esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 5 Material Platforms
### Si Photonics Platforms
#### 5.1.4. OPAs Based on the Ge/Si\({}_{3}\)N\({}_{4}\) Platform  
MIR OPAs employing wafer-scale fabrication are attractive to chemical and biological sensing, spectroscopies, thermal LiDAR, and free-space optical communications overcoming some atmospheric turbulence and scintillation [72; 124]. Additionally, MIR OPAs can also benefit from exploiting the transparency window of the atmosphere and high eye-safety optical power levels [72]. However, current Si photonics platforms involving Si and SiO\({}_{2}\) are incompatible with MIR for the strong material absorption [124]. Due to the large transparency window covering 2-14 \(\upmu\)m and CMOS compatibility [124], Ge is a promising material for the MIR waveband. Compared with Si photonic structures, Ge-based photonic structures have a larger TO effect coefficient and a higher refractive index [72]. The free-carrier effect is more pronounced [72]. A Ge/Si\({}_{3}\)N\({}_{4}\) WGA for OPAs presented in Figure 11c was capable of 2D beam steering at 4.6 \(\upmu\)m [72]. The linearly tapered millimeter-long WGA with uniform emission intensity realized an FOV of 12.7\({}^{\circ}\)\(\times\) 12\({}^{\circ}\) and a beamwidth of 3.08\({}^{\circ}\)\(\times\) 0.18\({}^{\circ}\)[72]. Another chip-scale Ge-Si\({}_{3}\)N\({}_{4}\) OPA enabled the same FOV and a beamwidth of 2.86\({}^{\circ}\)\(\times\) 0.47\({}^{\circ}\)[102]. Ge has been widely used in Si photonic foundries, making it attractive for MIR OPAs [72; 102].
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 7 Secure Data-Sharing Protocols
### Secure Data-Sharing Gateways  
The secure data-sharing gateways discussed herein serve as pivotal intermediaries, implementing advanced encryption protocols that directly address the security requirements outlined in the literature. The emphasis on end-to-end encryption, utilizing cryptographic algorithms such as the RSA or ECC, guarantees the the confidentiality of patient data even when being transmitted across diverse stakeholders, which resonates with the need for conventional and novel security requirements to ensure the trustworthiness of IoT-based healthcare systems [48].
# Multi-Material Topology Optimization Taking into Account the Position of Material Interfaces in 3D
## 3 Methods
### Boundary Value Problem of Elastostatics  
The focus of this article is the two-material quasi-static boundary value problem of elastostatics (see Equation (1)). However, the following restrictions apply: the displacements \(\mathbf{u}_{D}\) on the Dirichlet boundary \(\xi_{D}\) and the volume forces \(\mathbf{f}_{b}\) are both set to zero.  
\[\begin{split}\text{div}(\mathbf{\sigma})-\mathbf{f}_{b}&= \mathbf{0}\qquad\text{in}\;\Omega,\\ \mathbf{u}&=\mathbf{u}_{D}\quad\text{on}\;\xi_{D},\\ \mathbf{\sigma}\mathbf{n}&=\mathbf{f}_{t}\quad\text{on}\;\xi_{N}.\end{split} \tag{1}\]  
\(\Omega\) is the domain, \(\mathbf{u}\) the displacement field, \(\xi_{N}\) is the Neumann boundary and \(\mathbf{n}\) its normal. The force \(\mathbf{f}_{t}\) is applied on \(\xi_{N}\).  
[PERSON]'s linear-elastic law is used as the constitutive law, which establishes a relationship between the stress tensor \(\mathbf{\sigma}\) and the strain tensor \(\mathbf{\varepsilon}(\mathbf{u})=\frac{1}{2}(\
abla\mathbf{u}+(\
abla\mathbf{u})^{T})\):  
\[\mathbf{\sigma}=\tilde{\mathbb{C}}[\mathbf{\varepsilon}(\mathbf{u})]. \tag{2}\]  
The elasticity tensor \(\tilde{\mathbb{C}}\) for the case with two materials is \(\tilde{\mathbb{C}}=\chi_{1}(\mathbf{x})\mathbb{C}_{1}+\chi_{2}(\mathbf{x})\mathbb{C}_ {2}\). \(\chi_{i}(\mathbf{x})\) denotes the indicator function of the i-th material. These take the value 1 if the corresponding material is present at a point, otherwise they are 0.  
