esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Identifying PVI Location
#### 3.2.3 Simplified CNN Model to Minimize the Response Time  
The results showed that Aruco markers can be detected in adverse conditions, albeit the execution time is not suitable for real-time usage. To minimize the time required to detect markers, the authors formulated the following hypothesis:  
**H4:**_CNN models can be simplified by removing some convolutional layers to improve the response time._  
To minimize the response time, the authors have simplified the convolutions layers of the CNN model and used the same parameters for training and validation. As shown in Figure 9, the authors used two FEUs in the simplified model. In the first FEU, the authors used a convolutional layer with a dimension of \(20\times 5\times 5\) and a sigmoid activation function; then Max pooling was used with a pooling filter size of 2 and stride of 2 to downsample the layer's outputs by a scale factor of 2. In the second  
Figure 8: The proposed convolutional neural network (CNN) architecture used in training of markers.  
FEU, a convolutional layer with a dimension of \(200\times 5\times 5\) was used. Dropout layers are placed to prevent an overfitting problem. The simplified model can detect Aruco markers very well in adverse conditions and the execution time is minimized to be suitable for real-time identification of markers. However, this model still failed to detect markers under occlusive conditions.
## 4 Discussion
### Limitations and Future Work  
The purpose of this paper was to introduce a voxelisation-based framework for porosity prediction in metal additive manufacturing. It was not practical to attempt to conclusively cover all of the variables involved in the machine learning classification and future work could further optimise the classifiers or introduce more complex classifier architectures. Future work could also experiment with combinations of undersampling and oversampling as well as other preprocessing techniques.  
It is likely that focussing on the input features during the voxelisation process is more important than the machine learning step and would be more likely to produce significant improvements. The results support this, in that none of the variables tested here showed a strong impact on accuracy, and with improved input features it is very likely that all of the tested models would be more accurate. The voxelisation approach only involved calculating the mean of each variable for each voxel and should be expanded in the future to include other features, such as standard deviation, range, etc., to produce a richer set of input features to train the machine learning models. The current work is a valuable step towards this, having developed a framework within which these tests can be carried out and have shown the viability and computational efficiency of the 2.0 mm voxel size.  
The dataset was from a single production machine operating at manufacturer-recommended settings and from a small number of separate print runs with one fixed geometry. Models trained on this dataset may not generalise well to other geometries without further training; however, this was mitigated by choosing input features that were independent of the geometry or location within the print volume.  
This geometric independence has further advantages for expanding the technique to other geometries. Only cylindrical shapes were tested here; however, AM is well known for its suitability for very complex shapes. The voxel-based approach used here should be very suitable.  
The present work considers the binary presence or absence of porosity in a voxel and the classification of either lack-of-fusion or keyhole pore type. This is useful but predictions of porosity volume, number of pores, and more in-depth consideration of pore type will be the focus of future work. While being more useful, this may also help to increase classifier accuracy by creating better defined classes. For example, keyhole and lack-of-fusion poresare known to form under different process conditions, as discussed in the introductory section of this paper, so they might be better classified individually instead of under the single porous/non-porous classification used here. This was not tested here as the feature set should first be improved.
## 4 Results
### Security Analysis
#### 4.1.2 Phishing Attack  
In this study, we employ public key encryption technology for identity verification, thereby reducing the risk associated with entering passwords on untrusted websites. User passwords are never transmitted, so even if users enter their passwords on a phishing website, attackers cannot obtain the actual password information. Additionally, we introduce indicators such as lights or touch sensors on hardware keys or biometric recognition devices to help users identify trusted identity verification operations, thus avoiding interaction with potential phishing websites or malicious software.  
Furthermore, our Contract Address in Contract Info serves as a crucial element in the authentication process. The Contract Address, generated by the service provider (smart  
Figure 6: Access Control List.  
contract), is linked to the user's account during registration. During authentication, the verifier checks if the registered Contract Address matches the current service provider's Contract Address. This verification mechanism ensures that the verifier authenticates only against the specific service provider and prevents attackers from using forged verifiers for phishing attacks. By validating the Contract Address, we ensure that users interact only with the correct service provider, effectively thwarting phishing attacks.  
