esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Security Analysis
### Anonymity and Message-Vehicle Untracability
The vehicle computes \(AEnc(h,r_{i},ID)\rightarrow(K_{1},K_{2})=(g^{t_{i}},IDh^{t_{i}})\) and sends \((K_{1},K_{2})\), \((\overline{m},m^{\prime})\), \(\mathcal{R}\) to the SA. The \(ID\) is revealed since \(CA\in\)Corrupted. The adversary has knowledge of all pairs \((\overline{m}_{i},m^{\prime}_{i})\), \((l_{i},s_{i},b_{i})\) since RSUs collude. The SA will respond using the public key of the vehicle \(enc_{V}(I||s)\) (The public key of the driver can be stored in \(BB_{SA}\) or can be included in each request). The adversary will attempt to identify which of the \((l_{i},s_{i})\) is the encryption \(enc_{V}(I||s)\) and relate \((\overline{m}_{i},m^{\prime}_{i})\) with the \(enc_{SA}(\overline{m},m^{\prime})\). Assuming the public key cryptosystem of the user and the SA is IND-CPA secure this is not possible. Messages must be shuffled before exported. A linear computation of incoming messages results to linkability. Signatures on random strings can be inserted to further decrease probability. **Case 3**. : \((e\leftrightarrow f)\) Blindly signed messages sent by a vehicle and received by an RSU are untraceable by \(\mathcal{A}^{t}\), provided that the \(RSU\
otin\)Corrupted. Proof. : Straightforward since the encryption scheme of the RSUs is IND-CPA secure. **Lemma 1**. : _By combining Cases 1, 2 and 3 is is easy to see that the adversary \(\mathcal{A}^{t}\) will fail, if at least one of the entities \(CA,SA\) and RSU is honest._
## 4 Forecasters Development  
### Development of the Analytical Model  
The following section presents the development of the analytical model. As mentioned above, the open-source software OpenModelicahas been used to implement it. The libraries used were PVSystems and PhotoVoltaics [32]. Figure 8 shows the ESTIA1 building PV installation as a block diagram. The model has the classical components of a PV system; a DC/DC converter, the PV panels, an MPPT tracker, and a voltage source representing the grid side.  
Figure 8: Block diagram representation of ESTIA1 building PV installation.  
In order to characterize the PV cells, the most common equivalent circuit has been used, which is the single diode model, shown in Figure 9. The cells are gathered together to create PV modules, which are then connected in series or in parallel to form a PV installation. From the equivalent circuit, the following mathematical equations are obtained, which characterize the PV cell:  
\[i=I_{ph}-I_{d}-I_{r}, \tag{11}\]  
\[I_{ph}=\frac{(I_{SC}+K_{i}\cdot(T-T_{n}))\cdot\mathbb{G}}{G_{n}}, \tag{12}\]  
\[I_{d}=I_{0}\cdot\left\{\exp\left(\frac{v-R_{s}\cdot i}{\alpha\cdot V_{t}} \right)-1\right\}, \tag{13}\]  
\[I_{r}=\frac{v-R_{s}\cdot i}{R_{sh}}, \tag{14}\]  
\[V_{t}=\frac{N_{S}\cdot k\cdot T}{q}, \tag{15}\]  
\[I_{0}=\frac{I_{SC}+K_{i}\cdot(T-T_{n})}{\exp\left(\frac{V_{oc}+K_{i}\cdot(T-T_ {n})}{\alpha\cdot V_{t}}\right)-1}, \tag{16}\]  
where, \(I\) and \(V\) are the current and voltage, \(I_{ph}\) addresses the cell photo-generated current, \(I_{d}\) correspond to the diode current, \(I_{r}\) is the resistance current, \(V_{t}\) equals to the thermal voltage, \(I_{sc}\) and \(V_{oc}\) are the short circuit current and open circuit voltage, respectively, \(N_{s}\) are the cells connected in series, \(k\) is the Boltzmann's constant, \(q\) is the electronic charge, \(K_{i}\) and \(K_{v}\)are the \(I_{sc}\) and \(V_{oc}\) temperature coefficient respectively, \(\alpha\) is the diode ideality constant, and finally, \(R_{s}\) and \(R_{sl}\) are the equivalent series resistance and equivalent parallel resistance, respectively.  