To summarize, the weak form of the boundary value problem can be written as  
\[\mathbf{a}(\mathbf{u},\mathbf{\psi})=I(\mathbf{\psi})\quad\forall\mathbf{\psi}\in U_{ad}, \tag{3}\]  
where \(\mathbf{a}(\mathbf{u},\mathbf{\psi})\) and \(I(\mathbf{\psi})\) are defined as follows:  
\[\begin{split}\mathbf{a}(\mathbf{u},\mathbf{\psi})&=\int_{ \Omega}\mathbf{\varepsilon}(\mathbf{u})\cdot\tilde{\mathbb{C}}[\mathbf{\varepsilon}(\mathbf{ \psi})]d\Omega,\\ I(\mathbf{\psi})&=\int_{\xi_{N}}\mathbf{f}_{t}\cdot\mathbf{ \psi}d\xi_{s}.\end{split} \tag{4}\]  
The space of kinematically admissible displacement fields is represented by \(U_{ad}\).
# Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration
## 2 Power Quality Issues of WPP Integration
### Voltage Issues of WPP Integration
#### 2.2.1 Voltage Sag  
Voltage sag means a decrease in the RMS voltage between 10% to 90% of the nominal voltage for the duration of a half-cycle to 1 min [24]. Thus, voltage sag can be categorized as instantaneous, momentary, and temporary [28]. In Ref. [29], the dip in voltage is given as 70% voltage reduction for 1 s, whereas the limit for many electronic devices is 85% for 40 milliseconds. Furthermore, the voltage depths of 10% to 15% are commonly due to the switching loads. Hence, voltage sag is of PQ disturbance due to its effects on sensitive equipment and industrial processes. There are many causes of voltage sag such as start-up of WTGs, wind speed variations, fault at the system network, or fault in consumer installation. Other events causing voltage sag are transformer or capacitor energizing, switching off electronic load, or connection of heavy loads. Voltage sag can lead to the disconnection of WTGs which may have negative effects on the stability of the system. Additionally, a large invnsh current, as a result of voltage sag, during the recovery process after the fault, has been cleared [2].
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 3 EO-Based Railway Infrastructure Monitoring: Literature Review
### Comparing Methods in Terms of Accuracy, Efficiency (Cost) and Scalability
Statistical analysis of histograms showed a range of zero to a few millimeters for residual displacement (subtracting InSAR results from GPS).
## 5 Chitosan Nanocarriers in Brain Targeting
### Nanoparticles (NPs)
Surfactant-coated nanoparticles are highly efficient in the targeted delivery of bioactive agents in therapeutic concentrations to the brain with reduced reticuloendothelial system uptake [83]. A report by [PERSON] et al. indicated that P80 coating is important in the interaction between nanoparticles and brain micro-vessel endothelial cells and entry into CNS [84]. The increased drug transport across BBB by passive diffusion is attributed to an increase in the retention of the nanoparticles in the brain blood capillaries and its binding capability to the endothelial cell lining [85]. Nanoparticles can be taken up by the brain capillary endothelial cells via receptor-mediated endocytosis, in which the drug is released followed by diffusion into the brain where the particles undergo transcytosis [83]. On the BBB, many receptors are overexpressed such as transferrin (Tf) receptor, low-density lipoprotein receptor-related protein, etc. These receptors can bind with selected ligands and induce internalization into the cells. Decoration of NPs with receptors enhanced the transport of the NPs through the BBB [86]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Drugs** & **Composition** & \begin{tabular}{c} **Physicochemical** \\ **Properties** \\ \end{tabular}  & \ \begin{tabular}{c} **Biological Studies** \\ **Outcome** \\ \end{tabular}  & **References** \\ \hline \hline \multirow{8}{*}{Rivastigmine} & \multirow{8}{*}{Chitosan} & \multirow{8}{*}{Particle size below 200 nm} & The uptake of the coated & \multirow{8}{*}{ \begin{tabular}{c} \\ nanoparticles by the \\ Sertoli cells was low and \\ revealed reduced toxicity. \\ \end{tabular}  } \\  & & & & \\ \cline{1-1}  
_Appl. Sci._**2019**, \(9\), 2219 \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Drugs** & **Composition** & **Physicochemical Properties** & **Biological Studies Outcome** & **References** \\ \hline Interleukin-1 receptor antagonist & \begin{tabular}{c} Chitosan, \\ poly(ethylene glycol), \\ OX26 monoclonal \\ antibody \\ \end{tabular} & Good stability & \begin{tabular}{c} Good brain uptake, brain \\ tumor reduction, and \\ behavioral recover in vivo. \\ \end{tabular} & [56] \\ \hline Thymoquinone & \begin{tabular}{c} Chitosan, sodium \\ tripolyphosphate \\ \end{tabular} & \
\begin{tabular}{c} Particle size of 172–281 nm, \\ Zeta potential of 24.5–30.3 mV, \\ polydispersity index of \\ 0.13–0.24. \\ \end{tabular} &
\begin{tabular}{c} Sustained drug \\ concentration in the brain. \\ Prolonged contact time of \\ the drug-loaded NPs with \\ the nasal mucosa. \\ \end{tabular} & [57] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont_.
