esa-sceva/satcom-chunk-collection huggingface dataset:
# A Quantum-Secure Cryptographic Algorithm Integrating Fractals and Prime Numbers
## 4 Overview of the Infrastructure
In our algorithm, this matrix is constructed from the RSA private key; by scanning one digit at time, this represents the index at which the one is positioned in the row. Since the private key is composed of equal digits, we perform two controls on the private key before constructing the matrix: the first is to make sure that the length of the key is at least equal to the number of rows/columns in the matrix. In the second, we verify whether there are equal elements in the key. This because the ones that we position in the matrix must all have different positions; otherwise, the matrix will no longer be an identity matrix with some rows/columns changed. If we find numbers that are already present in the key, we substitute them with a number generated randomly that is not present. After constructing the permutation matrix, we multiply the hybrid matrix and the permutation matrix. The algorithm then continues on to the quantum security part, which we anticipated in the introduction. The technique used to guarantee this property regards operations inside the newly obtained product matrix, and it draws inspiration from quantum mechanics, specifically the scattering theory. The technique consists of creating two fragments of matrix digits of a certain length and \"colliding\" them to create two new fragments, mixing the digits in them. In this case, the crypto-agility consists of dynamically evolving each fragment, which means that at different times, the fragment will be in a different position from its initial allocation. For each fragment created and at every moment the daemon is free from a task, it takes two fragments, \(FrA\) and \(FrB\), which have two contents, \(Ca\) and \(Cb\), and gives as output two new fragments \(FrC\) and \(FrD\) with contents \(Cc\) and \(Cd\). Figure 10 shows this interaction. The algorithm we used randomly chooses one of these two operations: the first is swapping rows \(m\) and \(m+1\) in the matrix, with \(m\) randomly chosen at each specific time; the second is taking rows \(m\) and \(m+1\) with \(m\) randomly chosen and subdividing them into specific amounts of the total, for example, 30% and the 70% of the digits. So, we obtain four fragments, which we collide two at a time; mix the content; and recombine them with a cross-shifting operation, where the first 70% of the row \(m\) is merged with 30% of the row \(m+1\) and 70% of the row \(m+1\) is merged with 30% of the row \(m\). Finally, we unite, row by row, the matrix digits, obtaining the new cryptographic key.
## 4 Thermographic Techniques for Breast Cancer Detection
### Steady-State Thermography
Each participant rested for 15 min on a chair in an erect position with their hands over the head in a controlled-temperature room maintained at 23-25 \({}^{\circ}\)C. After that, two lateral and two oblique views of IR images were recorded using an ATIR-M301 thermal imaging system (with a 320 \(\times\) 240 focal plane array detector and a temperature resolution less than 0.1 \({}^{\circ}\)C) placed 2.5 m away from the subject. To investigate the diagnostic performance of the IR thermography, the authors analyzed the collected thermal images by evaluating five independent diagnostic signs by means of statistical analysis. In particular, they defined univariate and multivariate logistic regression models to investigate the correlation between IR signs and the final disease status assessed by biopsy. The authors predicted sensitivity and specificity values of 72.4% and 76.6%, respectively. Low sensitivity values for digital infrared thermal imaging applied under steady-state conditions were reported by [PERSON] et al. [51] in 2011. These authors examined 63 patients using the Meditherm med2000 thermal imaging system. A series of the recorded thermal images is shown in Figure 5. The examination was performed in a room without windows, in which the temperature was approximately maintained at 22 \({}^{\circ}\)C. Also, the patients were left to acclimatize with ambient air for 10-15 min before sitting on a rotation stool (without a back rest) placed 1 m away from the thermographic camera. The authors concluded that steady-state infrared imaging should not be used as a screening test for breast cancer detection, and it was also not suitable for the primary evaluation of symptomatic patients because of its low sensitivity. [PERSON] et al. [52] examined a group of 47 breast cancer patients using the same dimensionless temperature gradient-based methodology described in [26]. In their study, the following two different IR cameras were employed: a ThermoCAM T400 FLIR E60 (with a resolution of 320 \(\times\) 240 pixels and a temperature sensitivity of 0.05 \({}^{\circ}\)C) and an SAT-S160 camera with a low optical resolution (160 \(\times\) 120 pixels) and lower temperature sensitivity (0.1 \({}^{\circ}\)C). Figure 6 shows a thermal image recorded by the highest performance camera. The authors reported that their methodology was efficient in identifying breast lesions even when a lower-resolution camera was used. Therefore, they concluded that their methodology can be potentially used as a non-invasive screening tool. Figure 5: Thermal images of a patient having a suspicious lesion on the left breast, from Ref. [51] (permission asked and obtained from Elsevier, Amsterdam, The Netherlands).