Most of the parameters that appear in Equations (11)-(16) are provided by the PV panels supplier in the datasheet, however, some of them are rarely facilitated. This is the case of, \(I_{ph}\), \(I_{0}\), \(R_{s}\), \(R_{sh}\) and \(\alpha\). In order to obtain a model as accurate as possible, the steps proposed in work [33] have been followed, in which a simple method to extract the parameters of the single diode model of a PV system is developed.  
Finally, this model uses the measured historical data of solar irradiance, ambient temperature, and wind speed to obtain the results.
## 4 Materials and Methods
### Microparticles Entrapment Efficiency (EE) and Cinnamaldehyde Release Studies  
The entrapment efficiency of CIN in the PLGA MPs was measured by reverse- phase (RP)-HPLC. A sample of 1 mg lyophilized MPs was dissolved in 1 mL of acetonitrile and water (95:5) and was filtered, using a 0.22 \(\upmu\)m filter, before HPLC analysis. A C-18 reverse-phase column (4.6 mm \(\times\) 250 mm, 5 \(\upmu\)m) was used. The mobile phase consisted of acetonitrile: water: acetic acid in a ratio of 50: 50: 0.01, with a flow isocratic rate of 300 \(\upmu\)L/min and a total run time of 40 min per sample. Analysis wavelength was 295 nm.  
MPs loading was calculated according to Equation (2):  
\[\text{loading}=\text{CIN}\in 1\text{mg~{}di~{}MPs}\times\text{mg~{} lyophilized~{}MPs} \tag{2}\]  
Then, the entrapment efficiency (EE) was calculated according to Equation (3):  
\[\text{EE}\left(\%\right)=\frac{\text{loading}}{\text{initial~{}CIN~{}amount }}\times 100 \tag{3}\]  
All experiments were performed in triplicate.  
For in vitro release studies 2 mg of lyophilized CIN-MPs were dispersed in 2 mL of ethanol under magnetic stirring (700 rpm). At fixed time intervals (1, 4 and 24 h), the sample was centrifuged at 11,000 rpm for 15 min and 1 mL of the supernatant was withdrawn and replaced with 1 mL of fresh ethanol. The supernatant was filtered with a 0.22 \(\upmu\)m filter and the released CIN was measured by reverse- phase (RP)-HPLC at the same conditions previously described. The release study in fungal cell culture medium (Sensititre YeastOne Broth, USA), both in presence and in absence of _Candida_ spp., was performed with the same procedure utilized in ethanol.
# Demosaicing by Differentiable Deep Restoration
## 4 Experiments
### Reconstruction from Noise-Free Data
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Demosaic** & \multicolumn{2}{c}{**Kodak**} & \multicolumn{2}{c}{**McMaster**} & \multicolumn{2}{c}{**vdp**} & \multicolumn{2}{c}{**moire**} \\ (**Bayer CFA**) & \multicolumn{2}{c}{**(24 images)**} & \multicolumn{2}{c}{**(18 images)**} & \multicolumn{2}{c}{**(1000 images)**} & \multicolumn{2}{c}{**(1000 images)**} \\ \hline  & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM \\ \hline Bilinear & 0.88 & 0.88 & 0.922 & 0.806 & 0.806 & 0.820 & 0.820 \\ [PERSON] et al. [51] & 0.954 & 0.954 & 0.861 & 0.804 & 0.886 & 0.883 \\ [PERSON] et al. [35] & 0.988 & 0.988 & 0.969 & 0.985 & 0.985 & 0.985 & 0.930 \\ [PERSON] et al. [9] & 0.987 & 0.987 & 0.981 & 0.971 & 0.973 & 0.973 & 0.960 \\ [PERSON] et al. [36] & 0.988 & 0.988 & 0.982 & 0.982 & 0.980 & 0.984 & 0.983 \\ [PERSON] et al. [37] & 0.988 & 0.989 & 0.981 & 0.980 & 0.980 & 0.989 & 0.983 \\ [PERSON] et al. [3] & 0.988 & 0.988 & 0.988 & 0.982 & 0.981 & 0.973 & 0.983 \\ [PERSON] et al. [40] & 0.988 & 0.989 & 0.987 & 0.971 & 0.986 & 0.986 & 0.986 \\ [PERSON] et al. [32] & 0.986 & 0.986 & 0.987 & 0.984 & 0.984 & 0.982 & 0.986 \\ \hline