Lastly, we implement a challenge response mechanism. During each authentication, a challenge is generated, and the user must provide the corresponding signature response. This mechanism complicates phishing attacks because attackers cannot predict previous challenges. Even if attackers deceive users into providing a response, the response will be invalid without the correct challenge. Therefore, the challenge response mechanism serves as an effective solution to prevent phishing attacks.
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 2 The Concept of Watermarking
### The Major Classification of Digital Watermarking Techniques
#### 2.4.4 Watermarking Work Scope  
Watermarking techniques are categorised into spatial and frequency domains. Spatial watermarking domain refers to the modification (or replacement) of the content of an object including an effect that is minimally noticed. For instance, the LSB technique could be an ideal example. This technique functions by substituting some of the information in particular pixels including the information from an object data. The technique is also applied by substituting the slightly less significant bit(s). Nonetheless, this substitution decreases the contrast or intensity of an object [64].  
In the frequency domain, the multimedia hidden data domain is converted to other formats, after which it is overwritten based on the hidden data. This domain includes many different techniques such as DCT, DFT, DWT and SVD techniques. The DWT and DCT techniques are two of the most commonly used techniques for watermark conversions. In practice, higher object imperceptibility is provided by these two techniques, which in turn demonstrate a robust action against watermark attacks and object manipulation [42].
# Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets
## 5 Results  
### Metrics  
The metrics we used to benchmark the different networks are the following:  
* The Peak Signal-to-Noise Ratio (PSNR);
* The Structural SIMilarity index (SSIM) [45];
* The number of trainable parameters in the network;
* The runtime in seconds of the neural network on a single volume.  
The PSNR is computed as follows, on whole magnitude volumes:  
\[PSNR(x,\hat{x})=10\log_{10}\left(\frac{\max(x)^{2}}{\frac{1}{n}\sum_{i,j,k}(x_ {ij,k}-\hat{x}_{i,j,k})^{2}}\right) \tag{5}\]  
where \(x\) is the ground truth volume, \(\hat{x}\) is the predicted volume (magnitude image), and \(n\) is the total number of points in the ground truth volume (same as the predicted volume). Since this metric compares very local differences, it does not necessarily reflect the global visual comparison of the images. The SSIM was introduced in [45] exactly to take more structural differences or similarities between images into account. It is computed as in the original paper, per slice, then averaged over the volume (the range, however, is computed volume-wise):  
\[SSIM(x,\hat{x})=\frac{(2\mu_{x}\mu_{\hat{x}}+\epsilon_{1})(2\sigma_{x}\sigma_{ \hat{x}}+\epsilon_{2})(cov_{xx}+\epsilon_{3})}{(\mu_{x}^{2}+\mu_{\hat{x}}^{2} +\epsilon_{1})(\sigma_{x}^{2}+\sigma_{\hat{x}}^{2}+\epsilon_{2})(\sigma_{x} \sigma_{\hat{x}}+\epsilon_{3})} \tag{6}\]  
where \(x\) is the ground truth slice, \(\hat{x}\) is the predicted slice, \(\mu_{i}\) is the mean of \(i\), \(\sigma_{i}^{2}\) is the variance of \(i\), \(cov_{ij}\) is the covariance between \(i\) and \(j\), \(c_{1}=(k_{1}L)^{2}\), \(c_{2}=(k_{2}L)^{2}\), \(c_{3}=\frac{c_{2}}{2}\), L is the range of the values of the data (given because computed over the whole volume), and \(k_{1}=0.01\) and \(k_{2}=0.03\).  
While the two aforementioned metrics control the reconstruction quality, it is important to note that this is not the only factor to take into account when designing reconstruction techniques. Because the reconstruction has to happen fast enough for the MR physician to decide whether to re-conduct the exam or not, it is important for the proposed technique to have a reasonable reconstruction speed. For real-time MRI applications or dynamic MRI (e.g., cardiac imaging), it is even more important (for example, in the context of monitoring surgical operations [46]). The runtimes were measured on a computer equipped with a single GPU Quadro P5000 with 16 GB of RAM.  
