esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Performance Metrics  
In this study, the classification performance of the trained models was measured globally and for each category using three different metrics: Accuracy, F1-Score, and Balanced Accuracy.  
Accuracy, the percentage of elements correctly labeled by the model, was calculated using Equation (2) to evaluate the global performance of the models:  
\[Accuracy=\frac{\sum_{i=1}^{C}{TP_{i}}}{N}, \tag{2}\]  
where \(N\) is the quantity of elements, \(C\) is the number of categories, and \(TP_{i}\) is the quantity of elements belonging to real category \(i\) correctly labeled by the model as category \(i\) for every category \(i\). By definition, the Accuracy is a real number between 0 and 1. A high Accuracy indicates good global performance of the model, with the best result reaching 1 when all the elements are correctly labeled by the model.  
On the other hand, F1-Score and Balanced Accuracy were calculated for every category \(i\) to evaluate the performance of the model over every category. Thr F1-Score is the harmonic mean of the trade-off metrics, precision and recall, as defined in Equation (3), for every category \(i\):  
\[F1-Score_{i}=2*\frac{Precision_{i}*Recall_{i}}{Precision_{i}+Recall_{i}}, \tag{3}\]  
where  
\[Precision_{i}=\frac{TP_{i}}{TP_{i}+FP_{i}},and \tag{4}\]  
\[Recall_{i}=\frac{TP_{i}}{TP_{i}+FN_{i}}. \tag{5}\]  
\(FN_{i}\) is the quantity of elements belonging to real category \(i\) incorrectly labeled by the model as a category different from \(i\), while \(FP_{i}\) is the quantity of elements not belonging to real category \(i\) incorrectly labeled by the model as category \(i\). The maximum possible F1-score value is 1, which indicates perfect precision and recall, while the minimum possible value is 0, which is the case if either precision or recall is zero.  
Second, Balanced Accuracy is the arithmetical mean of the trade-off metrics, sensitivity and specificity, as shown in Equation (6):  
\[\text{{Balanced Accuracy}}_{i}=\frac{\text{{Sensitivity}}_{i}+\text{{Specificity}}_{i }}{2}, \tag{6}\]  
where  
\[\text{{Sensitivity}}_{i}=\frac{TP_{i}}{TP_{i}+FN_{i}}, \tag{7}\]  
\[\text{{Specificity}}_{i}=\frac{TN_{i}}{TN_{i}+FP_{i}}, \tag{8}\]  
and \(FN_{i}\) is the quantity of elements belonging to real category \(i\) incorrectly labeled by the model as belonging to a category other than \(i\). The highest possible Balanced Accuracy value is 1, indicating perfect Sensitivity and Specificity, and the lowest possible value is 0, which is the case if both Sensitivity and Specificity are zero.  
In summary, when evaluating a particular category, the closer the Balanced Accuracy and F1-Score are to 1, the better the model can correctly classify observations.
## 4 Experiments and Analysis  
### Experimental Dataset  
To comprehensively assess the detection performance of the YOLO-AFR model across various scenarios and behavior scales, this study adopts the same two publicly available  
Figure 5: Structure of Separated and Enhancement Attention Module (SEAM).  
datasets, encompassing six categories of dangerous driving behaviors of different scales. These datasets are the YawDD-E and SfdDD datasets. The YawDD-E dataset is composed of three sub-datasets: YawDD [40], VOC-COCO [41], and DrivFace [42], containing a total of 2951 images captured from a frontal view within the vehicle. To ensure a fair evaluation and enhance the model's generalization ability across different individuals, we adopted a cross-driver split strategy, whereby images of the same driver do not appear in both the training and test sets. The SfdDD [43] dataset consists of 3900 images taken from a side-view perspective and a similar cross-driver partitioning was applied. This approach allows for a more robust evaluation of the model's capacity to detect dangerous driving behaviors performed by different drivers. The distribution of labeled categories in each dataset is presented in Table 1. Representative samples from the dataset are shown in Figure 6.
## 5 Evaluation Metrics
### Contrast Enhancement Based Contrast-Changed Image Quality Measure (CEIQ)  
In the existing NR-IQA studies, image contrast is a very important metric [21]. [PERSON] et al. [22] proposed an effective metric to evaluate the performance of contrast enhancement algorithms in the absence of reference images.  
