esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Dual Signature with Coherent States Based on Chained Phase-Controlled Operations
## 1 Introduction
A dual signature [42; 43; 44], which combines the customer's order information and payment information that are encrypted with separate secret keys, can be used to solve the problem that the customer's order information (payment information) should be hidden from the bank (business) while the payment information should be blindly forwarded to the bank by the business. It is a new way to sign on transaction information and plays an important role in e-payment system to ensure the secure communication among the consumer, merchant, and bank. The dual signature has a good development in classical cryptography, and a quantum dual signature scheme [44] was introduced based on coherent states with entanglement swapping in 2016. Compared to the classical dual signatures, the quantum dual signature which involves quantum algorithms and QKD protocols could be more secure and efficient, and it also has a wide application to the ecommerce system as a classical dual signature. In 2103, [PERSON] et al. [45] presented two types of improved encryption algorithms which are called Key-Controlled-I' QOTP and Key-Controlled-T' QOTP determined by the shared key and can prevent forgery attacks effectively. In 2015, [PERSON] et al. [46] proposed a novel chained CNOT operations encryption where one qubit is encrypted not only based on the corresponding qubit and key but also based on other qubits and keys, which is more secure compared with QOTP. Therefore, inspired by the chained CNOT operations and classical dual signature, we design a new encryption algorithm for signatures, i.e., the chained phase-controlled operation (CPCO) to construct a quantum dual signature scheme. Meanwhile, coherent states that can be easily generated [47; 48] by current quantum technologies are used as the input resources. Strong analysis we present proves that the scheme can resist the attacks on forging sender's signature, disavowing own signature, denying the received signature, and also the [PERSON]'s attack in Ref. [2]. Simulation experiments of the CPCOs are performed on quantum softwareplatform, namely Strawberry Fields [49], and the experimental results declares that the designed encryption algorithm can be implemented by quantum devices. It is worth mentioning that a secure quantum signature protocol must meet the following security conditions [8]: (1) Non-forgery: The signature cannot be forged by any attacker. Only the legitimate signer can generate a valid signature. (2) Non-repudiation: Any participant cannot deny what she/he has done. That is to say, the signer cannot later successfully disavow that she/he has signed a message and the receiver cannot deny her/his receiving or the integrity of the signature. (3) Quantum properties: Quantum signature algorithms contain pure quantum mechanics properties, and there are no classical counterparts. In this paper, we mainly focus on these security conditions to verify the feasibility of our scheme.
# Anomaly Detection and Identification in Satellite Telemetry Data Based on Pseudo-Period
## 2 Pseudo-Periodic Extraction of Satellite Telemetry Data
### Pseudo-Period Extraction of Telemetry Data Based on Symbolization Method
By setting the DTW distance detection threshold, the rationality of the preliminary partition point of the pseudo-period was determined. If the distance was less than the set threshold, the right boundary of the data corresponding to the subsequence was used as the pseudo-periodic partition point. If the distance was greater than the set threshold, the correction was performed within \(k\) characters of the subsequence, and the DTW distance of \(STR_{fT}\) and each modified character sequence were calculated separately. If the distance was less than the correction threshold, the right boundary of the data corresponding to the minimum correction character was taken as the partition point. If the distance was still greater than the correction threshold, the segment was retained as the anomaly partition point, and the right boundary of the data corresponding to the minimum correction character was still taken as the partition point. The locations of the pseudo-periodic partition points were extracted as follows:  
\[\left\{ \begin{array}{c}pr_{i}=X_{m}[STR_{i}],D\big{(}STR_{i},STR_{fT}\big{)} \leq th_{S}\\ pr_{i}=X_{m}[STR_{i}],D\big{(}STR_{i},STR_{fT}\big{)}\leq h*th_{S}\\ pw_{i}=X_{m}[STR_{i}],D\big{(}STR_{i},STR_{fT}\big{)}>h*th_{S}\\ STR_{i}=\min\Big{(}D\big{(}\{STR_{i}\pm k\},STR_{fT}\big{)}\Big{)}\end{array} \right. \tag{10}\]  
where \(pr_{i}\) is the location of the pseudo-period partition point in normal data, \(pw_{i}\) is the location of the pseudo-period partition point in the anomaly data, \(D\big{(}STR_{i},STR_{fT}\big{)}\) is the DTW distance of the subsequence \(STR_{i}\) and \(STR_{fT}\), and \(k\) is the character offset correction range. \(th_{S}\) is set at the DTW detection threshold, and \(h\) is the amplification coefficient of the DTW detection threshold after the character offset correction; usually \(h\in[1,2]\). Pseudo-periodic partition was performed for the characterizing result of the data in Figure 1. Given the sequence of the standard pseudo-periodic [**eeeeedcdd**], the pseudo-periodicity was divided into:  
The corresponding pseudo-period extraction points were: [64 135 222 299 385 464 506 579 663 743 821 901 983]. According to the results of the pseudo-period extraction, DTW could effectively extract the pseudo-period of the data. Sequence segmentation using the keywords of the standard pseudo-periodic character sequence could avoid the high complexity of the DTW distance calculation.