**Our 2 \(\times\) 2 Bayer** & 0.989 & 0.988 & 0.972 & 0.972 & 0.981 & 0.977 & 0.985 \\ \hline
**(Non-Bayer CFA)** & \multicolumn{2}{c}{**Kodak**} & \multicolumn{2}{c}{**McMaster**} & \multicolumn{2}{c}{**vdp**} & \multicolumn{2}{c}{**moire**} \\ \hline  & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM \\ \hline [PERSON] et al. [50] & 0.987 & 0.962 & 0.988 & 0.921 & 0.807 & 0.807 & 0.910 \\ [PERSON] et al. [52] & 0.986 & 0.986 & 0.988 & 0.931 & 0.984 & 0.984 & 0.982 \\ [PERSON] et al. [21] & 0.987 & 0.988 & 0.988 & 0.981 & 0.988 & 0.984 & 0.982 \\ [PERSON] et al. [22] & 0.988 & 0.988 & 0.988 & 0.988 & 0.988 & 0.988 & 0.988 \\ [PERSON] et al. [20] & 0.988 & 0.988 & 0.988 & 0.988 & 0.988 & 0.988 & 0.988 \\ [PERSON] et al. [49] & 0.988 & 0.988 & 0.988 & 0.988 & 0.988 & 0.988 & 0.988 \\ \hline
**(Joint-Learned CFA)** & \multicolumn{2}{c}{**Kodak**} & \multicolumn{2}{c}{**McMaster**} & \multicolumn{2}{c}{**vdp**} & \multicolumn{2}{c}{**moire**} \\ \hline  & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM \\ \hline [PERSON] et al. [2] & 0.988 & 0.920 & 0.988 & 0.988 & 0.988 & 0.988 & 0.988 \\ [PERSON] et al. [3] & 0.988 & 0.991 & 0.988 & 0.976 & 0.977 & 0.977 & 0.981 \\ \hline
**Our Learned 4 \(\times\) 4** & \multicolumn{2}{c}{**3.87**} & \multicolumn{2}{c}{**0.992**} & \multicolumn{2}{c}{**10.96**} & \multicolumn{2}{c}{**0.976**} & \multicolumn{2}{c}{**50.1**} & \multicolumn{2}{c}{**0.981**} & \multicolumn{2}{c}{**50.967**} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PSNR (peak signal-to-noise ratio) (dB) and SSIM (structural similarity) results on noise-free data. The comparison methods are evaluated on the Kodak, McMaster, vdp, moiré datasets.
## 5 Performance Study
### Simulation Results
#### 5.3.1 End-to-end PDR Gain in a Simple Network Configuration  
In this subsection, we measure by simulation the end-to-end PDR gain when one relay node is placed at the mid-point position between the stationary source and destination nodes. Simulation setups presented in Section 5.2 are used except for network topology and node mobility. Both interference and non-interference cases are considered. The load of the source is set to 900 Kbps. For the case with interference, another source-destination pair, which has two intermediate nodes on the path, is placed in the neighborhood.  
Figure 6 shows the end-to-end PDR gains as the distance between source and destination nodes varies. As shown in Figure 6, the gain increases as the distance grows. In particular, the gain sharply grows when the distance becomes longer then 350m. The results in Figure 6 also indicate that the gain without interference is higher than that with interference.  
Note that the values of the gain in Figure 6 differ from those in Figure 4, since a different physical layer model is used. Although their absolute values are different, results from the simulations and the analytical model have a similar pattern, which fully verifies the motivation of our proposed algorithm.
