esa-sceva/satcom-chunk-collection huggingface dataset:
# Tripartite Quantum Key Distribution Implemented with Imperfect Sources
## Appendix A Estimation of Decoy State Parameters
The lower bound of \(C\) for single-photon states can be obtained in terms of error rates from measurements in the complementary bases as \[ \begin{array}{ll}\mathrm{C}_{1}^{L}&=(1-2e_{X_{X}X_{B}X_{C}}^{1,U})^{2}+(1-2e_{X _{X}X_{B}Y_{C}}^{1,U})^{2}+(1-2e_{X_{X}X_{B}X_{C}}^{1,U})^{2}+(1-2e_{Y_{A}X_{B}X _{C}}^{1,U})^{2}+(1-2e_{Y_{A}X_{B}X_{C}}^{1,U})^{2}+(1-2e_{Y_{A}X_{B}X_{C}}^{1,U}) ^{2}\\ &+(1-2e_{X_{A}Y_{B}X_{C}}^{1,U})^{2}+(1-2e_{Y_{A}X_{B}Y_{C}}^{1,U})^{2}.\end{array}  \tag{120}\]  
The upper bounds on error rates \(e_{F_{A}I_{B}I_{C}}\) with \(\Gamma\in\{X,Y\}\) obtained from the signals produced using the decoy intensity are provided by  
\[ \begin{array}{ll}E_{F_{A}I_{B}I_{C}}^{v}Q_{F_{A}I_{B}I_{C}}^{v}=\frac{1}{2} \left(\frac{1}{\Gamma+v}\right)Y_{0}+e_{F_{A}I_{B}I_{C}}^{1}\frac{v}{(1+v)^{2} }Y_{1}+\mathbb{E}_{\lambda\geq 2}e_{F_{A}I_{B}I_{C}}^{k}\frac{v^{k}}{(1+v)^{k+1} }Y_{k};\\ \hskip 113.811024 pte_{F_{A}I_{B}I_{C}}^{1,U}\leq\frac{E_{F_{A}I_{B}I_{C}}^{v}Q_ {F_{A}I_{B}I_{C}}^{v}-\omega_{0}Y_{0}(1+v)^{-1}}{Y_{1,L}\frac{v}{(1+v)^{2}}}, \end{array}  \tag{121}\]  
where the overall quantum bit error rate, \(E_{F_{A}I_{B}I_{C}}^{v}\), is described as follows:  
\[E_{F_{A}I_{B}I_{C}}^{v}=\frac{Y_{0_{A};1_{F}1_{F}}^{v}+Y_{1_{A}0;0_{F}}^{v}+Y_ {0_{A}0;0_{F}1_{F}}^{v}+Y_{0_{A};1_{F}0_{F}}^{v}+Y_{1_{A};0_{F}1_{F}}^{v}+Y_{ 1_{A};1_{F}0_{F}}^{v}}{Q_{F_{A}I_{B}I_{C}}^{v}}. \tag{122}\]  
Finally, the upper bound of \(I_{E}\) based on \(e_{Z_{A}Z_{B}Z_{C}}^{1,U}\) and \(C_{1}^{L}\) is determined using the following equations:  
\[I_{E}^{U}=(1-e_{Z_{A}Z_{B}Z_{C}}^{1,U})h\left(\frac{1+u_{\max}}{2}\right)-e_{Z _{A}Z_{B}Z_{C}}^{1,U}h\left(\frac{1+v(u_{\max})}{2}\right)+e_{Z_{A}Z_{B}Z_{C}} ^{1,U}\log 2, \tag{123}\]  
where  
\[ \begin{array}{ll}u_{\max}=\min\left[\frac{1}{1-e_{Z_{A}Z_{B}Z_{C}}^{1,U}} \sqrt{C_{1}^{L}/4},1\right],\\ \hskip 113.811024 ptv(u_{\max})=\sqrt{\frac{49}{19}\left[C_{1}^{L}/4-(1-e_{Z_{A }Z_{B}Z_{C}}^{1,U})^{2}u_{\max}^{2}\right]}/e_{Z_{A}Z_{B}Z_{C}}^{1,U}.\end{array}  \tag{124}\]
# Comparative Transcriptomics for Pepper (_Capsicum annuum_ L.) under Cold Stress and after Rewarming
## 3 Results
### Response of Key Unigenes to Photosynthesis, Plant Hormone Signal Transduction, and DDR Processes
ing _PYL_, _PP2C_, _SnRK2_, and _ABF_ increased significantly under T4 group, and the expression levels of _PYL_ (Capana00g003406 and Capana03g002049), _ABF_ (Capana04g000551), and _SnRK2_ (Capana02g003135) in CT were significantly higher than those in CS. In addition, jasmonate ZIM domain-containing protein (_JAZ_) (Capana03g000117, _Capana09g000144_, and _Capana07g000750_) was upregulated at T3 and T4 groups. Interestingly, under T3 and T4 groups, the expression level of _JAZ_ in CS was significantly higher than that in CT. This suggests that the expression of _JAZ_ may be more sensitive to cold stress. These pathways--including DNA replication, HR, and MMR--were significantly enriched, which belonged to DDR. Replication protein a (_RPA_) (_Capana03g000868_ and _Capana00g004882_) and DNA polymerase delta (_POL_ \(\delta\)) (_Capana06g002135_) were involved in the above three pathways (Figure 6). Compared with CS, most DEGs were significantly upregulated in T2 and T3 groups of CT; these DEGs showed lower expressions in T1 and T4 groups of CT, while the expressions in CS were higher than those in CT. It indicated that a large number of genes were upregulated during DNA damage repair after CT was subjected to cold stress, thus conducting DNA repair of cold stress injuries. Figure 5: DEGs involved in plant hormone signal transduction pathway. DEGs shared in auxin, abscisic acid (ABA), and jasmonic acid (JA) signal and comparison of their expression patterns in CT and CS. The log2 FC values were used for mapping. Red color represents high gene expressions; green color represents low gene expressions.
# A Novel Approach for Fraud Detection in Blockchain-Based Healthcare Networks Using Machine Learning
## 3 Proposed System
### Architectural Layers of BC-Based Healthcare System  
A blockchain-based smart healthcare system typically consists of several layers [23], each of which serves a distinct purpose as follows:  
1. Sensor layer: The sensor layer in a smart healthcare system refers to the layer of sensors and devices that collect health data from patients. This layer is critical for enabling real-time monitoring of patient health and providing personalized care;
2. Application layer: This layer contains the applications and interfaces that allow users to interact with the system. This includes applications for patients, healthcare providers, and other stakeholders;
3. Blockchain layer: The blockchain layer in a smart healthcare system refers to the layer of distributed ledger technology that underpins the system. This layer provides a secure and transparent way to store and share healthcare data, while ensuring privacy and confidentiality;
4. Access layer: In a smart healthcare system, the access layer refers to the layer of the system that provides access to various components and services of the system, such as sensors, devices, networks, databases, and applications. The access layer plays a critical role in enabling users to interact with the system, retrieve and analyze data, and control various aspects of the system.
# Convolutional Neural Networks for Off-Line Writer Identification Based on Simple Graphemes
## 4 Experiments with Convolutional Neural Networks
### Comparison with Other Approaches  
Table 12 shows the results obtained in different works regarding writer verification on the repository of simple graphemes. The upper part of the table concentrates other approaches, and the lower part presents the results of this paper.  
In Reference [13] a descriptor called Relative Position of the Minimum Gray Level Points (RPofMGLP) is proposed. The final descriptor is a vector whose elements correspond to the euclidean distance between the lower-gray value line and the considered reference edge. Said distance is measured over the perpendicular line that joins the point of the skeleton to the appropriate edge.  
In Reference [14] a descriptor is proposed that corresponds to the coefficients of the B-Spline transformation of the signal of the descriptor RPofMGLP (BSC-RPofMGLP).  