Concurrently, the number of parameters has to stay relatively low to allow the implementation on the different machines with potentially limited memory, which will probably need to have multiple models (for different contrasts, different organs, or different undersampling schemes including different acceleration factors).
## 4 Discussion
### Costing and Skills  
Although the SMS method is considered cost-intensive, in the present study, surprisingly, the per-sample cost of HT-qPCR was USD 875, whereas the sequencing price was USD 320. An in-house qPCR assay for mapping ARGs from aerosols was also conducted at a comparable price [63]. In yet another study, the SMS was conducted for USD 200 for 10 Gb of data [28]. It is also important to note that the sequencing prices include the bioinformatics analysis. Prices vary depending on the service providers, the platformused, and even the country where these facilities are available. Shipment costs of kits and consumables are also region- and country-bound. The SMS prices are based on sequence providers located in India, China, and Korea, whereas the HT-qPCR costs were obtained from some European countries. A higher skill level was still required for data interpretation and secondary analysis of SMS results. Online freely available visualization software such as MicrobiomeAnalyst [47; 64; 65] and ResistoXplorer [47] are quite user-friendly and easy to use. The total time taken to analyze HT-qPCR was superior as the results were obtained on the same day; however, the sequencing data were generated in about four weeks' time.
###### Abstract
[19] found that the crown beam in the pile-anchored support structure can play a certain coordination role in the displacement of the pile top through field tests. Scholars have conducted an abundance of studies on pile-anchored support systems. However, in the current design of deep and large foundation pits, the deformation of the foundation pit is regarded as a plane strain problem, and its spatial effect is frequently ignored, resulting in the deformation prediction of a foundation pit that differs greatly from the actual measurement, and imposing a negative impact on the construction [20; 21]. [PERSON] et al. [22] compared the analysis results between two-dimensional and three-dimensional finite element models and found that the three-dimensional FEM outperformed the two-dimensional plane strain model in predicting the lateral deformation and vertical soil settlement of underground diaphragm walls. [PERSON] et al. [11] found that the greater the long side of the foundation pit, the greater the horizontal displacement due to the spatial effect. Through case studies, [PERSON] et al. [23] demonstrated that the spatial variability of the soil has a considerable influence on the structural response caused by excavation. [PERSON] et al. [24] found that considering spatial effects during excavation of extra-long foundation pits can effectively reduce the deformation of the supporting structure and the surrounding strata through numerical simulations. The foundation pit deformation results obtained from 3D finite element calculations were compared with the plane strain results, and it was found that the safety factor of the foundation pit obtained from the 3D analysis was greater than that obtained from the 2D analysis [25]. The current research mainly explores the deformation of the pile-anchored support pit excavation process under two-dimensional conditions. However, there are fewer studies on the optimal selection of design parameters of pile-anchored support, and the analysis of deformation induced by deep excavation of foundation pits considering spatial effects has often been ignored. The field monitoring and numerical simulation were conducted to investigate the deformation characteristics of deep excavations with pile-anchor support structures in red sandstone strata. The changes in the horizontal displacement of the piles, ground settlement, and axial force of the anchor cables during the excavation of the foundation pit are compared and analyzed to verify the reliability of the numerical simulations. At the same time, considering the spatial effect of the pit excavation, numerical simulation is used to change the parameters, including the soil layer, piles and anchor cables, to determine the best parameters to control the pit deformation and to propose reasonable parameter values for the design of deep and large pile-anchor supported pits.
## 2 Proposed Scheme and RF Transmission Algorithm
### Signal Post-Processing and Demodulation  
Signal post-processing ensures the data are aligned at the correct position in the complex plane and the data are demodulated. This procedure is detailed in Algorithm 3, which initiates with synchronizing the transmitted and received signals (\(RX\_syn\)) to guarantee that both signals are in time sync. The algorithm proceeds by rotating the polar shape of the signal that has already been synchronized. \(RX\_rot\) displays the formula for constellation rotation. The value \(\theta\) is variable and is defined based on a visual assessment of the received constellation angles. Therefore, the value of \(\theta\) is adjusted as needed in real time. The original signal's characteristics are recovered by defitering the signal (\(RX\_f\)) before signal demodulation. The signal is recovered into its digital data using the n-QAM function (\(demodS\)), which is then concatenated into a single variable (\(ECG\_complex\)). The subsequent steps are equivalent to the inversion of the discretization and normalization procedure, in which each value of the encrypted signal is restored to its original state in time. In summary, the procedure involves the following steps:  
