esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation  
Utilizing the methodology presented by [2], the impact of the proposed estimation and prediction techniques has been simulated and assessed. Initially, the methods are outlined in Algorithm 2:In our study, we obtain the required censored data from the generalized Pareto distribution as defined, denoting the data points as \(X_{(1)},\ldots,X_{(r)},\ldots,X_{(m)}\). We utilize the true values of parameters \((\alpha,\lambda)\), specifically set at \((0.35,1.5)\). Maximum likelihood estimates are calculated using the expectation-maximization method. For Bayesian estimation and prediction purposes, we select the parameters \((a,b,p,q)\) with values of \((0.2,7.8,0.1,3.7)\), respectively. Additionally, we conduct Bayesian estimations using different loss functions, namely SEL, LINEX, and BSEL, employing the TK and Metropolis-Hastings approaches. We define the censoring model with respect to \(\mathrm{V}\). For instance, if \(m=5\) and \(n=9\), then the censoring scheme would be denoted as \((0,0,0,0,4)\), which we record as \(\mathrm{V}=(0^{*4},4)\). We will utilize the following ten censoring schemes for our simulations. Based on the schemes presented in Table 1, we will conduct a simulation study. The results are compiled and displayed below. In Table 2, we present the results of our numerical experiments conducted for each simulation. We report the maximum likelihood estimation and Bayesian estimation utilizing the TK method and the Metropolis-Hastings algorithm. Additionally, we provide the mean square error (MSE) values, which serve as a basis for comparing the results. In the table, for each censoring scheme, the estimated values are displayed in the first and third rows, while the second and fourth rows contain the corresponding MSE values. The fifth and sixth columns present the results obtained from the MH method using the BSEL function. The seventh and eighth columns illustrate the results from the MH method employing the LINEX function. The tenth and eleventh columns show the outcomes using the TK method under the BSEL function, and the twelfth and thirteenth columns provide the results from the TK method under the LINEX function. It is noteworthy that the MH method yields a smaller MSE compared to the TK approach. In terms of MSE comparison, the Bayesian estimation of parameters under the LINEX function exhibits a lower MSE than those using the SEL and BSEL functions, although the Bayesian estimations under SEL are closer to the true values. Regarding MLEs, larger sample sizes (denoted by \(m\) and \(n\), representing the total and observed samples, respectively) result in more accurate estimations. Overall, the findings indicate that Bayesian estimation has a clear advantage over MLEs. In Table 4, we present the point prediction results alongside the corresponding 95% prediction intervals. These predictions are generated based on the values of \(y_{3},y_{7}\), and \(y_{10}\) (collectively referred to as \(y_{p}\)) within a future dataset of size 10. Overall, it is observed that as \(p\) increases, the length of the prediction intervals also widens.
# _Ku_-Band CMOS Power Amplifier with Three-Stack Power Stage to Enhance Output Power and Efficiency
## 2 Typical CMOS Power Amplifiers
### Common-Source Structure  
In the case of a general power amplifier, a power stage for securing high output power and a driver stage for securing sufficient power gain are included. In the case of the driver stage, relatively less dc power is consumed compared with the power stage. However, the dc power consumption in the driver stage is a level that cannot be ignored in terms of dc power consumption of the entire power amplifier.  
The circuit structure commonly used for the driver stage and power stage of the power amplifier is a common-source structure and a cascode structure, as illustrated in Figure 1. In the case of a common-source structure, a relatively low power supply voltage is used in consideration of the breakdown voltage of the transistor. Accordingly, there is an advantage that the power consumption calculated as the product of the voltage and the current is relatively small. On the other hand, since a low supply voltage is used, it is relatively difficult to sufficiently secure an output power proportional to the square of the supply voltage. In addition, in the case of low power supply voltage and 50 \(\Omega\) load conditions, there is a disadvantage in that the sensitivity of the output matching characteristics to variations in the device values constituting the output matching network is high.