## 5 Experimental Simulation and Analysis
### SLP-MACGT Comparison with Other SLP Schemes
#### 5.3.4 Energy Consumption  
Figure 11a illustrates the comparison of node energy consumption among different methods, showcasing that the energy consumption of SLP-MACGT remains at an intermediate level even when facing multiple attackers. The incorporation of network coding and multi-round games in SLP-MACGT does impose a slight burden on the nodes; however, the variance in energy consumption between SLP-MACGT and MRGSLP is not notably significant. In contrast, the utilization of SSLP results in the highest energy consumption by nodes due to the traversal of multiple AUVs across all clusters and the energy requirements for periodic data relays. Conversely, PP-SLPP demonstrates relatively low energy consumption as several AUVs manage the majority of network functions, thereby consuming the bulk of the energy resources.  
Figure 11: Energy consumption: (**a**) network side length vs. energy consumption; (**b**) number of nodes vs. energy consumption.  
Figure 10: Packet delivery ratio: (**a**) network side length vs. packet delivery ratio; (**b**) number of nodes vs. packet delivery ratio.  
Furthermore, Figure 11b provides additional insights into the comparison of energy consumption across varying numbers of nodes. In SSLP and PP-SLPP, changes in the node count have minimal effects on energy consumption, as AUVs predominantly handle data transmission tasks. Conversely, in SLP-MACGT and MRGSLP, nodes are responsible for a significant portion of the network functionality, leading to heightened energy consumption during simulation scenarios. Notably, the adaptive coding strategy employed in SLP-MACGT enhances data transmission success rates, reduces the need for data retransmissions, and ultimately lowers overall energy consumption compared to MRGSLP.
# New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems
## 3 Signal Processing Techniques
### Empirical Mode Decomposition Approach  
The EMD method employs mechanical oscillators, each of which generally only vibrates with one frequency but does so differently in another weight and tension environment [36]. To find these functions intrinsically, an algorithm thus becomes necessary; an intrinsic mode function (IMF) is a function that meets both of the following two conditions:  
1. In the whole dataset, the sums at which maxima or minima occur must be equal; or if this is not true, then their difference can have at most one extremum.
2. For any point on that curve, its general height between two neighboring extrema and between two neighboring minima should always average to zero. Figure 6 shows a flowchart for performing averaging for EMD, which is called Ensemble EMD (EEMD).  
In the literature, the use of EMD for human activity recognition includes denoising purposes and extracting features based on the chosen IMF, such as the mean absolute average, root mean square, variance, and higher-order statistics. Figure 7 represents an example of denoising an EMG before using EEMD. Figure 8 illustrates that the third IMF is the most informative component. EMD is suited for decomposing nonlinear signals such as sEMG and IMU signals. Ensemble EMD (EEMD) is used to decompose nonstationary signals. It is a noise-assisted data processing technique that evaluates the ensemble average of IMF; however, its computational complexity makes it challenging to employ in real-time applications.  
Figure 6: Flow chart of applying EEMD.