In Reference [15] various descriptors are proposed to represent the simple grapheme. The first one corresponds to the gray level of the morphological skeleton points (GLofSK). It assumes that there is not a significant variation in the gray level perpendicularly to the skeleton. The second one corresponds to the Average Gray Level of the Skeleton Perpendicular Line (AGLofSPL), which attempts to represent the horizontal and vertical variability of the gray levels with respect to the skeleton. The third one corresponds to the width of the grapheme, which was measured using the lines perpendicular to the skeleton (WofGra). Finally, it proposes the Local Binary Patterns of the grapheme surface (LBPofGra).  
In Reference [16] the LBPofGra descriptor is considered but building classifiers based on Single Layer Extreme Learning Machine (ELM) networks and on Multiple Layer Extreme Learning Machine (ML-ELM).  
Table 12 reinforces the idea that simple graphemes have enough biometric information for the writer verification. The best descriptors from other works are AGLofSPL [15] and LBPofGra [15], both with an average performance of 98%. Processing the Original Graphemes through CNN gives a performance of 97% for the case of VGG-16. The CNN-based approach allows to obtain performance similar to the best results of other works but substantially simplifying the Simple Grapheme processing line.
# A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks
## 3 Implementation
### Performance Benchmark  
Our proposed approach (Section 3.2), referred to as the proposed method hereafter, was compared to three representative approaches, namely a Thompson sampling baseline, a Random baseline, and \(\varepsilon\)-greedy approaches to solving the caching problem. Thompson Sampling: In this method, the selection of content to cache on satellites is determined probabilistically using a Bayesian model. Each piece of content is chosen to be cached on a satellite by sampling from the posterior distribution, which is updated based on the historical hit rate and other relevant data. Content items are cached sequentially, with each decision influenced by the feedback obtained from previous cache hit outcomes.  
1. Random algorithm: In this approach, the selection of content to cache on satellites is performed randomly without considering past performance or user preferences. Each piece of content is randomly assigned to a satellite for caching without any probabilistic model guiding the decision-making process. Content items are cached in no particular order, with each allocation being independent of previous cache hit outcomes or historical data.
2. \(\varepsilon\)-greedy algorithm: In this approach, the selection of content to cache on satellites combines both the exploration and exploitation strategies. With a probability of \(\varepsilon\), a random content item is chosen for caching, allowing for the exploration of new content and satellite combinations. With the remaining \(1-\varepsilon\) of the time, the algorithm exploits the known information by selecting the content with the highest estimated cache hit rate for caching on a satellite. This method balances between exploring new content possibilities and exploiting the currently known best options.  
3. CAFR [33]: A cooperative caching scheme for edge computing that leverages FL and DRL to predict popular content and optimize caching strategies, this approach aims to improve cache hit ratios and reduce content transmission delays. It is currently the most outstanding method in the field of edge caching.  
All experiments were implemented in Python 3.9.2 and conducted on a Windows machine equipped with an Intel Core i9-12900K processor (16 CPUs, 3.2 GHz), 32 GB of RAM, and an RTX3070 graphics card.
# Combining Optimization and Simulation for Designing a Robust Short-Sea Feeder Network
## 5 Computational Study
### Determining Global Estimated Optimal Routes
#### 5.3.3 Analyzing the Best Deterministic Solution
For both seasons, arrival times are mostly within relatively small intervals, particularly for the main route and daughter route 1. The longer daughter route 2 is slightly more prone to variations in arrival time, especially during the winter season, although this route is considerably shortened during  
Figure 11: A box and whisker plot of the arrival times for the summer season of the Combined-strategy solution. Figure 12: A box and whisker plot of the arrival times for the winter season of the Combined-strategy solution. this season (see Figure 10). Still, these varying arrival times do not affect synchronization with the mother vessel at ocean hubs (except Bergen) because daughter vessel 2 always arrives first anyway (with one exception). Figure 13 presents the arrival times for the deterministic solution. Here, we observe much wider intervals for different arrival times. For example, about 50% of all arrivals at the main continental port in Maasvlakte happen approximately 48 h around the median arrival time. This median arrival time of the mother vessel is also about 100 h after the end of the planned two-week route due to a large number of duration violations. Moreover, Figure 13 shows that the deterministic solution is very sensitive to weather uncertainty, causing arrival times to vary a lot. Therefore, the deterministic solution is not applicable in a real-world setting and should be discarded in favor of the solution from the Combined-strategy. These results highlight once more the need for considering the weather uncertainty in designing the short-sea feeder network considered in this paper.