# Spatial Monitoring of Wafer Map Defect Data Based on 2D Wavelet Spectrum Analysis
## 2 Theoretical Background  
### 2D Discrete Wavelet Transform  
2D discrete wavelet transform (DWT) is a transform method for constructing blocks with discretely sampled wavelets. 2D DWT is well known as a powerful tool for real-time image analysis due to its computational efficiency. It converts the data of a single image into multi-resolution sub-band areas, which help self-similarity analysis. The decomposed output contains a set of basis functions called wavelets, which can be localized in the time and frequency domains and reconstructed through inverse DWT. The converted function consists of translations and dilations of univariate scale function \(\phi_{j,k}(\mathbf{t})=2^{2j}\phi\Big{(}2^{j}\mathbf{t}-\mathbf{k}\Big{)}\) and wavelet functions \(\psi^{i}_{j,k}(\mathbf{t})=2^{2j}\psi^{i}\Big{(}2^{j}\mathbf{t}-\mathbf{k}\Big{)}\), for two-dimensional timelines \(\mathbf{t}=(t_{1},t_{2})\in\mathbb{R}\) and location factors \(\mathbf{k}=(k_{1},k_{2})\in\mathbb{Z}\). Here, \(i\) has three directions: diagonal (HH; High-High sub band), horizontal (HL; High-Low sub band), vertical (LH; Low-High sub band), and \(j\) denotes the resolution level. Based on scale and wavelet functions, the two-dimensional image is transformed as:  
\[f(\mathbf{t})=\sum_{\mathbf{k}}c_{L,\mathbf{k}}\phi_{L,\mathbf{k}}(t)+\sum_{j\in L}\sum_{\mathbf{ k}}\sum_{\mathbf{i}}d^{i}_{j,\mathbf{k}}\psi^{i}_{j,\mathbf{k}}(\mathbf{t}), \tag{1}\]  
where \(j=L,\ldots,J-1\), \(\mathbf{k}\equiv(k_{1},k_{2})=0,\ldots,2^{j}-1\). \(L\) and \(J\) are the lowest number of the resolution level and the highest number of the resolution level, respectively. Through the hierarchical calculation, the coarser coefficients \(c_{j,\mathbf{k}}\) (also called approximation coefficients) and the detailed coefficients \(d_{j,\mathbf{k}}\) can be obtained. Each of them is defined as:  
\[c_{j,\mathbf{k}}=\int_{\mathbb{R}}f(\mathbf{t})\phi_{j,\mathbf{k}}(\mathbf{t})\;d\mathbf{t},\text {and }d^{i}_{j,\mathbf{k}}=\int_{\mathbb{R}}f(\mathbf{t})\psi^{i}_{j,\mathbf{k}}(\mathbf{t})\;d\mathbf{t}. \tag{2}\]  
Approximation coefficients capture the smooth and general information in the low frequency area, while the detailed coefficients characterize the detailed and oscillatory pattern in the high frequency region. Detail coefficients provide the vibrating behaviors of original image signals; thus, all the de-noising and feature extraction techniques are conducted based on the detailed coefficients.
## 5 Discussion of Sustainable Development Mechanism
### Technology Power Development Mechanism  
The swift evolution of information technology has bestowed unparalleled opportunities for smart health and elderly care. Innovations such as the Internet of Things (IoT), artificial intelligence (AI), big data, cloud computing, and 5G have found extensive applications in elderly care services, catalyzing the metamorphosis and enhancement of traditional elderly care services into intelligent elderly care services.  
The IoT technology is harnessed to achieve intelligent monitoring and management of the daily lives of the elderly. Through the utilization of wearable devices and sensor networks, real-time collection and analysis of the health data of elderly individuals can be achieved. For instance, smart wearable devices can monitor heart rate, blood pressure, sleep quality, and physical activity, providing invaluable data for health assessment and early detection of potential health risks.  
AI technology is leveraged to deliver personalized care and assistance. AI-powered virtual assistants can engage with the elderly, prompt them regarding medication schedules, offer entertainment, and even extend companionship to assuage loneliness. Big data and cloud computing are deployed to institute comprehensive databases and platforms for the storage and analysis of elderly health records, fostering the sharing of medical resources and optimizing service allocation. The advent of 5G technology engenders high-speed, low-latency communication, facilitating real-time remote consultations, telemedicine, and interactive experiences, markedly enhancing the efficiency and caliber of healthcare services for the elderly [44].  