# DLI-Based DP-QPSK Reception Scheme for Short-Range Optical Communication
## 6 Conclusions  
In this study, the structure and basic principles of a DLI-based DP-QPSK transmission system were briefly introduced. A data recovery algorithm was proposed for a receiver based on DLI that could recover raw data due to the lack of a differential precoding process in the DP-QPSK modulation.  
Figure 8: Measured OSNR penalty versus the chromatic dispersion at BER \(=3.8\times 10^{-3}\): (**a**) the dispersion tolerance due to the 2 dB OSNR penalty of the receiver, and (**b**) the OSNR penalty required for receivers with different delays to maintain a BER of \(3.8\times 10^{-3}\) under the same system dispersion.  
Measures to improve the demodulation performance were further studied and it was shown through theoretical derivation that appropriately increasing the FSR of DLI could achieve the goal. The simulation results showed that the algorithm was effective for recovering the raw data in a DLI-based DP-QPSK receiver and the 2 dB dispersion tolerance was increased by nearly 20% when decreasing the delay to a 0.7-symbol period under the simulation configuration set in this study. Hence, the DLI-based receiving scheme for a DP-QPSK is an ideal solution for short-range, non-cooperative communication. It is noteworthy that the performance of the data recovery algorithm depends to a certain extent on the frame synchronization algorithm, which will need to be optimized in our future work.  
Conceptualization, H.Y. and Y.Y.; methodology, H.Y.; software, H.Y.; supervision, Z.H.; validation, Y.Y.; writing--original draft, H.Y.; writing--review and editing, H.Y. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (General Program), grant no. 51575517.  
The authors declare no conflict of interest.
# Combining Machine Learning and Logical Reasoning to Improve Requirements Traceability Recovery
###### Abstract  
Maintaining traceability links of software systems is a crucial task for software management and development. Unfortunately, dealing with traceability links are typically taken as afterthought due to time pressure. Some studies attempt to use information retrieval-based methods to automate this task, but they only concentrate on calculating the textual similarity between various software artifacts and do not take into account the properties of such artifacts. In this paper, we propose a novel traceability link recovery approach, which comprehensively measures the similarity between use cases and source code by exploring their particular properties. To this end, we leverage and combine machine learning and logical reasoning techniques. On the one hand, our method extracts features by considering the semantics of the use cases and source code, and uses a classification algorithm to train the classifier. On the other hand, we utilize the relationships between artifacts and define a series of rules to recover traceability links. In particular, we not only leverage source code's structural information, but also take into account the interrelationships between use cases. We have conducted a series of experiments on multiple datasets to evaluate our approach against existing approaches, the results of which show that our approach is substantially better than other methods.  
Keywords:requirements traceability recovery; artificial intelligence; machine learning; rule-based reasoning; feature engineering +
Footnote †: journal: Applied sciences
# Vortex: Network-Driven Opportunistic Routing for Ad Hoc Networks
## 4 Performance Evaluation
### Simulation Setups
The end-to-end delay and hop count represent the length of the route in terms of time and logical distance, respectively. In general, a shorter hop count results in a shorter delay. However, ORs use appropriate routes opportunistically, and simultaneous forwarding with multiple routes may not use the shortest route. Thus, there might be another relationship between the delay and hop count in ORs, and this evaluation tries to reveal it. The energy consumption evaluates the overall transmission efficiency since ORs generally use multiple and non-shortest routes, which may require more transmission even though they improve end-to-end reliability. In addition to that, unicast routing protocols such as AODV adopt the ARQ mechanism in the MAC layer to improve link reliability, whereas it increases energy consumption. Thus, evaluating the energy required to send a single packet from source to destination represents reasonable overall routing efficiency including reliability.