## 2 Materials and Methods
### Shack-Hartmann Technique
\begin{cases}R_{n}^{l}(\rho)\cdot\cos(l\cdot\theta) \text{ for }l\leq 0\\ R_{n}^{l}(\rho)\cdot\sin(l\cdot\theta)\text{ for }l>0\\ \\ R_{n}^{l}(\rho)=R_{n}^{n-2m}(\rho)=\sum_{s=0}^{m}(-1)^{s}\cdot\frac{(n-s)!}{s! \cdot(m-s)!(n-m-s)!}\rho^{n-2s}\end{cases} , \tag{6}\]  
Thus, the wavefront \(W\) can be presented in a form of Zernike polynomial expansion:  
\[W=\sum_{i=0}^{N}a_{i}\cdot Z_{i}(x,y)=\sum_{m=0}^{k}\sum_{m=0}^{n}a_{nm}\cdot Z _{n}^{l}(\rho,\theta), \tag{7}\]  
where \(a_{nm}\)--Zernike coefficient that represents the aberration value,  
\(N\)--number of the Zernike polynomials used, \(l=n-2m\). Knowing the analytical representation of polynomials, we can calculate the partial derivatives \(\partial W/\partial x\) and \(\partial W/\partial y\) of a wavefront \(W\):  
\[ \begin{cases}\frac{\partial W(x,y)}{\partial x}\\ \frac{\partial W(x,y)}{\partial y}\end{cases} = \begin{cases}\sum\limits_{i}^{ N}a_{i}\cdot\frac{\partial Z_{i}(x,y)}{\partial x}\\ \sum\limits_{i}^{N}a_{i}\cdot\frac{\partial Z_{i}(x,y)}{\partial y}\end{cases} . \tag{8}\]  
Thus, the wavefront partial derivatives \(\partial W/\partial x\) and \(\partial W/\partial y\) can be analytically defined using Zernike polynomials (Equation (8)). However, they can also be calculated using the measured displacements \(S_{x}\) and \(S_{y}\) of focal spots on the Shack-Hartmann sensor:  
\[ \begin{cases}\frac{\partial W(x,y)}{\partial x}\\ \frac{\partial W(x,y)}{\partial y}\end{cases} = \begin{cases}\sum\limits_{i}^{ N}a_{i}\cdot\frac{\partial Z_{i}(x,y)}{\partial x}\\ \sum\limits_{i}^{N}a_{i}\cdot\frac{\partial Z_{i}(x,y)}{\partial y}\end{cases}  =\frac{1}{f} \begin{cases}S_{x}\\ S_{y}\end{cases} . \tag{9}\]  
Figure 2: (**a**) Set of focal spots (focal spot field, hartmannogram) formed by a lenslet array at the sensor plane of a Shack–Hartmann sensor; (**b**) Principle of calculation of wavefront derivatives by means of measurements of focal-spot displacement. Image adapted from [35]. Finally, we determine the overdetermined system of linear equations with the unknown coefficients \(a_{i}\). Solving this least square problem [74], we obtain the coefficients \(a_{i}\). From here on, the wavefront can be analytically described and analyzed.
# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms  
A localization system can classify the positioning and variable aspects, which involve signal strength, propagation time, received angle, ranging and devices. The position estimation techniques can be applied to define location coordinates. The traditional positioning algorithms are proximity and triangulation. The fingerprinting method, PDR method, and hybrid methods are also adopted to estimate the user position in the indoor system. There are several positioning algorithms described in the previous works. The positioning algorithms are illustrated in Figure 8.  
Among these methods, the most popular were presented. As the present review is related to the positioning algorithms or approaches that detect the optimal positioning accuracy of a target within indoor environments, the localization technologies in Section 2 and parameter-based measurement methods in Section 3 were combined with these algorithms at that point for finding the position and the direction of a target object.