## 5 Data and Results
### Confidence Threshold Setup
#### 5.4.2 Confidence Thresholds for Human Detection in the Thermal Infrared Spectrum  
Since the confidence value for human detection in the thermal infrared spectrum \(C_{IR}\) for a sequence is based on confidence value \(C_{V}\) for that sequence, experimental results for the thermal infrared spectrum have been divided into two different tables, regarding the value of \(C_{V}\). Results for values _HIGH_ and _LOW_ of \(C_{V}\) are shown in Tables 2 and 3, respectively. Both tables are organized in a similar manner as Table 1. The difference is that the new tables include the standard deviation of the average illumination of the sequence, as well as the the contrast \(\upsilon_{IR}\).  
Let us now consider the case where the visible-light spectrum offers good or excellent results. Under these conditions, less significant differences are observed in Table 2, except for the last sequence, where the environment temperature is greater than 20\({}^{\circ}\) and the results are drastically worse. Hence, the threshold \(\zeta_{IRH}\) is experimentally set at a value of \(2.2\). It has been proven, as well, that precision suffers an abrupt decrease with environmental temperatures greater than 10\({}^{\circ}\), when the parametrization is not set to more restrictive values than at lower temperatures. Since coefficient \(v_{IR}\) is always above \(1.9\) for high temperatures, \(1.9\) is the value assigned to confidence threshold \(\zeta_{IRL}\).
## 3 Results
### Identification of Putative Seed Specific GmNOS-like and GmNR Genes in Soybean Genome  
NO has been shown to be an important signalling molecule performing various physiological functions in plants. NO synthesis from oxidative and reductive routes is evident in various plant species [44]. In model plant _Arabidopsis thaliana_, there are two nitrate reductase genes, named _AtNIA1_ (_ATIG77760_) and _AtNIA2_ (_ATIG37130_) [44; 45]. Since there is no information available for genes encoding for nitrate reductase genes (_GmNR_) in soybean genome, we searched for the orthologs of Arabidopsis _AtNIA1_ and _AtNIA2_ genes in the soybean genome using the PANTHER tool ([[http://www.pantherdb.org/](http://www.pantherdb.org/)]([http://www.pantherdb.org/](http://www.pantherdb.org/))) accessed on 9 December 2019 and identified a total of five putative nitrate reductase encoding genes in the soybean genome (Supplementary Table S1). The presence of nitric oxide synthase genes in higher plants is controversial, but NO production via oxidative route (NOS-like activity), supported by several reports, could not be ignored [45]. Therefore, to find the putative _NOS-like_ genes in soybean, the Arabidopsis gene _AtNOI/AtNOS1_ (_AT3G47450_) was used, and two _NOS-like_ genes were identified in soybean (Supplementary Table S1). Since we were interested to select the putative _GmNR_ and _GmNOS-like_ genes expressing in the seeds or early germinating seedlings, we checked the putative seed-specific expression profile of identified soybean _GmNR_ and _GmNOS-like_ genes on Phytozome 13 ([[https://phytozome-next.jgi.doe.gov/](https://phytozome-next.jgi.doe.gov/)]([https://phytozome-next.jgi.doe.gov/](https://phytozome-next.jgi.doe.gov/)), accessed on 30 May 2022). On the basis of putative expression profile, we selected two _GmNOS-like_ (_Glyma.09G224600; GmNOS-like 1_ and _Glyma.12G012400; GmNOS-like 2_) and _GmNR_ (_Glyma.13G084000; GmNR1_ and _Glyma.06G109200; GmNR2_) genes (Supplementary Table S1).