1. Align the transmitted and received signals.
2. Rotate the synchronized signal's polar shape by modifying \(\theta\).
3. De-filter the signal.
4. Demodulate the signal.
5. Concatenate the demodulated signal into a single variable.
6. Reverse the discretization and normalization process to return each encrypted signal value to its original state.  
```
1:Received signal \(ECG_{RX}\), Transmitted signal \(ECG_{TX}\), n-QAM order \(M\)
2:Encrypted signal \(ECG_{encrypt}\)
3:procedureProcess_Received_Signal
4:\(RX_{syn}\leftarrow\text{synchronize}(ECG_{RX},ECG_{TX})\)
5:\(RX_{rot}\gets RX_{syn}\cdot\exp(i\cdot\theta)\)\(\triangleright\) Rotation in the complex plane
6:\(RX_{f}\leftarrow\text{filter\_design}(RX_{rot})\)
7:\(demodS\leftarrow\text{qamdemod}(RX_{f},M)\)
8:\(ECG_{complex}\leftarrow\)[real(\(demodS\)), \(\text{imag}(demodS)\)]
9:\(ECG_{d}\leftarrow[\;]\)
10:for each \(ECG_{complex}\) in \(ECG_{complex}\)do
11:\(ECG_{d}\leftarrow\frac{ECG_{complex}}{(M-1)^{2}}\)
12:endfor
13:return \(ECG_{d}\)
14:\(ECG_{encrypt}\leftarrow[\;]\)
15:for each \(ECG_{d}\) in \(ECG_{d}\)do
16:\(ECG_{encrypt}\gets ECG_{d}\cdot(\max-\min)+\min\)
17:endfor
18:return \(ECG_{encrypt}\)
```  
**Algorithm 3** Post-processing of received signal.
## 2 Optimization Siting Model  
Taking the practical circumstances, the comprehensive fire station location model was based on risk assessment across urban and rural areas. The framework of the optimization siting model is shown in Figure 3, including the risk classification method, revised site planning model, and results analysis, which are provided in the following subsections.
# Low-Power Rectennas in Microwave Wireless Power Transmission
## 5 Conclusions  
This article discusses technological breakthroughs and industrial applications of low-power-density rectennas in wireless power transmission. As an integrated system, a rectenna primarily consists of a rectifier and an antenna. The rectification efficiency of the rectifier under low input power is mainly limited by the intrinsic properties of nonlinear diodes and additional losses introduced by the matching networks. This review has examined various strategies for improving diode performance and recent innovations in matching networks and transmission line structures. In addition, as the critical interface between the rectifier and ambient RF energy, the antenna should not only achieve high gain but also be capable of multiband/wideband, multi-polarization/omni-polarization, or multi-beam/beam-controllable functionalities according to the application scenarios. Looking ahead, the rapid development of rectenna technology has contributed to both technological progress and industrial innovation, showing great potential to replace batteries or other rechargeable devices. Rectennas are expected to find broad applications in  
Figure 27: Demonstration of MPT in future SSPS [143].  
industrial IoT, smart home systems, wearable electronics, implanted healthcare devices, and the realization of future SSPSs.  
This research was funded by NSFC under grant number U22A2015.  
Not applicable.  
The authors declare no conflicts of interest.
# Antarctic Surface Ice Velocity Retrieval from MODIS-Based Mosaic of Antarctica (MOA)
## 4 Result and Analysis  
It is shown in Figure 2 that the flowing pattern extending into the interior of the ice sheet through ice stream channels. By taking processing steps mentioned above in Section 3, we found most valid data located in peripheral ice shelves, which means that the dominant inland ice sheet's velocity is well below the 30 m\(\cdot\)a\({}^{-1}\) in both the \(X\) and \(Y\) dimensions. Although the ice stream may firstly manifest itself as a roughly 15-km-wide area of enhanced flow starting within 100 km of the ice divide [(61)], this characteristic is indistinct because its motion signal upstream is too weak to detect in such scale. Only in large ice shelves such as the Ross or Filchner-Ronne does the dendritic system develop further upstream through tributaries.