The method first generates an enhanced image and calculates the similarity between the original and enhanced images using the structural similarity index (SSIM) as the first feature. Subsequently, the entropy and cross-entropy between the original and enhanced images are calculated separately to obtain the sum of the four features. Finally, a regression  
Figure 10: Flowchart of the GTIE-IACE algorithm.  
module is used to fuse these five features to obtain the evaluation score. The similarity calculation of SSIM is as follows:  
\[SSIM\left(I_{g},I_{e}\right)=L\left(I_{g},I_{e}\right)^{\alpha}*C\left(I_{g},I_{ e}\right)^{\beta}*S\left(I_{g},I_{e}\right)^{\lambda} \tag{26}\]  
where, \(I_{g}\) and \(I_{e}\) are two images, \(L\), \(C\), and \(S\) represent the similarity between the two images in terms of brightness, contrast, and structure, and \(\alpha\), \(\beta\), and \(\lambda\) denote the relative importance of the brightness, contrast, and structural similarity in SSIM. CEIQ uses SSIM as its first evaluation feature.  
Histograms provide image contrast information, so CEIQ utilizes histograms as the second metric. Entropy is a concept in information theory used to measure the uncertainty or information content of a random variable. It is a metric that quantifies the probability distribution of the random variable. The histograms of both high-contrast and contrast-enhanced images tend to have uniform distributions. In other words, they have high entropy. The entropy of a histogram can be expressed as:  
\[E=-\sum_{i=0}^{b}h(i)log_{2}h(i) \tag{27}\]  
where, \(b\) denotes the value of the bin of the histogram. By using cross-entropy, the relationship between the input grey level histogram \(h_{g}\) and the equalized histogram \(h_{e}\) is considered, and it is calculated by the formula below:  
\[E_{ge}=-\sum_{i=0}^{b}h_{g}(i)log_{2}h_{e}(i) \tag{28}\]  
\[E_{eg}=-\sum_{i=0}^{b}h_{e}(i)log_{2}h_{g}(i) \tag{29}\]  
For any image, CEIQ utilizes image similarity \(S_{ge}\), histogram-based entropy \(E_{g}\) and \(E_{e}\), and cross entropy \(E_{ge}\) and \(E_{eg}\) to give a feature vector \(f\). Next, with the help of feature vectors and subjective scores of \(k\) images \(\left\{(f_{1},Q_{1}),(f_{2},Q_{2}),\cdots,(f_{k},Q_{k})\right\}\), a training set can be constructed, and a regression function can be learned. CEIQ uses the LIBSVM package and linear kernels to implement support vector regression and uses the regression model to evaluate the quality of input images.
## Results
### Measured CO\({}_{2}\) Fluxes
#### 3.2.2 Influence of N Treatments  
Nitrogen treatment had greater influence on NEE, GPP and ER fluxes than tillage system, due to strong growth responses of winter wheat to available N (Figure 4; Tables 1 and 2). Rates of NEE generally increased with increased rates of N fertilizer both in autumn and spring (Figure 4a,b). However, the dynamics and magnitude of NEE fluxes were similar under both the 0-N and cowpea treatments. The dynamics of GPP under N-treatments mostly followed similar patterns as NEE (Figure 4c,d). Rates of GPP mostly increased with increased rates of N fertilization. However, dynamics and magnitude of GPP fluxes were similar under both 0-N and cowpea treatments. The dynamics of ER under N-treatments mostly mirrored dynamics of GPP (Figure 4e,f). Rate of ER mostly increased with increased rates of N fertilization, with dynamics and magnitude of ER fluxes similar under the 0-N and cowpea treatments. Influence of N-treatment was not significant for SR rates on both sites (Figure 4g,h; Tables 1 and 2).  
Figure 4: Dynamics of (**a,b**) net ecosystem exchange (NEE) of CO\({}_{2}\), (**c,d**) gross primary production (GPP), (**e,f**) ecosystem respiration (ER), and (**g,h**) soil respiration (SR) fluxes in response to N treatments. Each point represents averages across tillage systems under an N treatment. Error bars represent the spatial variations at plots (standard error, \(n=6\)). Unidirectional error bars are shown for clarity.
