esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminary Remarks  
The one-dimensional left-side quaternion discrete Fourier transform may be defined using the following expression:  
\[y_{m}=\sum_{n=1}^{N-1}\exp(\ -\ \mu\frac{2\pi}{N}mn)x_{n},\ m=0,1,...,N-1, \tag{1}\]  
where  
\(x_{n}=x_{n}^{(r)}+x_{n}^{(i)}i+x_{n}^{(j)}j+x_{n}^{(k)}k\ n\)-th quaternion-valued sample of discrete-time signal with \(N\) samples, \(x_{n}\in H\), \(x_{n}^{(r)},x_{n}^{(i)},x_{n}^{(j)},x_{n}^{(k)}\in R\);  
\(y_{m}=y_{m}^{(r)}+y_{m}^{(i)}i+y_{m}^{(j)}j+y_{m}^{(k)}k\), is \(m\)-th quaternion-valued QDFT coefficient, \(y_{m}\in H\), \(y_{m}^{(r)},y_{m}^{(i)},y_{m}^{(j)},y_{m}^{(k)}\in R\), \(\mu=\mu^{(i)}i+\mu^{(j)}j+\mu^{(k)}k\) is a unit pure quaternion, \(\mu\in H\), \(\mu^{(i)},\mu^{(j)}\), \(\mu^{(k)}\in R\).  
Most often, it is assumed that \(\mu^{(i)}=\mu^{(j)}=\mu^{(k)}=1/\sqrt{3}\), and we will assume so as well.  
In matrix notation, expression (1) can be rewritten as follows:  
\[\mathbf{y}_{N}=\mathbf{E}_{N}\mathbf{x}_{N}, \tag{2}\]  
where  
\(\mathbf{x}_{N}=\left[x_{0},x_{1},...,x_{N-1}\right]^{T}\) is a vector of size \(N\) containing quaternion-valued signal samples, \(\mathbf{y}_{N}=\left[y_{0},y_{1},...,y_{N-1}\right]^{T}\) is a vector of size \(N\) containing the QDFT coefficients,  
\(\mathbf{E}_{N}\) is a matrix whose entries \(w^{mn}\) represent the values of quaternion-valued discrete exponential functions, and \(w^{mn}=\exp(-\mu\frac{2\pi mn}{N})\), \(\exp(-\mu\frac{2\pi mn}{N})=\cos\frac{2\pi mn}{N}-\mu\sin\frac{2\pi mn}{N}\). It is easy to see that these values are quaternion-valued constants. Therefore, if the order of the matrix \(\mathbf{E}_{N}\) is known in advance, then its entries can be calculated in advance.  
It is the matrix representation of the left-side QDFT for which we will need to synthesize fast left-side QDFT algorithms. By analyzing the structures of matrices and finding successful tricks for their decomposition and factorization, it is possible to develop QDFT algorithms with reduced computational complexity. Let us show how we do it.
## Appendix C CNN-LSTM Neural Network  
Long short-term memory (LSTM), a kind of recurrent neural network, can learn this time feature very well through connecting the information of the last moment to the current learning process. Convolutional neural network (CNN) is a type of feed-forward neural network, including the input layer, convolutional layer, activation function, pooling layer and full connection layer. Its artificial neurons can respond to a part of the surrounding units in the coverage area to extract local features. The process is shown in the figure. In order to describe the process of convolution operation more clearly, we number the read-in data elements. \(Xi,j\) represents the elements of row \(i\), column \(j\). _Wm,n_ represents the weight of row \(m\), column \(n\). \(Wb\) represents the offset term of filter. \(f\) represents the activation function. We use this formula to extract the characteristics of time series:  
\[a_{i,j}=f(\sum\limits_{m=0}^{2}\sum\limits_{n=0}^{2}W_{m,n}X_{i+m,j+n}+W_{b})\]  
The model structure and specific parameters are shown below.  
1. Convolution layer: the convolution kernel size is \(3\times 3\), the number of convolution kernels in the first layer of 3D Convolution (Conv3D) is 100, the number of convolution kernels in the second layer of Conv3D is 50, and the number of convolution kernels in the third layer of Conv3D is 5.
2. Pool layer: the pool area size of the first layer Max pooling is \(3\times 3\times 3\), and the pool area size of the second layer is \(2\times 3\times 3\).
3. LSTM layer: the number of neurons in the first layer of LSTM is 100, and that in the second layer is 50.
4. FC layer: the number of neurons is 50.
5. Activation function: sigmoid function.
6. RMSPROP is selected as the optimizer, the initial learning rate is 0.001, the number of iterations is 64, and the batch size is 32.  