## 2 Related Work
### Multi-Exit Architectures  
[PERSON] et al. [30] was the first study to propose the use of early exit branch networks to achieve fast inference in DNNs. Subsequently, [PERSON] et al. [31] discovered that added early exits affect the performance of the final exits and the early exits do not possess coarse-scale features; therefore a multi-scale dense network was proposed. Additionally, two experimental conditions were set for image classification under different resource constraints. Since then, many researchers have investigated multi-exit networks from different perspectives. [PERSON] et al. [32] explored methods to train multi-exit networks effectively and used gradient equalization algorithms to solve the learning conflicts among the exits and improved the learning speed of the exits. There has been research on combining KD with a multi-exit architecture. [PERSON] [33] and [PERSON] et al. [34] prevented multiple classifiers of different depths from negatively affecting each other and enabled them to benefit from each other by adding KD and attention mechanisms. [PERSON] et al. [35] used KD to encourage early exits to mimic the previous exit, thereby resulting in a new form of KD. Each exit in harmonized dense KD [36] learns from all other exits and uses adaptive weight learning to optimize multiple losses. Such studies ignore teacher guidance, which can reduce resource use but also misses out on more effective guidance.  
In terms of the overall framework, hierarchical self-supervised augmented KD (HSAKD) [28] is similar to this study. HSAKD adds multiple auxiliary classifiers at different depths of the network to transfer different self-supervised augmented distributions extracted from the hierarchical features. In contrast, the purpose of constructing the teacher-student multi-exit architecture, construction methods, and knowledge transfer in this study are different from those in HSAKD. The purpose of constructing the multi-exit student network is to stimulate the learning ability of the student, to allow multiple stages of learning, and to use the attention mechanism to ensure a different focus for each stage of learning. These can ultimately achieve better ensemble learning. The purpose of constructing a teacher multi-exit network is to transfer knowledge comprehensively and allow the students to adapt to the current stage of learning. Additionally, HSAKD uses a complex knowledge definition, whereas we only use logit, which is a simple knowledge definition in the traditional approach.
## 2 Materials and Methods
### Data Analysis  
The data were read from the GNSS database and analysed within the statistical software R (GNU). The satellite data used, the frequency bands, and the carrier-to-noise density ratio C/N0 were extracted and analysed.  
Plausibility analysis of the positions provided by the smartphones was carried out. This was necessary because, for example, the Huawei P40, in some cases, first supplies the coordinates of the previous 'old' position in the NMEA string after switching on the position data option until a new position is known. In these cases, the 'old' data were not considered.  
The measured positions were adjusted along the longitude for the distance by which the smartphones deviated in the west-east direction on the holder. This was performed using a view table directly on the database. The adjusted distances are as follows: Mi 10 light: 70 cm; Huawei P40: 56 cm; Huawei P20: 42 cm; Samsung Xcover 4s b: 24 cm; Samsung Xcover 4s: 13 cm; Xiaomi Mi 8 pro: \(-\)14 cm; Samsung A7: \(-\)28 cm; Samsung S5: \(-\)42 cm; Samsung Xcover 4s a: \(-\)56 cm; and Xiaomi Mi 8: \(-\)70 cm.  
In this study, only the horizontal error was examined. This was calculated for each individual measurement at 1 s intervals, considering the position error in relation to the respective geodetic survey point. For the evaluation of the absolute error, various mathematical ratios were then derived. In addition to the arithmetic mean and standard deviation, different circular error probable (CEP) values (25, 50, 75, 95, and 99.7), the DRMS (distance root mean square), and the 2 DRMS (twice distance root mean square) of the error were derived. The circular error probable (CEP\({}_{x}x\)) is defined as the radius of a circle centered on the true value that contains xx% of the actual GNSS measurements. It is based on the 2D-error of the GNSS measurement: \(\sqrt{(x_{i}-x_{sp})^{2}+(y_{i}-y_{sp})^{2}}\), where \(x_{sp}/y_{sp}\) are the \(x/y\) coordinates of the specific survey point. Equation (1) shows the calculation of the DRMS.  
\[DRMS=\sqrt{\sigma_{x}^{2}+\sigma_{y}^{2}}=\sqrt{\frac{\sum_{i=1}^{N}(x_{i}-x_{ sp})^{2}}{N}+\frac{\sum_{i=1}^{N}(y_{i}-y_{sp})^{2}}{N}}. \tag{1}\]A Wilcoxon Rank Sum Test was carried out to test whether the different receivers significantly affected the results. The \(p\)-value was adjusted using the continuity correction method of 'holm' [86]. The significance threshold was set at 0.05.
