esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design Procedure and Simulation Results
Figure 5: (a) PTE vs. frequency vs. distance at 13.56 MHz; (b) PTE vs. frequency vs. distance at 40.68 MHz; PTE vs. alignment configurations at (c) 13.56 MHz; (d) 40.68 MHz. Figure 6: Proposed geometry coil types. Additionally, the effects of different compositions of tissue, input power and coil shape on the SAR is analyzed. The percentage of skin, fat and muscle varies from person to person, therefore, in addition to 1 mm of skin, 2 mm of fat and 7 mm of muscle, the human tissue model is also formed using 1.5 mm of skin, 2.5 mm of fat and 6 mm of muscle and 2 mm of skin, 3 mm of fat and 5 mm of muscle. Figure 7e,f show the effects of different tissue composition on SAR. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Frequency (MHz)** & **Dry Skin** & **Wet Skin** & **Fat** & **Muscle** \\ \hline Conductivity & 13.56 & 0.23802 & 0.38421 & 0.030354 & 0.62818 \\ [S/m] & 40.68 & 0.37982 & 0.45519 & 0.034136 & 0.66986 \\ \hline Relative & 13.56 & 285.25 & 177.13 & 11.827 & 138.44 \\ permittivity & 40.68 & 122.91 & 92.985 & 7.286 & 82.115 \\ \hline Loss & 13.56 & 1.1062 & 2.8754 & 3.4021 & 6.0152 \\ tangent & 40.68 & 1.3655 & 2.1631 & 2.0703 & 3.6046 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The dielectric properties of the human body tissue at 13.56 MHz and 40.68 MHz [30]. Figure 7: (**a**) PTE vs. frequency for varied distances at 13.56 MHz; (**b**) PTE vs. frequency for varied distances at 40.68 MHz; (**c**) Simulated SAR at 13.56 MHz; (**d**) Simulated SAR at 40.68 MHz (**e**) SAR vs. Tissue Composition at 13.56 MHz. (**f**) SAR vs. Tissue Composition at 40.68 MHz (**g**) SAR vs. Input power at 13.56 MHz. (**h**) SAR vs. Input power at 40.68 MHz (A: 1 mm skin, 2 mm fat and 7 mm muscle, B: 1.5 mm skin, 2.5 mm fat and 6 mm muscle, and C: 2 mm skin, 3 mm fat and 5 mm muscle). To analyze the effects of input power on SAR, in addition to 1 W, the system is also simulated with input powers of 0.3 W and 2 W at 13.56 MHz and 40.68 MHz. Figure 7g,h depicts the change in SAR value with respect to the input power at 13.56 MHz and 40.68 MHz respectively. From Figure 7g,h, the analysis shows that the SAR value increases with increase in input power. For an input power of less than 2 W, the SAR produced is below the limit set by FCC at 13.56 MHz, but, at 40.68 MHz, the SAR exceeds the limit set by FCC for a low input power of 0.3 W.  
To analyze the impacts of coil shape on SAR, a system with circular shaped external and spherical shaped implantable coils is simulated with an input of 1 W at 13.56 MHz and 40.68 MHz [31]. The SAR value at 13.56 MHz and 40.68 MHz are 0.29 W/kg and 3.60 W/kg respectively. From the results, it can be observed that the SAR value at low frequency is small when compared to high frequency irrespective of the shape of the coil. _Step 6: End of Optimization Procedure:_ The proposed 3-coil WPT system designed through simulation is validated by experiments.