As shown in the Figure A2, \(x_{t}\) represents the input data at the current time, \(y_{t-1}\) stands for the output data at the previous time, C represents the cell state of the stored information flow, and \(\sigma\) represents the function \(\sigma(\mathrm{x})\ =\ \frac{1}{1+\mathrm{e}^{-\mathrm{x}}}\), which outputs the result of 0-1, and here represents the amount of information allowed to pass. For each moment, the input data at this time and the output data at the previous time \(\{x_{t};y_{t-1}\}\) constitute the total input, which together with the final cell state at the previous time determines the current learning results.  
Figure A3: CNN-LSTM hybrid model structure.  
Batch normalization (BN) can accelerate the convergence of the model. The Max Pooling layer extracts the maximum value of the fixed area to represent the information of the area through downsampling, so as to reduce the data dimension and calculation consumption.
# Mini-LED and Micro-LED: Promising Candidates for the Next Generation Display Technology
## 3 Micro-LED
### Color Conversion Full-Color Display  
The full-color display can be achieved by employing excitation sources, such as ultraviolet (UV) micro-LED or blue micro-LED, with color-conversion materials. RGB color-conversion materials are needed to achieve RGB three primary colors if UV micro-LEDs are used, while only red and green color-conversion materials are required if blue micro-LEDs are used (Figure 11). Generally, color-conversion materials can be divided into the phosphor and the quantum dots (QDs).
# Improving DFT-Based Image Watermarking Using Particle Swarm Optimization Algorithm
## 2 Background and Contributions
The range of VIF values is \([0,1]\), where the closer value to \(1\) represents the better fidelity with respect to the original image;
* To improve the robustness, we consider a set of geometric and signal processing attacks in the fitness function of the PSO algorithm, each one with aggressive image distortion;
* Instead of performing the imperceptibility and robustness testing using the classic images such as Lena, Baboon, etc., the proposed method is tested on 1000 color images extracted from COCO dataset [30], which contains images of complex everyday scenes with common objects in their natural context;
* Imperceptibility testing is performed using PSNR and VIF metrics;
* Robustness testing is performed considering a wide range of geometric, common signal processing and combined distortions;
* Detailed performance comparison with the conventional 2D DFT-based invisible watermarking is performed to show the improvements in terms of imperceptibility and robustness when the PSO optimization is adopted.
## 3 Results and Discussion
### Differences in Output and Use of Tiers 1 and 2 to Assess FFU and Determine WQRs
#### 3.2.1 FFU Evaluations for Tiers 1 and 2  
For the Tier 1 FFU evaluation (Table 4), IrigWQ reports a single calculated profile salinity value. This value is displayed in the results column of the corresponding FFU category (in this case, the Ideal category, coloured blue). Because the calculated profile salinity value is reported (and not only the FFU category), information is also conveyed to the user about how close the predicted profile salinity is to the boundary of the FFU category, thereby providing some indication of how close the predicted value is from falling into the next FFU category.
# Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application to Face Recognition
## 6 Application to Face Recognition
### Experimental Setup  
In the following, we describe the database, the employed face recognition system, and metrics used to evaluate the biometric performance:  
* **Database**: for the evaluation, we use the publicly available database _HDA Facial Tattoo and Painting Database_[[https://dasec.h-da.de/research/biometrics/hda-facial-tattoo-and-painting-database](https://dasec.h-da.de/research/biometrics/hda-facial-tattoo-and-painting-database)]([https://dasec.h-da.de/research/biometrics/hda-facial-tattoo-and-painting-database](https://dasec.h-da.de/research/biometrics/hda-facial-tattoo-and-painting-database)) (accessed on 13 December 2022), which consists of 250 image pairs of individuals with and without real facial tattoos. The database was originally collected by [PERSON] et al. in [5]. The images have all been aligned using the RetinaFace facial detector [59]. Examples of original image pairs (before tattoo removal) are given in Figure 16. These pairs of images are used for evaluating the performance of a face recognition system. For evaluating the effect of tattoo removal, the models described in Section 5.1 are employed on the facial images containing tattoos, whereafter the resulting images are used during the evaluation.
* **Face recognition system**: to evaluate the applicability of tattoo removal for face recognition, we use the established ArcFace pre-trained model (LResNet100E-IR,ArcFace@ms1m-refine-v2) with the RetinaFace facial detector.
* **Recognition performance metrics**: the effect of removing facial tattoos is evaluated empirically [60]. Specifically, we measure the FNMR at operationally relevant thresholds corresponding to a FMR of 0.1% and 1%:
* False Match Rate (FMR): the proportion of the completed biometric non-mated comparison trials that result in a false match.