## 2 Background
### QCA Four-Phase Clock  
A four-clocking phase scheme for the QCA is shown in Figure 3. As shown in the figure, the barriers (potential barriers) rise during the first clock phase (switch). At the beginning of this phase, the borders are low, and the QCA cell is unpolarized; in this state, under the effect Columbic repulsion, the cell receives data from its adjacent cells. Then, with the barriers rising, the QCA cells are polarized according to their input drive modes, and at the end of this clock phase, the borders are high enough to prevent electron tunneling. As a result, the cell is locked. It is in this phase that the actual switching happens. During the second phase of the clock (hold), the barriers remain high. In this phase, the cell is relatively stable and transmits its data to the adjacent cells. The walls gradually decrease during the third clock phase (release), and the cell becomes unstable. In this phase, the cell is allowed to lose its polarization (unpolarized). During the fourth clock phase (relax phase), the cell barriers are in the lowest state, and the cells remain unpolarized. The cell is not used in this phase. After the end of this phase, the cell enters the switch phase again [3; 9].  
Figure 3: (**a**) Clock phases and (**b**) QCA four-phase clock mechanism.  
Figure 2: Behavior of adjacent cells.  
### QCA Four-Phase Clock  
One of the gates used in logic circuits is the inverter gate (Not gate). A type of inverter gate used in QCA technology is shown in Figure 4. It is used for inverting the desired signal as required [10,11].  
One of the most usable logic gates in QCA technology is the majority gate. This gate has an odd number of inputs and one output. In other words, the output cell value (output cell polarization) is determined according to the logical value of the majority inputs. As a result, the output cell value is determined based on the majority of inputs [8,10]. Figure 5a shows an example of this gate.  
By stabilizing (fixed) one of the inputs of the majority gate and considering a logical value \"0\" (polarization \(-1\)), the AND gate is generated [10,12]. Figure 5b shows a two-input AND gate.  
The OR gate is generated by fixing one of the majority gate inputs and considering a logical value \"1\" (polarization +1) [10,12]. Figure 5c shows a two-input OR gate.  
Figure 4: Two different Not-gate in QCA based on 90\({}^{\circ}\) cells.  
Figure 5: (a) QCA with implementation of the majority gate, (b) QCA with implementation of the AND Gate with two inputs and (c) QCA with implementation of the OR Gate with two inputs.
## 3 Principle and Implementation of Markov Chain Random Carrier Frequency Modulation  
### Principles of RCFM Technique and Random Number Analysis  
Conventional PWM techniques employ fixed carrier frequency modulation, resulting in the sideband harmonic current spectrum concentrated in the carrier frequency and its multiples, which significantly affects the vibro-acoustics performance of the motor. By combining the carrier frequency with random numbers, the carrier frequency can be randomly varied within a certain range. The random carrier frequency \(\omega_{n+1}\) after randomization of the original fixed carrier frequency \(\omega_{c}\) can be expressed as  
\[\omega_{n+1}=\omega_{c}+R\cdot s \tag{9}\]  
where \(s\) denotes the random number varying in the range of \((-1,1)\). \(R\) denotes the random gain of the pseudo-random number generation strategy, which is specified in the spectrum as the spreading width of the sideband harmonic components.  
The core of the RCFM technique is the random number generation strategy. The pseudo-random number generation method, linear congruential method, table look-up method and chaotic mapping method are usually used to generate random numbers. If the randomness of random numbers is good, the harmonic current will be uniformly distributed within the carrier frequency range, and the harmonic amplitude will be effectively reduced, thus further suppressing the vibration noise of the motor.  
The modulation effect after the carrier frequency randomization is determined by the random value \(s\) and the random gain \(R\) at a certain moment, i.e., the carrier frequency varies within the range of \([\omega_{c}-R,\,\omega_{c}+R]\), and the PWM signal after asymmetric random modulation is shown in Figure 3. In addition, since RCFM does not impose restrictions on random number generation, it unavoidably results in random number values exceeding  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Current Harmonics** & **Magnetic Flux Density** & **Radial** \\  & & **Electromagnetic Force** \\ \hline \(\omega_{c}-7\omega_{0}\) & \(\omega_{c}-7\omega_{0}\) & \(\omega_{c}-6\omega_{0}\), \(\omega_{c}-8\omega_{0}\) \\ \(\omega_{c}-5\omega_{0}\) & \(\omega_{c}-5\omega_{0}\) & \(\omega_{c}-4\omega_{0}\), \(\omega_{c}-6\omega_{0}\) \\ \(\omega_{c}+5\omega_{0}\) & \(\omega_{c}+5\omega_{0}\) &or falling below the average expected value during the generation process. Consequently, the random carrier frequency affected by it will be larger or smaller than the center carrier frequency in a certain period of time, as shown in Figure 4.