## 2 Methods
### Adaptive Denoising Learning Strategy  
In real-world applications, labels are inherently noisy, in the sense that they do not represent a precise ground truth. Our adaptive denoising learning (ADL) strategy is fairly straightforward, and yet efficient. To simulate different scenarios involving noise labels, a proportion \(\beta\) of the provided ground truth masks in the training data (which are accurate) are replaced with noisy labels generated synthetically, as described in Section 2.3. These labels exhibit erosions, dilations, or elastic transformations, which are illustrated in Figure 1.  
Drawing inspiration from O2U-Net [36], in every training epoch, we log the discrepancy between each prediction and its corresponding ground truth label. Labels accruing higher losses are more likely to be considered noisy, and are subsequently discarded at the epoch's conclusion.  
During the training process, our ADL strategy identifies and discards a specific quantity of labels with high loss values. As illustrated in Figure 3, a considerable quantity of noisy labels are detected and eliminated at the beginning of the training iteration; this quantity decreases gradually, as the training progresses. This reflects the transition of the training process from a state of underfitting to overfitting [17]. The number \(N(t)\) of labels identified and removed at each epoch is calculated as  
\[N(t)=\frac{\beta y}{k^{2}}(k-t) \tag{11}\]  
In this equation, \(t\) corresponds to the current training epoch, \(\beta\) represents the proportion of items in the training dataset subjected to noise, \(k\) stands for the total count of training epochs, and \(y\) indicates the total count of masks. By integrating ADL into the training process, we amplify the network's robustness to noisy labels. This strategy, in turn, improves the model's performance and clinical applicability.
## 3 Experiments and Results
### The Experimental Data  
The data set used from the bearing dataset of the University of Ottawa [46]. The sampling frequency of the test bench is 200 kHz. The encoder and acceleration sensor measures the speed and bearing vibration signals. The measured data include normal, inner race fault, and outer race fault. There are four speed shifting schemes, which are acceleration (\(\uparrow\)), deceleration (\(\downarrow\)), acceleration then deceleration (\(\uparrow\downarrow\)) and deceleration then acceleration (\(\downarrow\uparrow\)). The minimum speed in data collection is 9.9 Hz, and the original signal is segmented with 20,000 sampling points to ensure that each sample contains as much as possible a period. The length of each sample is also reduced to 800 to obtain a total of 3600 samples, including 1200 for each condition, which is randomly divided into the training set, validation set, and test set in the same ratio (6:2:2). More detailed information is shown in Table 1.  
To verify the superiority of our proposed method, the proportion of samples between the training set and the test set is still 3:1, in which the test set samples are the same in each experiment, and the training set is randomly selected from the rest of samples in proportion. In other words, the total sample size is 3600, the sample size of the test set is fixed at 720, 240 for each fault, and 2160 samples are randomly and evenly selected from the remaining 2880 samples in each experiment training set. The time-domain waveform of some samples is shown in Figure 4a-c. It can be seen that the time-domain waveform of vibration signals of rolling bearing with variable speed is complex, and different fault types contain signal components with frequency transients, making it difficult to extract features directly.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bearing** & **Variable Speed** & **Training Set** & **Validation** & **Test Set** & **Class** \\
**Condition** & **Condition** & **Set** & **Test Set** & **(Label)** \\ \hline Healthy state & \(\uparrow\) / \(\downarrow\) / \(\uparrow\downarrow\) / \(\downarrow\uparrow\) & 720 & 240 & 240 & 1 \\ Inner race fault & \(\uparrow\) / \(\downarrow\) / \(\uparrow\downarrow\) / \(\downarrow\uparrow\) & 720 & 240 & 240 & 2 \\ Outer race fault & \(\uparrow\) / \(\downarrow\) / \(\uparrow\downarrow\) / \(\downarrow\uparrow\) & 720 & 240 & 240 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bearing dataset of the University of Ottawa.  
Figure 4: Time–frequency images of vibration signal of rolling bearing: (**a–c**) are time–domain waveforms of normal, inner race fault and outer race fault samples respectively; (**d–f**) are STFT time-frequency images corresponding to (**a–c**) respectively; (**g–i**) are TMSST time-frequency images corresponding to (**a–c**); (**j–l**) are MSST time-frequency images corresponding to (**a–c**) respectively.
# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 5 Experimental Verification and Results
### Results Analysis  
Based on the validation experiment results shown in Figure 6 and Table 3, we analyzed the performance of the proposed MEC-based crowdsourced fingerprint environmental sensing framework. The key findings and observations include the following:  
1. Perception accuracy: By comparing the system's outputs with ground-truth environmental sensing data from target devices, we evaluated localization accuracy. For the three integrated algorithms, the building prediction error rate remained below 0.5%, while the floor prediction error rate was approximately 1%. The system achieved a mean positioning error of less than 5 m, with around 95% of devices demonstrating errors within 10 m. These results confirm the high localization precision of the proposed framework under most conditions, underscoring the effectiveness of the MEC and crowdsourcing-based approach.
2. Real-time capability: By uploading environmental sensing data from user devices to MEC servers, the framework enabled real-time localization and dynamic fingerprint map updates. Experimental results demonstrated that the system completes environmental sensing processes and updates fingerprint maps efficiently, providing a robust foundation for real-time tracking applications.
3. Operational efficiency: Leveraging crowdsourcing for data collection significantly reduced the workload of professional surveyors while enhancing data collection efficiency. Participants seamlessly contributed environmental sensing and device metadata, enabling continuous fingerprint map construction and updates. This distributed data collection approach aligns well with the rapid development and widespread adoption of 5G networks.  
In summary, the validation experiments demonstrate that the proposed MEC-based crowdsourced fingerprint environmental sensing framework achieves high perception accuracy, real-time responsiveness, and operational efficiency.
# Comparative Analysis of Volatile Compounds in Flowers of Different _Actinidia_ Species
## 2 Results and Discussion
### Pollinators' Interest  
Since actinidia is dioecious, effective pollination is essential to obtain well-formed fruit. Honey bees seem to be the most important pollinators for kiwifruit [29]. Bumble bees are also used for pollination, especially since they are commercially available [22]. Our unpublished observation of kiwiberry orchards in Polish conditions suggests that _A. arguta_ flowers are visited mainly by _Bombus terrestris_, _B. lapidarius_ and _Apis mellifera_. According to [PERSON] et al. [18], honey bees and bumble bees are interested in certain _A. chinensis_ volatile compounds from male and female flowers. Some of these compounds also found in our research, e.g., phenylethyl alcohol, alpha-Farnesene, linalool, 6-methyl-5-hepten-2-one, geranyl acetone, 2-phenylethyl acetate and 8-heptadecene, were the same or similar isomers found in at least one sample of _A. arguta_ flowers in our research. Phenylethyl alcohol and 6-methyl-5-hepten-2-one were present in most samples. The first compound, as [PERSON] et al. [17,18] found out, provided the largest response from antennae in both honey and bumble bees. Furthermore, seven out of 19 compounds attractive to the investigated pollinators were found in our _A. chinensis_ var. _deliciosa_ sample. Honey bees were observed to respond to phenylethyl alcohol, 1-hexanol and (Z)-3-hexen-1-ol. These compounds were also attractive to nocturnal bees--major pollinators for cambuci plants  
Figure 4: Sample chromatograms with main volatile compounds in flowers of female and male _Actinidia arguta_ (**a**,**b**) and _Actinidia kolomikta_ (**c**,**d**) genotypes: (**a**) “Ananasnaya”, (**b**) “Nostino”, (**c**) female, (**d**) male.  
(_Campomanesia phaea_) [30]. In other reports, linalool was observed to attract nocturnal bees _Megalopa_ on guarana (_Paullinia cupana_) [31], and honey bees on _Medicago sativa_[32] and pears (_Pyrus communis_), where (E)-\(\beta\)-ocimene and lilac alcohols were also found to be attractive to bees [33]. Compounds like \(\alpha\)-pinene and D-limonene were also found to be attractive to _A. mellifera_[34], with the second one identified in the _A. chinensis_' headspace sample.  
A comparison of our data on volatile compounds in actinidia flowers and the literature about insect interest allows us to conclude that actinidia flowers may be attractive to pollinating insects, which makes it possible to select the best genotypes, especially male ones.
