity. All the up mentioned parameters were measured, and they were stored by a data logger instrument (Delta T, GP2, Cambridge UK). Measurements from all sensors were taken every 15 min and stored by the data logger when the experiment was running. All the sensor positions inside and outside the greenhouse, except for the ambient wind speed, are presented in Figure 5. All the sensors as the data logger were brand new, and they were used for the first time in ambient and experimental conditions, so no calibration was required as they were already factory calibrated. The experimental measurements occurred from 19 June to 6 July 2021. The drying process was repeated 4 times during this period.  
Figure 4: Drying box schematic view.
## 3 Experiment
### Experimental Results  
In this study, we employed the DAXING dataset, collected from 90 smartphones, and the DRESDEN dataset, obtained from 74 cameras. To evaluate the algorithm's performance across various image resolutions, we uniformly cropped images from their top-left corners to sizes of 128 \(\times\) 128, 256 \(\times\) 256, and 512 \(\times\) 512 pixels. For experimentation purposes, we selected a total of 150 images per digital imaging device; out of these, randomly chosen sets of 50 images were used for training, while the remaining 100 images constituted the validation set.  
First, the training set images undergo InvDN filtering, joint estimation, and augmentation methods to extract the PRNU fingerprints of the devices. Subsequently, noisy residuals are computed for the validation set images and PCE values are calculated between these residuals and the PRNU fingerprints extracted from the training set. Based on these PCE values, ROC curves are plotted and Kappa values are calculated to evaluate performance. To validate our proposed algorithm's effectiveness, we compared its ROC curves and Kappa values with those obtained using several other algorithms, i.e., block-matching and 3D filtering (BM3D), Wavelet-based approaches, the feedforward denoising convolutional neural network (xDnCNN), the deep iterative down-up convolutional network (DIDN), and the multi-stage progressive image restoration network (MPRNet). The experimental results can be found in Figures 3 and 4, as well as in Table 1.