## 1 Introduction
### Theoretical Background
#### 1.1.1 Digital Twins and Their Industrial Applications
This abstract model allows capturing and understanding as well as clearly describing the physical twin, its behavior,and its environment at an abstract level [12]. Thus, digital twins will enable engineers, designers, and other stakeholders to assess the consequences of the decisions they make concerning products, processes, and services, as well as to improve the quality and performance of the product. Cyber-physical production systems have considerable advantages over traditional ones and can be conducive to arriving at smart manufacturing solutions. In traditional production systems, physical manufacturing resources are registered in advance by manually establishing device dependencies, which is quite time-consuming and can result in errors. If unexpected changes take place in cyber-physical production systems based on digital twins, machines and sensors are dynamically registered, quick access to them is provided, and machines and sensors can be easily moved, added, or removed, which significantly improves monitoring and control of manufacturing operations [13]. We can see that the digital-twin approach will make manufacturing proactive rather than reactive, so the preventative maintenance of equipment will be more commonplace than dealing with failures. Digital twins and other technologies related to Industry 4.0 can improve all kinds of manufacturing practices. The integration of digital twin concepts into sustainable manufacturing can be regarded as a practical demonstration of how cutting-edge technologies contribute to the development of a circular economy, which can be defined as an economic system aimed at continual use of resources via recycling, reuse, sharing, etc. Using digital twins allows identifying possible machine states, identifying whether the machines need maintenance, measuring the consumption of power, and many other ways of improving efficiency when exploiting resources [14]. Thus, digital twins can be conducive to making environmentally friendly decisions, which is especially topical given the global environmental issues, such as pollution, waste management, and running out of non-renewable resources. Thus, digital twins are widely used for monitoring and prediction as well as for helping humans in their decision-making. Some new technologies have been launched aimed at improving human-machine interactions, but humans have not yet become part of the smart manufacturing system. However, research is being done into creating digital twins for people in order to enable them to become part of the smart manufacturing environment [15]. Digital twins will bring about a paradigm shift in manufacturing systems by bridging the gap between the virtual and physical worlds. Virtual laboratories are one of the ways in which digital twins can be applied.
# Lightweight End-to-End Speech Enhancement Generative Adversarial Network Using Sinc Convolutions
## 2 Related Works  
[PERSON] et al. [19] focus on generative architectures for speech enhancement, which leverage the ability of deep learning to learn complex functions from large example sets. The enhancement mapping is accomplished by the generator, whereas the discriminator, by discriminating between real and fake signals, transmits information to the generator so that the generator can learn to produce outputs that resemble the realistic distribution of clean signals. The proposed system learns from different speakers and noise types, and incorporates them together into the same shared parametrization, which makes the system simple and generalizable in those dimensions.  
On the basis of [19], [PERSON] et al. [30] indicate that all existing SEGAN systems execute the enhancement mapping via a single stage by a single generator, which may not be optimal. In this light, they hypothesize that it would be better to carry out multi-stage enhancement mapping rather than a single-stage one. To this end, they divide the enhancement process into multiple stages, with each stage containing an enhancement mapping. Each mapping is conducted by a generator, and each generator is tasked to further correct the output produced by its predecessor. All these generators are gradually cascaded to enhance a noisy input signal to yield an refined enhanced signal. They propose two improved SEGAN frameworks, namely iterated SEGAN (ISEGAN) and deep SEGAN (DSEGAN). In the ISEGAN system, parameters of its generator are fixed, constraining ISEGAN's generators to apply the same mapping iteratively, as its name implies. DSEGAN's generators have their own independent parameters, allowing them to learn different mappings flexibly. However, the parameters of DSEGAN's generators are \(N_{\text{G}}\) times more numerous than ISEGAN's generators, where \(N_{\text{G}}\) is the number of generators.  
[PERSON] et al. [31] reveal that the existing class of GANs for speech enhancement solely rely on the convolution operation, which may obscure temporal dependencies across the sequence input. To remedy this issue, they propose a self-attention layer adapted from non-local attention, coupled with the convolutional and deconvolutional layers of SEGAN, referred to as SASEGAN. Furthermore, they empirically study the effect of placing the self-attention layer at (de)convolutional layers with varying layer indices, including all layers (as long as memory allows).  
As [PERSON] et al. [19] report, they open the exploration of generative architectures for speech enhancement to progressively incorporate further speech-centric design choices for performance improvement. This paper aims to further optimize SEGAN by fusing the powerful diagram: Since convolution. To our best knowledge, although Sinc convolution has achieved great success and been widely utilized in the some fields of speech signal processing, e.g., speech recognition [27] and speaker verification [26], its implementation on the speech enhancement task remains unexplored.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 5 Simulation Study
### Results and Analysis  
In this section, the performance analysis of the proposed SMTrustOF-based protocol is presented in comparison with existing protocols. Multiple simulation runs under three different scenarios were used to verify the results. Figures 6-8 present the average resultsfor RPL topology, including 26 legitimate nodes, one sink node, and three attacker nodes. Scenario I indicates that there are only static nodes in the network. Scenario II means that there are mobile sender nodes in the network with a mobile to static ratio of 1:3, and a static sink node. Scenario III indicates that there are mobile sender nodes with a mobile to static ratio of 1:3, and the mobile sink node in the network. Four parameters are used for the performance evaluation of our proposed solution, that is, packet loss rate, frequency of node rank changes, throughput, and power consumption. A detailed analysis of results comparison is presented below.  