* False Non-Match Rate (FNMR): the proportion of the completed biometric mated comparison trials that result in a false non-match. Additionally, we report the Equal Error Rate (EER), i.e., the point where FNMR and FMR are equal. To show the distribution of comparison scores, boxplots are used. The comparison scores are computed between pairs of feature vectors using the Euclidean distance. A subset of semi-controlled images from the FRGCv2 dataset are used to obtain non-mated comparison scores during the experiments. These scores are used during the experimental evaluating for calculating the operating points corresponding to a FMR of 0.1% and 1% and together with mated scores, obtained on the tattoo database, for computing the EER before and after tattoo removal.
# Quantum Stream Cipher Based on Holevo-Yuen Theory
## 4 Survey of the Mathematical Security Analysis
### Randomization Technology  
In the early days when Y-00 was invented, the model used was the so-called basic model, and it just explained the principle. In order to achieve sufficient quantitative security, the randomization technique described here is necessary. In the criteria of cryptography by [PERSON], such as Equations (12) and (13), the Y-00 scheme has a potential to have excellent quantitative security by additional randomization technology.  
In this point of view, we have developed a new concept such as \"quantum noise diffusion technology\" [13; 14]. In addition, several randomizations based on [PERSON]'s idea [3] have been discussed [21]. Using these techniques, it is expected to have security against known-plaintext attacks on key that cannot be achieved by a conventional cipher, as follows:  
\[H(X_{n}\mid C_{n}^{E},K_{s})\
eq 0 \tag{16}\]  
for certain finite \(n=n_{2}>|K_{s}|\) under the condition Equation (10). This means that one cannot pin-down the data under the finite length of ciphertext with error even if the secret key is provided to the attacker after they have received the Y-00 signals by their instruments [19; 20]. This comes from the fact that the ciphertext for attacker is not correct ciphertext. This is called advantage creation based on receivers with key and without key.  
This is an amazing capability, and this cannot be achieved even with \"One Time Pad Cipher\". However, as the pointed out in the above, these security of abilities are limited to \"**finite**\" \(n_{1}\), and \(n_{2}\) in principle, and these depend on the randomization technique. The general quantitative evaluation for the concrete randomization is still an open question. In this way, we can say that the Y-00 quantum stream cipher has the ability to provide security that exceeds the performance of conventional cryptography while maintaining the capabilities of ordinary optical communication. To date, there have been several criticisms of the security of the Y-00 principle, but one can see that they all turn out to be based on misunderstandings of the structure and claim of the Y-00 principle.
# New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach
## 4 Discussion
### Can the Long-Period-Gap-Filled Flux Data Capture the Interannual Variability?
Our results suggests that the performance of short-, mid-, or long-term training  
Figure 8: Interannual variations of GPP **(a)**, RE **(b)**, NEE **(c)**, and ET **(d)** for the study site over the entire study period from the observations (Obs), SVR-based model trained using all available data except for the target year (SVRall), and SVR-based model using two years of data around the target year (SVR2 years). The numbers in parentheses represent the slope (bold) and \(r^{2}\) (italic) from the linear regression of the observations. might vary from site to site, and thus further scrutinization of the ideal training length should be inferred from a multi-site study, or needs to be evaluated for each site separately. In accordance with the results and discussion, we estimated the daily fluxes in 2007 and 2014 using the SVR2\({}_{\text{years}}\) model trained by in situ measurement data for long-period-gap-filling of the HFK site, and then replaced the original gap-filled data from the marginal distribution sampling method with the estimated data using SVR2\({}_{\text{years}}\) when the daily data retrieval rate of 0% lasted more than a month (Figure 10). The RMSEs of the SVR2\({}_{\text{years}}\) in 2007 and 2014 quantified using the available measured data were 0.839 g C m\({}^{-2}\) day\({}^{-1}\) for GPP, 0.503 g C m\({}^{-2}\) day\({}^{-1}\) for RE, 0.745 g C m\({}^{-2}\) day\({}^{-1}\) for NEE, and 0.392 mm day\({}^{-1}\) for ET, which were comparable to those from Experiment 3-6. The final annual sums with uncertainty related to the long-period flux-gap-filling (2\(\sigma\), quantified using the Monte Carlo approach) for 2007 (2014) were 1347 \(\pm\) 58 (1174 \(\pm\) 62) g C m\({}^{-2}\) year\({}^{-1}\) for GPP, 1129 \(\pm\) 40 (1146 \(\pm\) 51) g C m\({}^{-2}\) year\({}^{-1}\) for RE, \(-\)217 \(\pm\) 60 (\(-\)28 \(\pm\) 57) g C m\({}^{-2}\) year\({}^{-1}\) for NEE, and 587 \(\pm\) 29 (639 \(\pm\) 29) mm year\({}^{-1}\) for ET. Since the mean bias errors of the SVR2\({}_{\text{years}}\) in 2007 and 2014 were close to 0, the annual sums were almost identical whether we included the measured data or not. Despite the long gaps of 143-164 days, the uncertainties except for NEE were less than 5% of the annual sums. Figure 9: Same as Figure 8 (GPP (**a**), RE (**b**), NEE (**c**), and ET (**d**)), but for the Gwangneung deciduous forest in the Korea National Arboretum (GDK) site from 2006 to 2015.