## 4 Technological Innovations
### Augmented Reality  
Augmented reality displays are significantly enhancing situational awareness in vehicles [40]. AR-based head-up displays (HUDs) project critical information such as navigation and hazard alerts directly onto the windshield, allowing drivers to keep their eyes on the road [41]. It should be noted that HUDs with different sizes of fields of view evoke similar driving performance, cognitive load, and user experience [42], so size does not have any significant effect on the driver's situational awareness. Originally developed for military aircraft, HUDs were introduced into the automotive domain in the late 1980s by General Motors, becoming widely available in the early 2000s (BMW 2003, Audi 2010, Mercedes 2014). An illustration of Mercedes-Benz's augmented reality-based HUD is shown in Figure 6.  
Figure 6: Augmented reality-based HUD projecting critical driving information, such as navigation directions and speed, directly onto the windshield (Mercedes Benz, available online: [[https://www.mercedes-benz-mena.com](https://www.mercedes-benz-mena.com)]([https://www.mercedes-benz-mena.com](https://www.mercedes-benz-mena.com)) (accessed on 26 January 2025)).  
Mercedes-Benz's MBUX system (Mercedes-Benz User Experience) is the latest infotainment system, which overlays AR graphics onto real-world imagery to guide drivers in complex intersections [43]. It is a prime example of how AR can improve navigation. A picture of Mercedes-Benz's MBUX system, which leverages AR for enhanced navigation, is shown in Figure 7. AR displays reduce cognitive load by allowing drivers to access key information without need to divert attention from the road, thus improving overall driving safety [9].  
AR displays enhance both multimodality, by merging real-world and digital content, and safety--by reducing the need for drivers to shift attention away from the road, as indicated in Figure 4.
## 3 Materials and Methods
### Word Representation
#### 3.1.2 Semantic Role Labeling Information  
Explicit semantic information is obtained from SRL. We adopt the most commonly used PropBank-style annotator, which considers one single sentence as a unit to analyze the related local semantic structure of each predicate in the sentence. Semantic structure information is very related to named entity recognition and relation extraction tasks. The role information such as _agent_, _theme_, _time_ and _location_ can help model extract entities. The relation information between predicates and other words can improve relation extraction to a certain extent.  
Figure 2 shows an example for semantic role labeing. SRL centers on the predicate to mark the relationship between other words and predicates in the sentence. In this example, _ARG1_ represents the theme, _ArgM-TMP_ is an adjunct indicating the timing of the action, \(O\) represents non-argument word, and \(V\) represents the predicate.  
SRL is predicate-centric to assign labels to words in sentences. Since the concerned predicates are different, the resulting semantic label sequences are different. In order to express the semantic structure of sentences as much as possible, we select five semantic label sequences for each sentence and vectorize them, respectively. Their aggregated representations are then concatenated with the sequence of word vectors from BERT to obtain the final word vector representation.  
One semantic role label sequence is represented as:  
\[\mathbf{T}_{1}=(\mathbf{t}_{1}^{1},\mathbf{t}_{2}^{1},\ldots,\mathbf{t}_{n}^{1}) \tag{4}\]  
Figure 2: An example for Semantic Role Labeling.  
where \(\mathbf{t}_{1}^{1}\), \(\mathbf{t}_{2}^{1}\),..., \(\mathbf{t}_{1}^{n}\) are labels of the first one semantic role label sequence.  
Five semantic label sequences are aggregated through a full connections layer:  
\[\mathbf{T}_{s}=\mathbf{W}_{2}(\mathbf{T}_{1},\mathbf{T}_{2},\ldots,\mathbf{T}_{5})+\mathbf{b}_{2} \tag{5}\]  
where \(\mathbf{W}_{2}\) and \(\mathbf{b}_{2}\) are trainable vectors.  
The final word vector is represented as:  
\[\mathbf{X}^{w}=[\mathbf{X}^{b};\mathbf{T}_{s}] \tag{6}\]  
where \([.]\) is vector concatenation across row.
# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
## 3 Proposed Method
### Gaze Result Evaluation  
During the model's output process, the features extracted from both the eye stream and the fusion stream are first subjected to global average pooling, reducing the feature map size to \(1\times 1\), followed by processing through a fully connected layer. The fully connected features are then concatenated into a comprehensive feature vector, which is subsequently passed through two fully connected layers to predict the two-dimensional gaze direction vector. In this process, the channel dimension of the eye stream is reduced from 1024 to 256, while the channel dimension of the fusion stream remains unchanged. Finally, a specific angular computation method is used to convert the output two-dimensional vector into a three-dimensional gaze direction. This transformation process is implemented using the following angular computation function:  
\[gaze=\begin{pmatrix}x=-\cos\varnothing\cdot\sin\theta\\ y=-\sin\varnothing\\ z=-\cos\varnothing\cdot\cos\varnothing\end{pmatrix} \tag{8}\]  
where \(x\) represents the horizontal component, \(y\) represents the vertical component, and \(z\) represents the depth component. \(\varnothing\) is the yaw angle predicted from the 2D vector, and \(\theta\) is the pitch angle predicted from the 2D vector. Through the above transformation formula, the conversion from a 2D vector to a 3D gaze direction is completed.  
The angular error between the predicted and ground truth values is calculated using the cosine similarity to determine the angle between them. The specific formula is as follows:  
\[\theta=\arccos\left(min\left(\frac{g\cdot l}{\|g\|\cdot\|l\|},0.9999999\right) \right).\frac{180}{\pi} \tag{9}\]  
where \(g\) represents the predicted value obtained through angle conversion, and \(l\) is the ground truth label. The function \(\min(\cdot,0.9999999)\) is used to prevent precision issues caused by floating-point operations, ensuring that the input to the \(arccos\) function remains within the valid range \([-1,1]\).
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Method** & **RMSE** & **Ref.** & **Method** & **Accuracy** \\ \hline \multirow{3}{*}{[7]} & SVM model & 0.0521 & \multirow{3}{*}{[11]} & CNN (Greyscale Images Training Set) & 0.987 \\  & SVM Markov-model & 0.0091 & & CNN (Greyscale Images Validation Set) & 0.997 \\ \hline \multirow{3}{*}{[8]} & Bayesian & 80.55–91.67\% & \multirow{3}{*}{[11]} & CNN (Greyscale Images Test Set) & 0.989 \\  & NB & 71.67–91.11\% & \multirow{3}{*}{[13]} & CNN (Greyscale Images Validation Set) & 0.989 \\  & MLP & 87.22–96.11\% & \multirow{3}{*}{[11]} & **Ref.** & **Method** & **Accuracy** \\ \cline{3-5}  & & Weighted k-NN & 91.67–96.11\% & \multirow{3}{*}{[11]} & \multirow{3}{*}{[11]} \\  & J48 & 89.44–90.55\% & \multirow{3}{*}{[13]} & \multirow{3}{*}{[11]} & \multirow{3}{*}{[12]} \\  & Random Forest & & 90.56–95.55\% & \multirow{3}{*}{[13]} \\ \hline
**Ref.** & **Method** & & **Accuracy** \\ \hline \multirow{3}{*}{[9]} & Constant torque load & SMO & 80–100\% \\  & & ASMO & 81–100\% \\  & & AFSMO & 84–100\% \\  & & SMO & 75–100\% \\  & Variable torque load & ASMO & 81–100\% \\  & & AFSMO & 75–100\% \\ \hline
**Ref.** & **Method** & & **Accuracy** \\ \hline \multirow{3}{*}{[10]} & FS1 + SVM & 75.00\% \& 73.57\% \\  & FS2 + SVM & 92.50\% \& 78.81\% \\ \cline{1-1}  & LiftingNet & 99.63\% \& 93.19\% \\ \hline \end{tabular}
\end{table} 
Table 13: Results comparison. Figure 12: Summary of results for the ML and DL models. This work evaluated the applicability of physical methods, such as the CPW-enhanced envelope spectrum, for the ML-based classification problem. Feature extraction from this method has yielded excellent results demonstrating its usefulness in combination with time-domain features. These results have been superior to those of the CNN trained with greyscale images of raw time signals under the same conditions. The results obtained in this paper are to be understood as a complement to the study of relevant features for diagnosing REBs based on ML.