Figure 6: Results comparison of network performance under Blackhole and Rank attack in Scenario I.  
Figure 7: Results comparison of network performance under Blackhole and Rank attack in Scenario II.
# Whole Organ Engineering: Approaches, Challenges, and Future Directions
## 3 History of Whole Organ Engineering
### Early Success in Animal Studies for Liver Engineering  
The key factor in the liver's distinctive regenerative ability is its unique ECM, which allows for tissue growth and remodeling in a manner that re-establishes the tissue composition of healthy, mature liver tissue after injury [39]. Because of this, utilizing ECM-derived bios scaffolds is crucial for the successful engineering of liver tissue. Similar to the previously mentioned first instance of whole heart engineering, the first engineered whole liver was developed through leveraging native liver ECM architecture via de-cell/re-cell methods. In 2009, [PERSON] et al. adapted [PERSON] et al.'s heart de-cell/re-cell method for the decellularization and recellularization protocols for rat livers [40]. After decellularization, histological analysis found that \(>\)97% of DNA was removed and dye perfusion revealed the decellularized vascular network to be intact, while perfusion recellularization successfully seeded rat hepatocytes onto the vasculature, from which they migrated into the tissue and populated the liver construct [40]. Immunostaining of the liver revealed the cells to be producing hepatic enzymes and proteins at levels similar to that of functional livers. Furthermore, [PERSON] assessed early graft function of the recellularized livers via auxiliary transplantation in rats and ex vivo whole blood perfusion, finding hepatic function and cellular function preserved with minimal signs of ischemic damage [40]. [PERSON] et al.'s work further established whole organ engineering's viability as a substitute to transplantation by demonstrating the ability of an entirely engineered whole organ to be viable and functional after implantation.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 3 Method
### Cross-Frame Association Module
#### 3.3.1 Multimodal Feature Encoding  
To capture the comprehensive representation of targets, this module simultaneously embeds physical size and motion state information to form a multi-modal feature representation as follows:  
Current Frame Feature Encoding: The spatial dimensions of the target are integrated into the feature representation through position encoding, enhancing spatial discriminative power as follows:  
\[Q_{i}=W_{0}f_{i}^{t}+\text{PE}(w_{i},h_{i}),\quad\text{PE}(w,h)=\text{sin} \bigg{(}\frac{w}{W_{0}}\bigg{)}\oplus\text{cos}\bigg{(}\frac{h}{H_{0}}\bigg{)} \tag{9}\]  
Here, \(f_{i}^{t}\) represents the original feature vector of the \(i\)-th target in the current frame; \(W_{0}\) is a learnable projection matrix; \(w_{i}\) and \(h_{i}\) are the width and height of the target, respectively; \(W_{0}\) and \(H_{0}\) are normalization reference constants; and \(\oplus\) denotes the feature concatenation operation.  
Historical Trajectory Encoding: Motion state information is integrated to enhance temporal consistency and motion prediction capability as follows:  
\[K_{j}=W_{K}f_{j}^{t-1}+\text{PE}(v_{j}^{x},v_{j}^{y}),\quad v_{j}^{x}=\frac{x_{ j}^{t-1}-x_{j}^{t-2}}{\Delta t},\quad v_{j}^{y}=\frac{y_{j}^{t-1}-y_{j}^{t-2}}{ \Delta t} \tag{10}\]  
Here, \(f_{j}^{t-1}\) denotes the feature vector of the \(j\)-th target in the previous frame; \(W_{K}\) is a learnable projection matrix; \(v_{j}^{x}\) and \(v_{j}^{y}\) are the velocities of the target in the \(x\) and \(y\) directions, respectively; and \(\Delta t\) is the time interval between adjacent frames.  
This dual-modal feature encoding strategy enables the model to simultaneously focus on the target's appearance features, spatial dimensions, and motion state, providing a more comprehensive basis for subsequent target associations. This approach particularly excels in multi-target scenes where appearance features are similar but motion patterns differ.
# Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations
## 3 Calibration of GNSS-R Land Reflections  
The conversion of CYGNSS-observed count values into calibrated bistatic radar cross section (BRCS) or surface reflectivity information also requires reconsideration for land surfaces.
# Designing Advanced Multistatic Imaging Systems with Optimal 2D Sparse Arrays
## 3 Optimization Algorithm
### Cost Function  
In optimization, the cost function plays a vital role in evaluating solution efficiency and appropriateness within the problem space. Its primary objective is to measure solution quality in terms of optimization objectives while considering problem constraints to enable numerical performance evaluation. The optimization algorithm aims to minimize a function to identify the ideal solution among multiple options. In this case, the goal is to establish the transmitter element spacing (d\({}_{tx}\)) and receiver element spacing (d\({}_{rx}\)) combination that yields the lowest magnitude of the secondary lobes.  