## 2 Experimental Program
### Material Properties  
Various material tests were performed to identify the mechanical properties of concrete, reinforcing bars, and masonry. Figure 3 shows types of material tests for masonry. The compressive strength tests were performed on the masonry units (concrete bricks) and mortars, and the compressive strength and bond strength were tested on the masonry prism.  
Figure 2: Details of a test specimen (unit: mm). (**a**) Dimension of specimens. (**b**) Column section. (**c**) Beam section. (**d**) Masonry-infilled frames. (**e**) Masonry wall in IF-0.5 & IFS-0.5. (**f**) Masonry wall in IF-1.0 & IFS-1.0.  
Table 2 summarizes the results of material tests and applied test standards. Originally, it was planned to use a D16 rebar of SD300 (with a nominal yield strength of 300 MPa) as the longitudinal rebar in all specimens. However, during the construction of the BF specimen, a D16 rebar of SD400 was used by mistake. Table 2 includes the yield strength of the rebars of both grades. For masonry, various material tests were performed. Figure 3a-c depicts the compressive strength test for the masonry unit, mortar, and masonry prism, respectively. The masonry unit is a commonly used solid concrete brick with dimensions of 190 \(\times\) 90 \(\times\) 56 mm. The mortar specimens are cubes with a volume of 50 mm\({}^{3}\) according to ASTM C109 [26]. Figure 3d shows the bond strength test to determine the shear strength of masonry [27]. In this test, the loads that cause bond failure with the prestress of 0.1, 0.3, and 0.5 MPa, respectively, were obtained, and the bond stress and the friction coefficient were derived from the regression formula, as shown in Figure 4. The bond strength and friction coefficient were 0.462 and 1.564, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Test** & **Result** & **COV (\%)** & **Test Procedure** \\ \hline Concrete & Compressive Strength & 35.884 MPa & 4.08 & ASTM C39 [28] \\ \hline \multirow{4}{*}{Rebar} & D16 Bar Yield Strength (SD300) & 367.6 MPa & 0.83 & \\  & D16 Bar Yield Strength (SD400) & 428.8 MPa & 1.82 & ASTM E8M [29] \\  & D10 Bar Yield Strength (SD400) & 440.2 MPa & 1.44 & \\ \hline \multirow{4}{*}{Masonry Unit (Concrete Brick)} & Compressive Strength & 11.85 MPa & 8.21 & \\  & Modulus of Elasticity & 6171 MPa & 9.54 & \\ \cline{1-1}  & Flexural Tensile Strength & 2.09 MPa & 15.09 & ASTM C67 [31] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties.  
Figure 3: Material tests for masonry. (**a**) Masonry unit compression test. (**b**) Mortar compression test. (**c**) Masonry prism compression test. (**d**) Initial shear strength test.
## 4 Current Studies Focused on PPO Activity
### Influence on PPO Activity
#### 4.4.2 pH  
Regarding pH, two opposite effects can be found in PPO activity, one related to PPO by itself and another one affecting the effectiveness of different washing treatmentsused in minimal processing in order to maintain browning and inhibit PPO activity, among other purposes (as it will be commented in following sections). In general, PPO shows a higher activity in cut surfaces of minimally processed produce at a neutral pH (approximately 7) and in the range 30-50 \({}^{\circ}\)C, being completely inactivated at 80 \({}^{\circ}\)C in most cases (considering minimally processed apple, pear or medlar) [8; 58; 66; 67]. However, depending on the produce, pH optimal range might vary, as it happened to fresh-cut pears, pointing out the importance of pre-harvest factors in many aspects of PPO activity, not only paying attention to its initial content [68]. Concerning the effectiveness of some treatments depending on pH, this fact might influence on the use of coatings or antioxidants. Thus, some anti-browning additives depend on the pH of the washing treatment, and some studies have found that the optimum pH range was found at around 7, as in the case of cysteine for minimally processed artichoke or calcium lactate for fresh-cut pears. In both cases, it was pointed out the selection of the optimum pH range in order to improve the effectiveness of the washing treatment, which is vital at industrial scale [25; 69].