In summation, the sustainable development mechanism of smart health and elderly care demonstration bases embodies a holistic approach that amalgamates market forces, governmental support, and technological innovation to guarantee the enduring viability and efficacy of elderly care services [45]. This mechanism serves as a template for propelling the sustainable progression of the elderly care industry in the epoch of smart technology, contributing to the well-being and quality of life of the elderly populace [46].
# A Middleware-Based Approach for Multi-Scale Mobility Simulation
## 5 Experiments and Results
### Hypotheses  
One of the objectives of the middleware is to harmonize the behaviors of simulations. More precisely, it allows for \"correcting\" the behavior of a simulation, based on another (valid) one. Four hypotheses are tested in the experiments.  
**Hypothesis 1** (Impact of the middleware).: _The use of the middleware allows for the behavioral convergence of two mobility simulations._  
This is the first justification for using a middleware. We would expect that the difference in travel times or speeds are limited when using the middleware.  
**Hypothesis 2** (Impact of the difference in simulations' behavior).: _The use of the middleware is more positive when the simulations have a big behavioral difference._  
We would expect the middleware to have a bigger impact with simulations that have very different output (when used in isolation).  
**Hypothesis 3** (Impact of the scheduling).: _In case of inter-dependency, bidirectional corrections via the middleware provides better results than unidirectional corrections._  
Not all simulation platforms allow for an external scheduling (by the middleware) allowing notably to execute several times (but with different inputs) the same time step (instructions (6) in Algorithms 1-3). If external scheduling is possible, we expect the middleware to have a higher impact. If this hypothesis is valid, it would be a strong argument for the simulation platforms to invest in the development of the right interfaces to allow an external scheduling.  
**Hypothesis 4** (Computational cost of the use of the middleware).: _The use of the middleware comes with a reasonable computational cost._  
Interactive synchronized simulations come surely with a cost. However, we expect this cost to remain reasonable, i.e., not very far from \(\max(\Theta_{\mathcal{S}1},\Theta_{\mathcal{S}2})\), with \(\Theta_{\mathcal{S}1}\) (resp. \(\Theta_{\mathcal{S}2}\)) the simulation time of \(\mathcal{S}_{1}\) (resp. \(\mathcal{S}_{2}\)).
## 4 Discussion
### Application of BCG/SCG in Medical Treatment and Health Care
#### 4.3.2 Subjectivity and Objectivity  
The main part of BCG/SCG in the diagnostic monitoring in medical treatment is the signal itself, which has certain subjectivity. The research of its application in the medical field mainly focuses on the correlation between waveform characteristics and heart activities to draw monitoring conclusions. The signal itself and the analysis results are strongly causal. Therefore, BCG/SCG signal can be used to monitor and feedback human heart functional status in real time, and make decisions on the outcome of changes in the body when signal abnormalities occur. In [PERSON] et al. [44]'s work, features of BCG signal were related to cardiovascular activities by mathematical modelling. In a normal BCG signal (see Figure 1), the time interval between the beginning of the I wave and peak of the J wave may represent the aortic pulse transit time while the ratio of the amplitude of the J-K down-stroke to the amplitude of the J wave may indicate PP (aortic pulse pressure, PP = systolic BP \(-\) diastolic BP) amplification. Both of them are powerful predictor of cardiovascular risk [94].  
The application of BCG/SCG in health management is more objective, requiring long-term monitoring and evaluation of human macroscopic conditions, so as to make timely prediction when the health level declines. Although the basis of data analysis in the field of health management is still signal, with the accumulation of a large number of data, the judgment indicators of human health functions will gradually become regular and identical, because of high comprehensiveness of the data analysis results, even if one of the parameters has an error, the overall judgment will not be affected. Through multi-parameter and comprehensive analysis, balance or imbalance of human health status can be judged, and the occurrence of diseases can be predicted prospectively, thus timely intervention, prevention and control of diseases can be achieved in the early stage.
## 6 Analysis of Three-Junction Scenarios  
The model for this scenario is shown in Figure 11; the parameters for the traffic lights were the same for each junction. In the previous section, we considered Junction 1 and Junction 2 connected via road and link and showed how we calculated the delay in such a system. In this section, we will extend the work to consider a three-junction scenario and, hence, we introduce Junction 3 as well as the second link between Junction 2 and Junction 3 shown in Figure 12.  