###### Abstract  
In recent years, there has been an increase in the search for novel raw materials for the production of natural carotenoids. Among yeasts, _Rhodotorula_ species have the ability to synthesize carotenoids, mainly \(\beta\)-carotene, torulene, and torularhodin, depending on the culture conditions. This study aimed to determine the effect of selected cations (barium, zinc, aluminum, manganese) and B vitamins (biotin, riboflavin, niacin, pantothelonic acid) on the biosynthesis of carotenoids by _Rhodotorula mucilaginous_ MK1 and estimate the percentages of \(\beta\)-carotene, torulene, and torularhodin synthesized by the yeast. The cultivation was carried out in a medium containing glycerol (waste resulting from biodiesel production) as a carbon source and potato wastewater (waste resulting from potato starch production) as a nitrogen source. Caroenol biosynthesis was stimulated by the addition of aluminum (300 mg/L) or aluminum (300 mg/L) and niacin (100 mg/L) to the medium. The number of carotenoids produced by _R. mucilaginous_ MK1 in the medium containing only aluminum and in the medium with aluminum and niacin was 146.7 and 180.5 mg/gd\({}_{\text{d,m}}\), respectively. This content was 101% and 147% higher compared to the content of carotenoids produced by yeast grown in the control medium (73.0 mg/gd\({}_{\text{d,m}}\)). The addition of aluminum and barium seemed to have a positive effect on the biosynthesis of torulene, and the percentage of this compound increased from 31.86% to 75.20% and 68.24%, respectively. Niacin supplementation to the medium increased the percentage of torularhodin produced by the yeast from 23.31% to 31.59-33.79%. The conducted study showed that there is a possibility of intensifying carotenoid biosynthesis by red yeast and changing the percentages of individual carotenoids fractions by adding cations or B vitamins to the medium. Further research is needed to explain the mechanism of action of niacin on the stimulation of torularhodin biosynthesis.  
red yeast; carotenoids; metals; vitamins; glycerol; potato wastewater +
Footnote †: journal: Applied Sciences
## 4 Graphene/Graphene Oxide-Based RF Biosensing Platforms
### Graphene-Based Wireless Mobile Health System
#### 4.2.2 Graphene-Based Mental Stress Sensors  
Figure 8 shows an integrated, flexible, and miniaturized wireless mobile health (mHealth) sensing device based on laser-engraved graphene (Figure 8a). This integrated sensor was used for immuno-sensing with proven utility for fast, reliable, sensitive, and non-invasive monitoring of cortisol, a stress hormone, using sweat as a test sample. Based on the test results, [PERSON] et al. [75] demonstrated a strong empirical correlation between serum and sweat cortisol, revealing exciting opportunities offered by sweat analysis toward non-invasive dynamic stress monitoring by wearable and portable sensing platforms.  
Figure 8: Integrated wireless graphene-based sweat stress sensing platform. (**a**) Schematic illustration of the origin of cortisol in sweat and saliva and the use of the GS4 to track the circulating cortisol level. CRH, corticotropin-releasing hormone; ACTH, adrenocortic hormone; (**b**) schematic of the electrochemical detection of cortisol in human sweat; and (**c**) representation of the affinity-based electrochemical cortisol sensor construction and sensing strategy. HRP, horseradish peroxidase; HQ, hydroquinone; PPA, pyrrole propionic acid; BSA, bovine serum albumin; mAb, monoclonal antibody (reprinted with permission from [75]).  
Figure 7: A smart contact lens with and without graphene coating. (**a**) Electromagnetic (EM) wave passes through the contact lens and absorbed by an eyeball, possibly causing heat damage inside, and (**b**) EM energy is absorbed by graphene and dissipated as heat before reaching the interior of the eye (reprinted with permission from [71]).  