# A New Defect Diagnosis Method for Wire Rope Based on CNN-Transformer and Transfer Learning
## 3 Methodology
### Transfer Learning Architecture  
As shown in Figure 7, the designed TL architecture for wire rope defect diagnosis mainly consists of four steps and two tasks: data preprocessing, image establishment, DCNNT model pre-training and fine-tuning, and source domain and target domain tasks. The details of the DCNNT are described in Section 3.4.  
TL architecture focuses on model pre-training and fine-tuning stages that are necessary and crucial for successful domain adaptation results. The dataset of feature images between source and target domains presents different data distributions and the number of the latter is usually scarcer in practical implementations due to real-life limitations. The definition of the source domain task is pre-training performed using high numbers of images obtained from the source domain with all categories (normal and defect); then, an original DCNNT model can be created (green highlight). Consequently, the target domain task is the fine-tuning of the internal parameters of the trained model using a part of the target domain  
Figure 6: Normal and defect feature images following data preprocessing and image establishment.  
Figure 7: Flowchart of transfer learning architecture.  
dataset to create a better transfer DCNNT model (orange highlight). The purpose of the proposed TL architecture is to use a small target domain dataset to rapidly create an effective model, instead of using time-consuming retraining methods. According to the TL strategy, the unfreeze training from lower to higher layers was implemented in this study. The relevant details can be observed in reference [33].
# A Method for Modeling and Testing Near-Real-Time System Scenarios
## 6 Discussion and Limitations  
The elements of the architectural description are located in the architectural views of the 1+5 model (see [PERSON] [40]). Two views were used. The first was the use cases view, where the functional requirements are placed. The second one was the logical view, where class design is presented along with UML sequence diagrams and UML state machine diagrams. The method also includes non-functional requirements expressed in the form of procedures, e.g., \"REQ 2001: Handover procedure\". However, this has not yet been properly formalized. In the 1+5 model, the contracts view is devoted to storing non-functional requirements. In the course of further work, a way of including this type of requirements in the contracts view and their direct connection with test cases will be modeled. The model also includes an integrated services view. It will be used to model the cooperation of the UAV and the ground control station. The article focuses on the implementation of the mission. Authentication, which is an important aspect of drone swarm security, is not yet considered in the method. In general, distributed identity management and authentication of individual drones is the topic of current research, in particular using blockchain technology (see [PERSON] et al. [41]). This seems to be an interesting area for potential extensions of the method.  
Proving the correctness of software is not limited only to the methods described in the article. Other popular and frequently encountered methods are formal proofs using linear temporal logic (LTL) and computation tree logic. Computation tree logic (CTL) is branching logic, which means that the algorithm model has a tree structure. It is used to formally verify software components, usually by applications called model checkers. One of them is NuSMV (symbolic model verifier; see [PERSON] et al. [42]). Model checkers check whether a given software module has safety properties. CTL can determine that certain initial or final conditions are met (e.g., all paths of the algorithm being checked end in one of the feasible final states). The general application of the CTL technique is to prove that all possible executions of a program avoid certain undesirable conditions. The safety property is verified by the model checker, which examines all possible transitions from program states that satisfy the initial condition and ensures that all such program executions satisfy this property. The interested reader can obtain additional information about this formal technique in the papers by [PERSON] et al. [43] or [PERSON] and [PERSON] [44]. Computation tree logic belongs to a class of temporal logics that includes linear temporal logic (LTL).