## 3 Experimental Results
### Plastic Hinge Length  
The plastic hinge length for each SCMSW specimen is presented in Table 4. The plastic hinge lengths were calculated based on the recommendation of [PERSON] et al. [21] and [PERSON] [22]. According to these recommendations, the yielded length (\(L_{\text{yield}}\)) is taken equal to the height above the foundation at which the curvature is equal to the yield curvature, as shown in Figure 12. Then, the plastic hinge length (\(L_{p}\)) is assumed equal 0.5 \(L_{\text{yield}}\). The resulting plastic hinge lengths are shown in Table 4 for each SCMSW specimen. Average values of the plastic hinge lengths fall between values from 12.65% to 28.85% of the wall length. [PERSON] et al. [21] and [PERSON] et al. [20] found ratios of plastic hinge length to wall plan length falling between values from 17% to 37% and 10% to 41%, respectively. The plastic hinge lengths were not significantly affected by the amount of the flexural reinforcement and the amount of the axial load in SCMSW specimens. On the other hand, when the aspect ratio (height to length) increased, the plastic hinge length increased.
## 3 Background
### Security Requirements
#### 3.3.5 Nonrepudiation  
Nonrepudiation not only detects compromised nodes but also prevents the sender or receiver from denying the transmitted message. Coordination and cooperation among users of an IoT network within a particular range are crucial. For instance, information on an emergency or an accident should be communicated promptly to identify the person responsible. As such, if a user denies a sent message, the user can jeopardize the lives of other users.
## 5 Modularization Case Study Based on the DIIS
### Design of the Generic Case Study
#### 5.2.3 Model Development  
Once the problem has been defined, modelers must start generating assumptions as well as defining the standard values that define the models. These provide the basis for the model behavior and how the research question arose. First, assumptions are defined:  
* Distribution of finished products as well as the procurement of raw material is given;
* Each order has a production unit;
* Bill of materials (BOM) is not considered.  
Moreover, the following items are considered to enable the comparison between the simulation models:  
* Same demand, use of demand replicas;
* Same number of employees and capabilities;
* Same production logic for all simulation models;
* The warehouses without stock capacity limitations;
* Transport between the different production stages has no limitations;
* There are two products. One existing product is in its mature stage and has a stable demand and with a price of USD 10,000/unit. The new model is in the process of being launched with a price of USD 20,000/unit. These values were used to calculate sales. If there is loss in volume, it is assumed that the new product in development will have the loss in volume due to unknown future demand;
* The simulation model considers a volume loss when the customer order lead time is greater than 60 days;
* Time restrictions: 10 working years, i.e., 500 weeks, as the model attempts to evaluate the impacts in the medium and long term;
## 3 Results
### The Analysis of the Principal Component Numbers  
In this study, 1-4 PCs were selected for the analysis of SIF retrieval, and the results are shown in Figure 2. According to Figure 2, the PCs were inevitably affecting the accuracy of SIF inversion. As the previous literature has illustrated, the global spatial SIF maps from OCO-2 products show that SIF values had a range from \(-4\) mW\({}^{-2}\)sr\({}^{-1}\)nm\({}^{-1}\) to 4 mW\({}^{-2}\)sr\({}^{-1}\)nm\({}^{-1}\)[40]. Furthermore, when probing the moist tropical or subtropical forests, the researchers found out that even the lowest SIF value was larger than 0.2 mW\({}^{-2}\)sr\({}^{-1}\)nm\({}^{-1}\)[32]. Thus, when inverted from one or two PCs, the SIF intensity values easily fell into invalidity, showing either too many negative values (Figure 2a) or extremely miniscule values (Figure 2b). Another obtrusive interference was that in this approach, as shown in Equation (5), the SIF values were derived partly by \(\lambda^{i}\), which means that the SIF values were anomalously high when considering a higher polynomial order (magnitude of four or higher), which are illustrated in Figure 2d. Above all, three PCs would be apropos for PCA-based SIF retrieval. In comparison, the spectral band regions would have a greater and analytical influence on the results of SIF inversion.