# Radiofrequency Performance Analysis of Metal-Insulator-Graphene Diodes
## 3 Model-Based Performance Projection of MIG Rectifiers
### Power Detection
The tangential responsivity achieved at 1 GHz also stands among the highest values reported in previous studies. It is important to highlight that the input power range used to calculate the tangential responsivity varied among the different studies, which directly influenced the obtained values. For example, in [4], the input power range was from 0 mW to 3 mW, while in [25], a fixed power of \(-\)30 dBm was used. In [13; 16], similar \(\beta_{\mathrm{v}}\) values were reported at the same frequency, suggesting comparable input power ranges. In this work, a large range of 0 mW to 4 mW was employed, allowing for a more detailed evaluation of the detector's response. The tangential responsivity depends on the input power range, as the detector's behavior varies significantly across different operating regions. This explains why, even at similar frequencies, the reported values of \(\beta_{\mathrm{v}}\) can range from tens to hundreds of V/W. Furthermore, the impact of parasitics should be emphasized, as their removal led to a notable improvement in the \(\beta_{\mathrm{v}}\) values. This optimization increased \(\beta_{\mathrm{v}}\) by a factor of 1.75 times at 2.45 GHz and 1.21 times at 1.225 GHz, illustrating that reducing parasitic components is crucial for attaining higher tangential responsivity levels. This highlights \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & \(\mathbf{f_{\mathrm{ASYM}}}\) & \(\mathbf{f_{\mathrm{NL}}}\) & **\(\mathbf{f_{\mathrm{RES}}}\)** & \(\mathbf{\beta_{\mathrm{v}}}\) \\ \hline \multirow{3}{*}{this work} & \multirow{3}{*}{806.6 at 1 V} & \multirow{3}{*}{6.64 at 0.7 V} & \multirow{3}{*}{14.65 V\({}^{-1}\)} & \multirow{3}{*}{34.6 V/W at 2 GHz, 26.9 V/W at 2.45 GHz,} \\  & & & & 2.9 V/W at 4 GHz and 0.2 V/W at 6 GHz \\ \hline [4] & 270 at 1.1 V & 11 a 0.7 V & 12 V\({}^{-1}\) at 0.2 V & 2.8 V/W at 2.4 GHz and 2.2 V/W at 30.8 GHz \\ \hline [6] & 18 at 0.9 V & 3 at 0.3 V & 8 V\({}^{-1}\) at 0.1 V & - \\ \hline [13] & - & - & - & 42 V/W a 2.45 GHz \\ \hline [20] & 608 & - & 10 V\({}^{-1}\) & - \\ \hline [16] & - & - & 12.4 V\({}^{-1}\) & 43.6 V/W at 2.45 GHz \\ \hline [15] & - & - & 71 V\({}^{-1}\) & - \\ \hline [25] & Up to 525 & Up to 10 & Up to 13.5 V\({}^{-1}\) & 15 V/W at 60 GHz and 168 V/W at 2.5 GHz \\ \hline [26; 27] & above 400 & above 20 & - & - \\ \hline [28] & - & - & ** 10–20 \(\mu\)S/V & - \\ \hline \hline \end{tabular}  
* The values were modified according to Equation (1). ** Calculated as \(d^{2}1/dV^{2}\); not directly comparable with \(f_{RES}\).  
\end{table} 
Table 1: Comparison of figures of merit. Figure 10: Output voltage versus input RF power at different frequencies with (dashed lines) and without (solid lines) parasitic elements. the need to further optimize the design of MIG diodes to reduce parasitic influences and improve performance.
## 3 Energy-Efficient Optimal Power Allocation
### Energy-Efficient Optimal Power Allocation for Real-Time Applications
#### 3.2.2 Peak Transmit Power Constraint  
If the PTP and AIP constraints are considered for the satellite user, the EE maximization problem for real-time applications should be formulated as  
\[\begin{array}{l}\max_{P_{t}}H\left(P_{t}\right)=\frac{R_{th}\Sigma\left[1- \chi_{s}\right]}{\mathrm{E}\left(\xi P_{t}+P_{c}\right)},\\ s.t.\left\{\begin{array}{cc}P_{t}\leq P_{m}&(a2)\\ \mathrm{E}\left(P_{t}\right)\leq I_{th}&(b)\end{array}\right.\end{array} \tag{32}\]  
Similar to (26), we can solve (32) by introducing the equivalent parameter optimization problem based on the [PERSON]'s method, which is given as  
\[T\left(\eta\right)=\max_{P_{t}\in\mathbb{S}_{2}}R_{th}\mathrm{E}\left[1-\chi_ {s}\right]-\eta\mathrm{E}\left(\xi P_{t}+P_{c}\right). \tag{33}\]  
Then, we decompose (33) into multiple parallel subproblems with the same structure for all fading states, which can be represented as  
\[\max_{0\leq P_{t}\leq P_{m}}D\left(P_{t}\right)=-R_{th}\chi_{s}-\eta\xi P_{t}- \mu P_{i}. \tag{34}\]  
Let \(P_{t}^{*}\) denote the optimal transmit power. By addressing (34), we can obtain the following results.  