# Quantitative Assessment Methods of Early Enamel Caries with Optical Coherence Tomography: A Review
## 4 Quantitative Assessment Methods of Early Enamel Caries with OCT
### Quantitative Assessment Based on Scattering Coefficient  
Since the scattering properties of the enamel changes significantly after demineralization, the scattering coefficient can serve as an indicator of enamel lesion severity. A single scattering model combined with dynamic focusing can be used to determine the scattering coefficients of sound and carious enamel [81]. The OCT signal intensity is:  
\[I(z)\propto\sqrt{\frac{e^{-2\mu_{g}z}}{\left[1+\left(\frac{z-z_{cf}}{z_{R}} \right)^{2}\right]}} \tag{5}\]  
where \(z\) is the depth, \(z_{cf}\) is the focal plane position, \(z_{R}\) is Rayleigh length, and \(I(z)\) is the OCT depth profile.  
[PERSON] et al. [81] applied acid gel to demineralize enamel and scanned the sample in vitro before and after demineralization using 850-nm SD-OCT with an axial/lateral resolution 3/4 um. The estimated scattering coefficient is shown in Figure 3. The scattering coefficient increased with the demineralization time and leveled out at times greater than 120 s. Moreover, the average scattering coefficients were 4.60 mm\({}^{-1}\) and 8.46 mm\({}^{-1}\) for sound and carious enamel, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **OCT System** & **Sample Preparation** & **DOPU Result** \\ \hline [PERSON] et al. [74] & 1310-nm SD & & The significant DOPU contrast provided better identification of lesions in comparison with the reflectivity images. \\  & PS-OCT, axial/lateral resolution & 3 human molar teeth with different proximal lesions & 0.6–0.97 \\  & 15.1/15.6 μm, sweep rate 50 kHz. & & Method: demineralization for 49 days. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of quantifying enamel caries with degree of polarization.  
However, as mentioned above, the single scattering model used above is inaccurate for enamel caries. According to the optical properties of the caries lesion, it shows a significant growth in the scattering coefficient of enamel during the production of the initial lesion. Hence, if an accurate scattering coefficient is used for the quantitative assessment of early caries, early demineralization can be detected more sensitively. However, there are few studies using scattering coefficient to quantitatively evaluate early caries.
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 4 Algorithm Design
### Effective Distance  
As shown in Figure 3, due to the road restrictions and roadside buildings, the distribution of vehicles is in the form of \"pipeline\", and the aggregation distribution may occur. Hence, the validity of dummy locations further decreases. To ensure the validity of dummy locations, it is necessary to make the location distribution be uniform and dispersed, as shown in Figure 3b.  
Figure 2: Service request probability distribution. (**a**) with random dummy location selection algorithm; (**b**) with dummy location selection algorithm under road restrictions.  
In order to make the distribution of generated dummy locations be uniform, we define the effective distance between locations as the minimum distance between the current location and other locations in a location set. That is,  
\[d(\mathbf{r}_{i})=\min_{\mathbf{r}_{w}\in W,\mathbf{x}\
eq i}\lvert\mathbf{r}_{i},\mathbf{r}_{w} \rvert=\min_{\mathbf{r}_{w}\in W,\mathbf{x}\
eq i}\sqrt{\left(x_{i}-x_{w}\right)^{2}+ \left(y_{i}-y_{w}\right)^{2}}, \tag{4}\]  
where \(\mathcal{W}\) represents a location set, \(\mathbf{r}_{i}\) represents location \(i\) in set \(\mathcal{W}\), the corresponding coordinates is \(\left(x_{i},y_{i}\right)\), \(\mathbf{r}_{w}\) represents location \(w\) in set \(\mathcal{G}\), the corresponding coordinates is \(\left(x_{w},y_{w}\right)\), \(i=1,2,\ldots,\ \left\lvert\mathcal{W}\right\rvert,\)\(w=1,2,\ldots,\ \left\lvert\mathcal{W}\right\rvert,\)\(1\) is the number of elements in set \(\mathcal{W}\), and \(d(\mathbf{r}_{i})\) is the effective distance of \(\mathbf{r}_{i}\).  
From Figure 4, one finds that the larger the effective distance, the greater the spacing between vehicles, and the more dispersed the distribution.