# Periodic Monitoring and Filtering Suppression of Signal Interference in Mine 5G Communication
## 4 Results and Discussion
### Comparing the Interference Suppression Capabilities of Different Methods  
#### 4.2.1 Suppression of In-Band Spurs  
In the uplink signal with a bandwidth of 100 MHz, the in-band spurs interference signals are added. By analyzing the output spectrum, the fusion anti-interference scheme proposed in this paper is compared with equalization and notch filtering. The spectrum of the original signal without any suppression is shown in Figure 6a. Several extremely strong spikes can be found in the frequency domain, which will affect the accuracy of signal reception. Traditional equalization algorithms can provide 10 dB interference-suppression capability, as shown in Figure 6b, but the number of taps needs to be significantly increased,  
Figure 5: Experimental test deployment diagram.  
which is not conducive to maintaining the low-latency characteristics of 5G systems. Although notch filtering is the best way to remove frequency domain spikes in theory, complex designs are required in practical applications, including frequency points, roll-off factors, and filtering strength. Due to the high control difficulty, notch filtering is prone to residual spurs or additional dips, as shown in Figure 6c. The fusion anti-interference scheme achieved 12 dB interference suppression capability, as shown in Figure 6d, where the interference monitoring period was 500 ms.
## 4 Related Work: Generative FTS Models
Another common evaluation approach is to compare it to the performance of structural time series modelling benchmarks such as autoregressive integrated moving average (ARIMA) [60], generalised autoregressive conditional heteroscedasticity (GARCH) [61] and vector autoregressive (VAR) and vector error correction models (VECM) for multivariate data [62]. These evaluation approaches were explored by [PERSON] et al. [11], [PERSON] et al. [12], [PERSON] et al. [13], [PERSON] [14], [PERSON] et al. [15], [PERSON] and [PERSON] [50], [PERSON] et al. [51]. A less common approach in the FTS GAN domain is known as the 'train on real, test on synthetic' cross-validation approach demonstrated by [PERSON] [14] and originally applied by [PERSON] et al. [19] in the medical data domain. A generative model is trained on a portion of training data to produce synthetic data to augment a validation set. Next, two separate classifiers are trained, one on the original validation set and another on an augmented validation set. If the classifier performance improves due to augmentation, the model is considered to generate data \(\mathbb{P}_{g}\) of the same distribution as \(\mathbb{P}_{r}\). As noted in Section 1, none of the above studies explicitly considered regime switching and regime imbalance in conditional generative modelling. Failure to account for this characteristic of complex financial market behaviour leads to 'unconditional' models being trained in'mixed-regime environments' and may not be reflective of current or future regime conditions. Though some cGAN studies attempt to account for regime imbalance [15] by selecting a balanced period, date period selection was based on heuristics rather than an unsupervised breakpoint detection algorithm. Though [PERSON] et al. [15] explores the performance of cGANs to interpolate and extrapolate synthetic data generation for low-dimensional ordinal and conditional variables on toy data, the high-dimensional conditioning variables from other cGAN studies [13; 14] that may be considered representations of regime conditions are in very high temporal dimensions, i.e., 252 days and 100 days, respectively. It is indeterminate if a better lower-dimensional manifold representation for the conditioning variable might improve performance, particularly if interpolated or extrapolated conditional variables are provided to the model.