Therefore, we now calculate the delay on the road between Junction 2 and Junction 3. VMAX Speed = 40 km/h.  
L(Road) = 2500 m between Junction 2 and Junction 3.  
Figure 11: Three-junction scenario.  
Figure 12: Three junctions with two links scenario.  
Figure 10: Link of a junction.  
The value of T(Road) is given as  
\[T(Road)=L(Road)/(VMAXSpeed*3600) \tag{10}\]  
T(Road) = 225 s.  
Therefore, the service rate per vehicle \(\mu\) is given by  
\[\mu=1/T(Road)=1/225 \tag{11}\]  
Let \(K\) be the capacity of the road link in terms of the number of vehicles that fit on the road.  
\[K=L(Road)/L(VEH) \tag{12}\]  
The value of \(K\) = 625.  
The arrival rate of the vehicles going from Junction 2 to Junction 3 is given by  
\[\lambda=1/(T Junction1+TSYS(Link1)+T Junction2)=0.0146 \tag{13}\]  
Using the algorithm to calculate the delay on the road, we obtain the following:  
Average number of vehicles on the road = 3.288;  
Average time to traverse the road = 225.225 s;  
Average response time for the system = 0.3603 s;  
Blocking probability = 0.
## 2 Materials and Methods
### Universal GNSS Channel Validation
#### 2.2.4 Modulations Compatibility  
For compatibility sake, GNSS signals are based on a few modulation types, all derived from PSK and BOC.
## 4 Conclusions  
In summary, perovskite films with various equimolar concentrations of MAI and PbI\({}_{2}\) were synthesized and analyzed. The (1.0:1.0), (1.2:1.2), and (1.6:1.6) films did not appear to be optimal and exhibited pinholes and cracks within the photobasorber, and thus these films were not suitable for the PV device fabrication of the solar cell stack. However, the (1.5:1.5) formulation exhibited a compact morphology which appeared more suitable as a photoabsorber in the PSCs. Accordingly, PSCs fabricated using the (1.5:1.5) films exhibited a PCE of \(\sim\)14.56% for the n-i-p structure. Operational stability analysis was conducted, and the continuous evolution of photovoltaic parameters with time up to 10 h was monitored while keeping the device at its maximum power point, which revealed that the (1.5:1.5) PSC retained \(\sim\)90% of its initial \(V_{\text{OC}}\) and \(FF\) after 10 h of continuous tracking, but the \(J_{\text{SC}}\) and PCE decreased to \(\sim\)70% and \(\sim\)60%, respectively, of their initial values during the same amount of time. These results provide a benchmark to further tune the materials synthesis and device fabrication efforts to form higher performance PSCs in the future.  
Figure 6: The light intensity-dependent \(J\)-\(V\) Characteristics of the n-i-p PSCs: (**a**) \(J_{\text{SC}}\) versus light intensity, and (**b**) \(V_{\text{OC}}\) versus light intensity, where the light intensity was varied from 0.1 sun to 1 sun.  
Figure 7: (**a**) The stabilized current output of PSC devices at 0.6 V for 3 min, and (**b**) the evolution of PV parameters with respect to time when the (1.5:1.5) PSC device was held at its maximum power point.  
**Author Contributions:** Conceptualization, A.B.K. and M.P.; Experimental Methodology, M.P.; Results Analysis and Discussion, M.P. and A.B.K.; writing--original draft preparation, M.P.; writing--review and editing, A.B.K. and M.P.; supervision, A.B.K.; project administration, A.B.K.; funding acquisition, A.B.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Office of Naval Research (ONR) (Grant No. N00014-20-1-2597) and the PACCAR Technology Institute at the University of North Texas.  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** We are grateful to [PERSON] from UNT for technical assistance and useful discussions. We thank the Office of Naval Research (grant number ONR N00014-20-1-2597) that enabled us to pursue this work. A.B.K. is also grateful for the support from the PACCAR Technology Institute at UNT and the Endowed Professorship.  
**Conflicts of Interest:** The authors declare no conflict of interest.