This patch-type flexible sensor consisted of three working electrodes (WEs) coated with graphene, one Ag/AgCl reference electrode (RE), and one counter electrode (CE) coated with graphene (Figure 8b). Cortisol detection through human sweat was achieved by combining carboxylate-rich pyrrole-derivative grafting and subsequent modification on the graphene surface and a competitive sensing strategy. Here, graphene is used as electrodes in terms of the large surface area and fast electron mobility in electrochemical sensing [76], while competitive immuno-sensing strategies offer significant advances in highly selective small hormone molecule detection [77].
# Intelligent Diagnosis of Concrete Defects Based on Improved Mask R-CNN
## 3 Experimental Data and Parameters
### Aspect Ratios of the Anchor Boxes  
Generally, in Mask R-CNN, there are three aspect ratios of anchor boxes (0.5, 1, 2). However, the shape of the concrete defects is varied, such as the cracks are slender, and the spalling is polygonal; these anchor boxes (three different aspect ratios (0.5, 1, 2)) are not necessarily suitable for the shape of the concrete defects.  
In this paper, the K-means clustering algorithm was used to select the proper aspect ratios of the anchor boxes. In the K-means clustering algorithm, K data points were randomly selected as the initial clustering center, and for each data point in the dataset, its distance to each cluster center is calculated and assigned to the cluster corresponding to the cluster center with the closest distance. For each cluster, calculate the average of all data points within it and use this average as the new cluster center. The above steps are repeated until the clustering centers no longer change significantly or a preset number of iterations is reached. When the clustering center starts invariant, this means that the algorithm has converged to a state where each sample point is assigned to the nearest clustering centre and these assignments do not change in successive iterations. All the samples are correctly classified, and the optimal classification result is achieved. Figure 11 shows the clustering graph for the concrete defect dataset. with the increasing number of clusters, the clustering center starts to be invariant until the ninth cluster. This state does signify that the algorithm has found a 'locally optimal' classification result. At this time, the aspect ratios of the anchor box are (0.06, 1, 17.36).
# Development of Quantum Private Queries Protocol on Collective-Dephasing Noise Channel
## 1 Introduction  
[PERSON] and [PERSON] proposed the first QKD protocol which is also known as BB84 protocol using single-photon polarization states in 1984 [1]. Since then, a lot of research on quantum cryptography has been done. After 30 years' development, the quantum cryptography protocol has been proposed for many territories including quantum key distribution protocol [2; 3; 4; 5], quantum secret sharing protocol [6], quantum direct communication protocol [7], quantum teleportation [8] and quantum private query protocol what we will study in this paper.  
Quantum private query (QPQ) is an important branch of quantum cryptography. One of the most important problems is the Symmetric Private Information Retrieval (SPIR) problem. This problem mainly involves two aspects: the first aspect is the database security problem, i.e., [PERSON] does not want [PERSON] to obtain any information other than the \(k\) pieces of information she wants to retrieve; the other aspect is user privacy, i.e., [PERSON] does not want [PERSON] to know what information she wants to query. The quantum oblivious key distribution protocol has been proposed by predecessors, which is a good solution to the SPIR problem. On this basis, this paper also improves the process of the oblivious key distribution, to improve the quantum private query protocol.  
With the increasing of protocol security, various attack methods emerge one after another, such as false entanglement attack, joint-measurement (JM) attack [9], etc. Therefore, it is necessary to study a more secure, practical and efficient QPQ protocol in view of the shortcomings of the existing QPQ protocol. In the communication process, photons inevitably face noise interference caused by thermal fluctuations, vibration and optical fiber defects. Therefore, from the perspective of practical implementation feasibility, it is very important to eliminate the negative effects of noise. There are many methods to resist the environmental noise on quantum private query. In reference [3], quantum error correction code is used to eliminate the influence of environmental noise. Decoherence-free (DF) [10] states are also used to resist collective noise [11]. Because decoherence-free state has the characteristic of resisting the invariance of collective noise, we use it to resist collective noise in this paper.  
Based on the above analysis, this paper improves the process of quantum oblivious key distribution. The logical Bell state (i.e., 4 qubit decoherence-free state) [11] is used to overcome the collective-dephase noise and improve the quantum private query protocol. In addition, database security and user privacy can be guaranteed.