In the case of \(P_{th}>P_{m}\), where \(P_{th}\) is calculated by (30), since the required minimum transmit power to maintain \(R_{th}\) is larger than the maximum available transmit power, the satellite user is always in outage. Therefore, \(P_{t}^{*}=0\).  
In the case of \(P_{th}\leq P_{m}\), the maximum of \(D\left(P_{t}\right)\) is \(-R_{th}\) when \(P_{t}=0\) or \(-\left(\eta\xi+\mu G_{1}h_{1}\right)P_{th}\) when \(P_{t}=P_{th}\), which is the maximum depends on their relationship. If \(P_{th}>R_{th}/\left(\eta\xi+\mu G_{1}h_{1}\right)\), the required transmit power to maintain \(R_{th}\) is very large, and the satellite user would stop working to save the power, i.e. \(P_{t}^{*}=0\). Otherwise, the satellite user transmits with \(P_{t}^{*}=P_{th}\).  
Based on the above analysis, the optimal transmit power of the satellite user can be summarized as shown in Theorem 6.  
Theorem 6: _The energy-efficient optimal transmit power with PTP constraint for real-time applications is given as_  
\[P_{t}^{*}=\left\{\begin{array}{cc}0,&P_{th}>P_{m}\\ 0,&\frac{R_{th}}{\eta\xi+\mu G_{1}h_{1}}<P_{th}\leq P_{m}\\ P_{th},&P_{th}\leq P_{m},P_{th}\leq\frac{R_{th}}{\eta\xi+\mu G_{1}h_{1}}\end{array}\right.. \tag{35}\]Similarly, we can modify Algorithm 1 to solve (32), where \(P_{t}^{k}\) is calculated by (35) in each iteration and only one Lagrangian multiplier \(\mu\) need to be updated. The details are not given here for simplicity.
# Metric Embedding Learning on Multi-Directional Projections
## 3 Methodology
### Measuring Performance  
On defining the performance of the novel projection based embedding learning method, one-shot classification accuracy is applied. In comparison to k-NN (k-nearest neighbors) [56], one-shot classification is done by selecting a subset of all available samples, therefore, the scale is relative, and accuracy represents the separational capabilities.  
The method measuring one-shot classification accuracy is described in several papers [4; 57]. A brief informal summary of the algorithm for \(N\)-way classification is as follows:  
1. an anchor observation is randomly selected;
2. a true pair is selected from the same category;
3. \(N-1\) false pair samples are selected from other categories;
4. distances between the anchor and pairs are measured;
5. classification is marked correct, if the distance between the anchor and the true pair is minimal amongst other anchor-based distances;
6. steps 1-5 are repeated \(k\)-times, while correct classifications are counted;
7. \(N\)-way one-shot classification accuracy is given as \(\frac{\text{Correct classifications}}{K}\).  
Figure 5: Illustration of different types of triplets. Subfigure (**a**) shows an easy negative, where \(d(x_{a},x_{n})>d(x_{a},x_{p})+m\), resulting in zero loss. Subfigure (**b**) illustrates a semi-hard negative, as \(d(x_{a},x_{p})<d(x_{a},x_{n})<d(x_{a},x_{p})+m\), resulting in a positive loss. On subfigure (**c**), the so-called hard negative is visualized, where \(d(x_{a},x_{n})<d(x_{a},x_{p})\).  
In the experiments explained in the next section, both Siamese and Triplet structures are evaluated on different sets of data, with and without pretraining, and by applying semi-hard and hard negative mining for a given number of selected classes.
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 3 Methodology  
### Problem Formulation  
Given a labeled and class-imbalanced training dataset \(S_{tr}=\left\{\left(x_{i},y_{i}\right)\right\}_{i=1}^{n},\ y_{i}\in\left\{0,1\right\}\) and an unlabeled test dataset \(\left\{\left(x_{j},\ \right)\right\}_{j=1}^{m}\) having the same distribution as the training dataset, where \(x_{i},x_{j}\in R^{d}\), respectively, represent \(i\)-th instance and \(j\)-th instance (i.e., software module) having \(d\) features (i.e., metrics) in the training and test datasets, \(m\) and \(n\) denote the number of samples in the training and test datasets, \(y_{i}=1\) denotes the sample \(x_{i}\) that belongs to the positive class (aka. the defective proneness module), and \(y_{i}=0\) denotes the sample \(x_{i}\) belonging to the negative class (aka. the defect-free module).  
The objective of this study is to build a software defect proneness prediction model by using training data \(S_{tr}\) and then predict the labels (i.e., defect-prone or non-defect-prone) of unlabeled test samples.