Figure 2 shows the cost function for the case under study. As expected, it presents complex, non-linear, discontinuous, and non-convex landscapes. Furthermore, not every value fulfills the problem requirements, resulting in a difficult optimization task. Therefore, we utilized the GA optimization algorithm due to its ability to handle such complexity. The next sections provide the rationale for selecting this technique over others and the details of the implementation for this particular issue.
## 4 Results and Discussion
### Spatial Resolution  
To characterize the resolution of the XS detector, a USAF1951 spatial resolution target in contact with the detector was illuminated by using a UV light. The basic concept of the centroiding algorithm is to calculate the central position of the incident photon using the charge value collected by each strip according to the induced charge distribution projection on two axes. Well-known decoding algorithms are Gaussian 3 strip (G3S), Gaussian 5 strip (G5S), and modified center of gravity (MCoG), which are summarized in Table 3. G3S and G5S, respectively, use three and five strips to fit the induced charge cloud as a Gaussian function. Modified center of gravity (MCoG) is the simplest and fastest method among all centroid algorithms, while its threshold depends on the noise of the system and distribution of induced charge [17]. For comparison, three centroiding algorithms have been used for decoding the same raw data, and the result images are shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Top Adjacent** & **Bottom** & **Top-Bottom** \\  & **Strips** & **Adjacent Strips** & **Adjacent Strips** \\ \hline Measured capacitance/F & 9.04 \(\times\) 10\({}^{-14}\) & 1.89 \(\times\) 10\({}^{-13}\) & 8.8 \(\times\) 10\({}^{-14}\) \\ Simulated capacitance/F & 9.74 \(\times\) 10\({}^{-14}\) & 2.06 \(\times\) 10\({}^{-13}\) & 9.6 \(\times\) 10\({}^{-14}\) \\ Difference & 7.7\% & 9.0\% & 9.1\% \\ \hline \end{tabular}
\end{table}
Table 2: Inter-electrode capacitance results of simulation and measurement.  
Figure 9: Pulse height distribution of top and bottom XS anodes.  
It is clear that the image of Group 3 Element 2 of the resolution target pattern obtained with G3S can be resolved, which means the spatial resolution in this case is 55.7 \(\upmu\)m. The image of G5S has a better result and reaches a resolution of 49.6 \(\upmu\)m (Group 3 Element 3). This is because G5S takes more strips into account to deduce distortions caused by various noise (readout noise, inaccuracies in anode geometry and amplifiers gain nonlinearity, etc.). MCoG resolves 44.2 \(\upmu\)m (Group 3 Element 4) and the distortion is substantially less than that of G3S and G5S. The reason why MCoG has a better imaging result than Gaussian fitting is that the mismatch between charge distribution on anode and Gaussian function. In the charge induction readout mode, due to the discontinuous structure and geometric deviation of the anode, Gaussian function cannot accurately describe the charge distribution on the XS anode. MCoG takes the charge of the strip as the weight factor of the distance and has a floating threshold, which is more suitable for describing the charge distribution on the anode.