## 2 Patients, Materials, and Methods
### Materials and Methods  
#### 2.2.1 Predictors  
Assessment of Depressive Symptoms  
The severity of the patient's depressive symptoms at baseline (preoperative assessment) was determined by the Beck Depression Inventory (BDI-II) test [51]. Since all the participants spoke Croatian, validated Croatian versions of BDI-II were used [52]. Both questionnaires were fulfilled by each patient included in the study with the supervision and assistance of the physician when deemed necessary.  
The BDI-II depression inventory test represents a 21-question multiple-choice self-report inventory, in which the patients were asked how they felt over the last two weeks [51]. Each answer to the BDI-II test ranges from 1 to 3, and the total ratings were calculated as follows: a score of 0-13 indicates patients with minimal depressive symptoms, 14-19 indicates those with mild depressive symptoms, and a score of 20-28 indicates patients with moderately expressed depressive symptoms, while a 29-63 total rating score designates CABG patients with high depressive symptoms [51]. A binary variable generated according to accepted cut-off values divides patients into a non-depressive symptom group with a BDI-II score ranging from 0 to 13 and a mildly to a severely depressed group with a BDI-II score > 13.  
Baseline and Postoperative C-Reactive Protein Levels  
The inflammatory activity of CABG patients included in the present study was monitored by measuring CRP values in the patient's peripheral circulation. Baseline CRP values were determined preoperatively upon admission to the Department of Cardiac Surgery. The CRP values were measured on the first, second, third, fourth, fifth, and sixth postoperative days in the postoperative course. To obtain the samples, 5 mL of peripheral blood from each patient was drawn into plasma separator tubes by vacuum puncture from the forearm at baseline and postoperatively, as designated. Blood was subsequently centrifuged for 10 min at 3700 rpm (revolutions per minute), and the resulting plasma was placed into Eppendorf tubes and frozen at \(-80\)\({}^{\circ}\)C until further analysis. CRP measurement was performed using a commercial immunodutbridimetry assay (Tina-quant(r) C-Reactive Protein Gen.3, Roche Diagnostics, Rotkreuz, Switzerland ) in the Roche Cobas c 501 modules (Roche Diagnostics Rotkreuz, Switzerland,) following the manufacturer's guidelines. Overall, two summary scores of postoperative CRP values were generated. The first summary score used the mean for the CRP values measured on days 1, 2, and 3 postoperatively, while the second CRP summary score used the mean for the CRP values measured on days 4, 5, and 6 postoperatively. The postoperative CRP responses to CABG surgery were calculated as differential scores by subtracting the baseline CRP value from the mean postoperative CRP scores and termed as \"early\" and \"persistent\", as previously described [48].
# An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers
## 2 GNSS Attitude Determination
### Direct Attitude Determination Method  
The baseline vector is parametrized as \(b_{1}=\left(\begin{array}{cccc}e_{1}&n_{1}&u_{1}\end{array}\right)\) and \(b_{2}=\left(\begin{array}{cccc}e_{2}&n_{2}&u_{2}\end{array}\right)\) in the local East-North-Up and Right-Front-Up body frame, respectively. The multiple antenna is set up in Figure 1. The primary baseline \(b_{1}\) is parallel to the vehicle moving direction, whereas the secondary baseline \(b_{2}\) has a rotation angle \(\alpha\) with respect to \(b_{1}\). Then, the yaw, pitch, and roll angle can be computed from the baseline coordinates, as follows:  
\[\begin{split}\psi&=-\text{arctan}\Big{(}\frac{e_{1}}{n_{1}} \Big{)}\\ \theta&=\text{arctan}\Big{(}\frac{u_{1}}{\sqrt{e_{1}^{2}+n_{1}^{2}}} \Big{)}\\ \gamma&=-\text{arctan}\Big{(}\frac{-\sin\theta\sin\psi_{2}-\sin \theta\cos\psi_{2}+\cos\theta u_{2}}{\cos\psi_{2}-\sin\psi_{2}}\Big{)}\end{split} \tag{13}\]  
where \(\psi\) is heading, \(\theta\) is pitch, and \(\gamma\) is roll.  
In this paper, the three-antenna GNSS multi-baseline model is adopted. No redundant observation is considered. The direct method is particularly applicable in this study because of its simple form and small computing burden.