## 2 Samples and Experiments  
The identically structured NUV LEDs were grown on a _c_-plane patterned sapphire substrate by using metal-organic chemical vapor deposition (MOCVD), beginning with a \(\sim\)20-nm-thick GaN layer deposited at 520 \({}^{\circ}\)C. After this thin layer growth, the temperature was increased to 1090 \({}^{\circ}\)C to grow a 3-\(\upmu\)m-thick undoped GaN layer, a 1.5-\(\upmu\)m-thick Si-doped n-GaN layer, a 70-nm-thick undoped GaN layer and a 30-nm-thick Si-doped n-GaN layer (n-doping = 5 \(\times\) 10\({}^{18}\) cm\({}^{-3}\)). The MQWs consisting of 3 nm-thick In\({}_{\text{x}}\)Ga\({}_{\text{1-x}}\)N wells sandwiched by 5-nm-thick Al\({}_{\text{0.15}}\)Ga\({}_{\text{0.85}}\)N barriers were grown on a 100-nm-thick InGaN/GaN superlattice layer at 830 \({}^{\circ}\)C. Subsequently, the temperature was raised to 1000 \({}^{\circ}\)C to grow a 20 nm-thick Mg-doped p-Al\({}_{\text{0.25}}\)Ga\({}_{\text{0.75}}\)N electron block layer (EBL). An 80-nm-thick Mg-doped p-GaN layer (p-doping = 7.5 \(\times\) 10\({}^{17}\) cm\({}^{-3}\)) was then grown at 950 \({}^{\circ}\)C. These samples were subsequently annealed at 750 \({}^{\circ}\)C in a N\({}_{2}\) ambient for the activation of acceptors in Mg-doped layers. Trimethyllanium (TMIn), trimethylgallium (TMGa), trimethylaluminum (TMAI), and ammonia (NH\({}_{3}\)) were used as the In, Ga, Al, and N precursors, respectively. Silane (Si\({}_{2}\)H\({}_{6}\)) and bis(cyclopentadienyl)magnesium (Cp\({}_{2}\)Mg) were used as the n- and p-type dopants, respectively. Hydrogen and nitrogen were used as carrier gases. The details of the 5-QW NUV LEDs structure are shown in Figure 1.  
The In compositions of samples #1, #2, #3 and #4 were estimated to be approximately 4.5%, 5.5%, 6.5%, and 7.5%, respectively. These In compositions were varied by controlling the flow rate of TMIn except sample #4 where the QW growth temperature was decreased by 10 \({}^{\circ}\)C from sample #3. After the epitaxial growth of NUV LED samples with different In compositions, an indium-tin-oxide (ITO) contact was evaporated onto the Mg-doped p-type GaN layer to serve as the p-electrode. A Ti/Al/Ti/Au metal contact was then deposited on the Si-doped n-GaN layer to form the n-electrode which was exposed by partial etching. The LEDs were fabricated with lateral electrodes, diced into 1000 \(\times\) 1000 \(\upmu\)m\({}^{2}\) chips, and packaged on metal cans without epoxy for optical and electrical characteristic measurements. Various optoelectronic characteristics measured on these samples are summarized in Table 1. For the ER spectroscopy, the probe beam was generated by using a 150-W Xe-arc lamp, which was combined with a 0.25-m dual grating monochromator (DK240). The LED was vertically illuminated by this probe beam with a wavelength resolution of 0.007 nm. For phase-sensitive detection, a lock-in amplifier (SR81) was used and the internal electric field was modulated by a function generator. A modulation voltage of 100 \(\mathrm{mV_{PP}}\) at 500 Hz was applied to the sample. The reflected beam from the sample was detected by a photomultiplier tube (Hamamatsu R9110).