## 4 Results
### Seismic Response
To show a clear picture of the dynamic response of the SFT under seismic loadings, the comparison of mid-span displacements and bending moments for hydrodynamic and seismic analysis (LC2) are shown in Figure 12. The hydrodynamic response is very small as compared to when the seismic load is considered. The comparison is shown only for one seismic scenario (LC2); due to that, both seismic load combinations (LC1, LC2) produce very close responses in comparison to hydrodynamic analysis. The hydrodynamic response of the SFT resembles the shape of the wave force model used, while the seismic loading effect the transient motion of the SFT. In addition, the transverse displacements (Figure 12a) and transverse bending moment (M\({}_{\mathrm{Z}}\), Figure 12b) are very large and significantly affected by seismic loading as compared to vertical displacements (Figure 12c) and vertical bending moment (M\({}_{\mathrm{Y}}\), Figure 12d), and this is attributed to the small cable stiffness in the transverse direction.
## 3 Results and Discussion
### Determination of the Total Carotenoids Content  
The carotenoid content of the tubers depended on the tested treatments, varying between 1.47 and 3.46 mg-100 g\({}^{-1}\) d.w. (Table 5). On average, the control contained the most total carotenoids, with 3.46 mg-100 g\({}^{-1}\) d.w., and the tuber enriched with 5-Cl-7-I-8-Q contained the least, with 1.47 mg 100 g\({}^{-1}\) d.w. A statistically significant decrease in the total carotenoid content was found in potato tubers biofortified with all the tested forms of iodine. The occurrence of carotenoids is characterised by high variability. This is due to genetic and environmental factors (light, temperature), as well as tuber maturity. The colour of fresh potato flesh is determined by the varying content of blue or red anthocyanin pigments or is a result of the accumulation of yellow and orange carotenoids [25]. Potato carotenoids are mainly oxygen carotenoids, which are also called xanthophylls. Potatoes with white flesh have 5-10 mg-kg\({}^{-1}\) f.w. of the so-called total carotenoids, potatoes with yellow flesh have 10-35 mg kg\({}^{-1}\) f.w., and dark yellow potatoes can contain up to 100 mg-kg\({}^{-1}\) f.w.; the highest recorded content reached up to 260 mg kg\({}^{-1}\) f.w. [25]. Lutein, zeaxanthin, violaxanthin and neoxanthin are the main carotenoids present in potatoes, while \(\beta\)-carotene is present in trace amounts [72]. Lutein and zeaxanthin are components of the human retina [25] that must be obtained from the diet to prevent age-related macular degeneration (AMD). Green leafy vegetables are a good source of lutein, but a poor source of zeaxanthin, which is found in significant concentrations in a small number of foods. Therefore, the potato could be a strategic source of lutein and zeaxanthin, especially as a staple food. The concentration of the total and individual carotenoids in potato tubers depends on several factors, such as the genotype, agronomic factors, post-harvest storage, and cooking and processing conditions [73].  
Potato biofortification caused a reduction in the total carotenoid content. This is an unfavourable consequence of the application of this process, as it reduces the attractive nutritional value of potato tubers. However, our research was carried out in a pot experiment, and thus the effect of the use of iodoquinolines on this parameter should be verified in field cultivation.
## 3 Results
### Aviation Technical Support as a Service
#### 3.15.3 Tier 3: ATSaaS  
At the apex of the model is the ATSaaS hub, a sophisticated operations center that interfaces with multiple airlines.  
Specialized engineers and data scientists provide advanced analytical services, drawing on a deep understanding of aircraft systems and predictive analytics.  
By servicing multiple airlines, the ATSaaS provider achieves economies of scale, driving down costs and enhancing the quality of support.  
The centralization of data and expertise facilitates ongoing improvements in maintenance practices and aircraft design.  
This tiered ATSaaS model represents a holistic approach to aircraft maintenance and health management, characterized by its layered yet integrated structure:  
* The tiered approach ensures that decisions at every level are informed by the most comprehensive and current data available.
* Each tier can customize the services and analysis it provides or receives, allowing for flexibility in meeting specific needs.
* The model promotes collaboration across the industry, with data sharing between airlines, ATSaaS providers, MROs, and OEMs, ultimately leading to a more efficient and resilient aviation ecosystem.  
This advanced framework reflects the next step in the transformation of maintenance and HMGT support paradigms within the industry.