## 4 Discussions
### Implication of Carbon Uptake Potential to Policies  
Urban expansion inevitably results in a loss of vegetation cover and, most likely, reduces the overall carbon sequestration of cities [26; 66]. One way to promote carbon uptake capacity in urban ecosystems can be made possible through designing optimal LMPs. The approach proposed in this study segmented GGA into homogeneous patches, allowing an objective comparison of the effect on carbon uptake from different LMPs. The study assumes that multiple LMPs coexist in the urban areas. The NPP variation within the environmentally homogeneous patches was illustrated, suggesting that the LMPs varied across locations and showed different impact on vegetation carbon uptake. The historical records of LMPs effective for vegetation carbon uptake provide reference candidates for future land management optimization. Policy makers should pay more attention to areas with a high potential of carbon uptake when planning urban development. The optimal LMPs can be identified from existing LMPs that have produced a high target level of NPP flux and, if extended to wider areas, are expected to improve carbon capture from vegetation. One common approach for policy makers to decide optimal LMPs and a target level of carbon capture is to compare the existing LMPs across locations. Comparative to a location with a low NPP, its neighbors presenting a high NPP with identical environmental conditions, if available, are believed to be under better LMPs which form a candidate list of optimal LMPs. The optimal LMPs, identified recursively at each location, are location-dependent because spatial heterogeneity is common in an urban environment and optimal LMPs do not apply uniformly [67]. Assisted by spatial modeling and remote sensing datasets, this study provides a way to automate the extraction of the neighboring references and dig out the LMPs. If the discovered optimal LMPs are freely transferable and can be applied to locations presenting lower carbon uptake, improvement in carbon uptake flux can be achieved. It is worth noting that, because the carbon uptake potential is assessed based on existing LMPs, the optimal LMPs might not be the best choices under the current urban context. Nature-based solutions (NBSs) are actions that protect, sustainably manage, or restore an ecosystem to address societal challenges [68]. In an urban context, the optimal LMPs are examples of NBSs that promote the local nature (in terms of soil, terrain, and vegetation) as a means for realizing a targeted level in vegetation carbon uptake. A large candidate in NBSs makes it possible to create or design a new LMP, resulting in even higher carbon uptake potential. Nevertheless, the advantage of our approach is that the effectiveness of the discovered LMPs has been confirmed by historical records, while any newly designed one must be verified via field observation before implementation. Therefore, the identified optimal LMPs from this study are safer to be carried out.
# Forensic Investigation of Remnant Data on USB Storage Devices Sold in New Zealand
## 5 Discussion  
This section discusses the findings from the forensic disk imaging tool experiments and compares the selected tools' performance (functionalities, hardware usage, and time consumption) in detail. We also discuss the analysis of collected remnant data and present several solutions/guidelines for completely erasing the data to prevent security or privacy risks.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Categories** & **USB Storage** & **USB Drive ID** & **Application** & **Audio** & **Image** & **Message** & **Text** & **Video** \\ \hline \multirow{4}{*}{Small (40 to 160 GB)} & 40 GB & USB-HD-01 & 1045 & 1 & 24 & 0 & 2 & 87 \\ \cline{2-9}  & \multirow{2}{*}{120 GB} & USB-HD-06 & 105,561 & 1874 & 43,672 & 4 & 3208 & 338 \\ \cline{2-9}  & & USB-HD-07 & 163 & 2 & 5 & 0 & 3 & 0 \\ \cline{2-9}  & \multirow{2}{*}{160 GB} & USB-HD-12 & 595,954 & 730 & 29,596 & 26 & 42,253 & 341 \\ \cline{2-9}  & & USB-HD-15 & 7573 & 5 & 1542 & 0 & 2035 & 46 \\ \hline \multirow{4}{*}{Medium (160 to 350 GB)} & \multirow{2}{*}{250 GB} & USB-HD-05 & 205,169 & 5 & 21 & 0 & 66 & 0 \\ \cline{2-9}  & & USB-HD-10 & 189,106 & 4603 & 11,338 & 0 & 542,232 & 415 \\ \cline{2-9}  & \multirow{2}{*}{320 GB} & USB-HD-08 & 19 & 4 & 3 & 0 & 0 & 0 \\ \cline{2-9}  & & USB-HD-14 & 617,139 & 106 & 9229 & 950 & 285 & 25 \\ \hline \multirow{4}{*}{Large (360 to 640 GB)} & \multirow{4}{*}{500 GB} & USB-HD-03 & 80,893 & 14,967 & 103,738 & 0 & 160,454 & 1325 \\ \cline{2-9}  & & USB-HD-04 & 536,671 & 4766 & 99,914 & 48 & 99,652 & 855 \\ \cline{1-1} \cline{2-9}  & & USB-HD-11 & 9 & 5 & 1 & 0 & 0 & 0 \\ \cline{1-1} \cline{2-9}  & \multirow{2}{*}{640 GB} & USB-HD-13 & 1,445,302 & 8215 & 92,111 & 4425 & 56,630 & 237 \\ \cline{1-1} \cline{2-9}  & & USB-HD-02 & 83,524 & 7166 & 82,847 & 69 & 3234 & 321 \\ \hline \multirow{2}{*}{Extra-Large (650 to 100 GB)} & \multirow{2}{*}{750 GB} & USB-HD-17 & 2619 & 405 & 65 & 0 & 20 & 1305 \\ \cline{1-1} \cline{2-9}  & & 1000 GB & USB-HD-16 & 33 & 280 & 66 & 0 & 1255 & 2783 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Second-hand USB external hard drives used for tool evaluation along with various types of data recovered in respective hard drives.