# Ex Vivo Exposure to Soft Biological Tissues by the 2-um All-Fiber Ultrafast Holmium Laser System
###### Abstract  
We present the results of ex vivo exposure by an ultrafast all-fiber Holmium laser system to porcine longissimus muscle tissues. A simple Ho-doped laser system generated ultrashort pulsed radiation with less than 1 ps pulse width and a repetition rate of 20 MHz at a central wavelength of 2.06 \(\upmu\)m. Single-spot ex vivo experiments were performed at an average power of 0.3 W and different exposure times of \(5,30\) and 60 s, varying the total applied energy in the range of 1.5-18 J. Evaluation of laser radiation exposure was performed according to the depth and diameter of coagulation zones, ablation craters and thermal damage zones during the morphological study. Exposure by ultrashort pulsed radiation with an average power of 0.3 W showed destructive changes in the muscle tissue after 5 s and nucleation of an ablative crater. The maximum ablation efficiency was about 28% at the ablation depth and diameter of 180 \(\upmu\)m and 500 \(\upmu\)m, respectively. The continuous-wave radiation impact at the same parameters resulted only in heating of the near-muscular tissue, without ablation and coagulation traces. Exposure to tissue with an average power at 0.3 W of ultrashort pulsed radiation led, within 30 and 60 s, to similar results as caused by 0.5 W of continuous-wave radiation, although with less carbonization formation.  
fiber laser; holmium laser; 2-\(\upmu\)m radiation; ultrashort pulses; continuous-wave radiation; longissimus muscle tissue; ex vivo experiment; ablation efficiency; ablation area; coagulation; lasers in medicine
## 3 Design and Implementation of the Proposed Scheme
### Image Scrambling  
Step 1: The scramble process is the same as that of AES encryption [27]. The shift rows operation works by shifting the values in each row of the state matrix to the left by a certain number of positions. The number of positions shifted by each row in the matrix is determined by its index. Specifically, the first row is not shifted at all, the second row is shifted by one position, the third row by two positions, and the fourth row by three positions; the process continues for the whole image. Figure 3 shows the example in a matrix P of size 4 \(\times\) 4.
# New Taipei City Smart Pavement Management Center and Road Maintenance Analysis
## 4 Pavement System Modular Work Order and Budget Analysis
### Project Dispatch and Billing Stage  
In the billing stage after project completion, a cloud storage method can be used to manage project billing documents and update the reported case information in real-time, thus simplifying the workflow of the management system. This method effectively shortens the workflow and facilitates the timely viewing and management of various project data.  
By creating a bid list to categorize and organize all project cases, each case contains a complete billing document. The billing document includes cost estimates before construction, photos of the site before construction, photos during construction, and quantity calculations after completion. These data are uploaded to the cloud system for easy access and verification by the road management authorities.  
In the billing process, the cost estimate before construction helps the authorities understand the budget requirements for the project. Photos taken before and during construction provide visual evidence of the project's progress, ensuring that each stage is monitored and traceable. The quantity calculation after completion is used to verify any discrepancies between the final results and the expected quantities, ensuring that the project meets the anticipated standards and provides a basis for subsequent billing. By using cloud storage and real-time logging, this method not only accelerates the billing process but also enhances the transparency and accuracy of data management (Figure 11).
## 4 Simulation Results
### Training Configuration  
In the given scenario, we set 60 episodes with 1000 steps each during the training process. As the mission is continuous, we manually aborted the training process in each training, and the environment was reset automatically after each episode. Since the capacity of the memory buffer we set was 3000, the training process did not start until the buffer was full, thus each USV adopted a random policy in the first 3000 steps, and then the whole network started to be trained in each step afterward.  
The whole training networks of the mission planning for the AUV are constructed based on the PyTorch framework, where the training process is implemented on the NVIDIA GeForce RTX 3060 GPU. The critic network consists of five fully connected layers, including three hidden layers, of which the hidden units are set as 64, 52, and 30, respectively. The ReLU function is adopted as the non-linearity function in the networks. Specifically, the first hidden layer with 64 neurons obtains the concatenated vector of observations \(o\) and actions \(a\), and the data then proceed through the following second and third hidden layers, with 52 and 30 neurons, respectively. The whole process is offline and can be divided into two parts: off-line centralized training and on-line decentralized execution. The advantage of using this method is that the system will have competitive execution speeds compared to the traditional methods during the testing stage, and the calculation could be gathered in the training stage.
# On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control +
## 4 Secrecy Sum-Rate Scaling Analysis
From (12), a lower bound of the sum-rate of the main channel is given by  
\[\lim\limits_{x\to\infty}(1-f(x))^{x}=\lim\limits_{x\to\infty}\left(1-\frac{c_ {3}}{x}\right)^{x}=e^{-c_{3}}>0,\]9 \[\Pr(\mathbf{C1})\triangleq\Pr\left\{|\mathbf{u}_{0}^{[l^{*}]T}\mathbf{h}_{n}| ^{2}\geq\eta_{\mathrm{Q}}^{*}\right\}=e^{-\eta_{\mathrm{Q}}^{*}}, \tag{14}\]0 \[\Pr(\mathbf{C1})\triangleq\Pr\left\{|\mathbf{u}_{0}^{[l^{*}]T}\mathbf{h}_{n}| ^{2}\geq\eta_{\mathrm{Q}}^{*}\right\}=e^{-\eta_{\mathrm{Q}}^{*}}, \tag{14}\]1  
Similarly, from (12), an upper bound of the sum-rate of wiretap channel with external eavesdropping is given by  
\[\Pr(\mathbf{C1})\triangleq\Pr\left\{|\mathbf{u}_{0}^{[l^{*}]T}\mathbf{h}_{n}| ^{2}\geq\eta_{\mathrm{Q}}^{*}\right\}=e^{-\eta_{\mathrm{Q}}^{*}}, \tag{14}\]2 \[\Pr(\mathbf{C1})\triangleq\Pr\left\{|\mathbf{u}_{0}^{[l^{*}]T}\mathbf{h}_{n}| ^{2}\geq\eta_{\mathrm{Q}}^{*}\right\}=e^{-\eta_{\mathrm{Q}}^{*}}, \tag{14}\]3  
Therefore, secrecy sum-rate is lower-bounded by  
\[\Pr(\mathbf{C1})\triangleq\Pr\left\{|\mathbf{u}_{0}^{[l^{*}]T}\mathbf{h}_{n}| ^{2}\geq\eta_{\mathrm{Q}}^{*}\right\}=e^{-\eta_{\mathrm{Q}}^{*}}, \tag{14}\]4  
which achieves full degree-of-freedom gain \(M\) and optimal MUD gain \(\log\log N\) as \(N\) tends to infinity. This completes the proof of the theorem. Remark 1: _Regardless of eavesdropping scenario, our proposed scheme achieves secrecy sum-rate scaling as \(\Theta(M\log(\rho\log N))\) since we consider \(\max_{k\in\mathcal{K}}\|\mathbf{g}_{mk}\|^{2}\) as a scheduling metric for restricting the maximum information leakage level. However, the actual performance will be different depending on eavesdropping scenarios because secrecy sum-rate scaling is an asymptotical analysis metric as \(N\) tends to infinity. It will be verified through simulation results in Section 5. **Remark 2**. : _When \(M=1\), our system model is reduced to single-input and single-output (SISO) system with multiple single antenna equipped eavesdroppers. Thus, our scaling law is reduced by \(N=\Theta\left(\rho^{\frac{M(K-1)+1}{1-\rho_{0}}}\right)=\Theta\left(\rho^{\frac{ K}{K-\rho_{0}}}\right)\). This result exactly agrees with the previous result in [19] (i.e., \(\rho^{\frac{K}{1-\rho_{0}}}\)). Therefore, our result generalizes the conventional scaling law in [19]._
## 4 Ground Segment
### Data Products
All information related to the data quality derived during the entire processing is documented in the metadata and within per-pixel data quality flags [54]. Typically, Level 0 products are contained in the archive allowing searching and ordering by users via an online interface at most 24 h after completion of the corresponding downlink. Afterwards, the processing and delivery of Level 2A products (see Figure 8, solid lines), including geometric and atmospheric corrections, is typically conducted in at most 8 h. As the format for not long-term \begin{table}
\begin{tabular}{c c} \hline \hline
**Product** & **Definition** \\ \hline Level 0 & Time-tagged instrument raw data with auxiliary information (internal) \\ Level 1B & Radiometrically-corrected, spectrally- and geometrically-characterized radiance \\ Level 1C & Orthorectified Level 1B \\ Level 2A & Atmospherically-corrected Level 1C \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Definition of EnMAP data products. archived products, the user can choose an image format of band-sequential, band-interleaved-by-line, and band-interleaved-by-pixel encoding (BSQ, BIL and BIP, respectively), JPEG2000 and GeoTIFF, whereas the metadata format is always Extensible Markup Language (XML). A report in portable document format (PDF) is appended to the product.
# Burst Channel Error Reduction Based on Interleaving for Efficient High-Speed Wireless Communication
## 2 Burst Error, OFDM, and Interleaving
### Interleavers  
Bit errors are more likely to occur in bursts on wireless channels because of the fading nature of the channels and the impulsive noise. The goal of interleaving is to disperse bursts of errors throughout the data stream. It rearranges symbols to be transmitted in a certain order. The receiver uses the reverse rule to revert the sequence [26; 27]. This section focuses only on the interleavers related to our proposal in this paper.