esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The global COVID-19 pandemic certainly has posed one of the more difficult challenges for researchers in the current century. The development of an automatic diagnostic tool, able to detect the disease in its early stage, could undoubtedly offer a great advantage to the battle against the pandemic. In this regard, most of the research efforts have been focused on the application of Deep Learning (DL) techniques to chest images, including traditional chest X-rays (CXRs) and Computed Tomography (CT) scans. Although these approaches have demonstrated their effectiveness in detecting the COVID-19 disease, they are of huge computational complexity and require large datasets for training. In addition, there may not exist a large amount of COVID-19 CXRs and CT scans available to researchers. To this end, in this paper, we propose an approach based on the evaluation of the histogram from a common class of images that is considered as the target. A suitable inter-histogram distance measures how this target histogram is far from the histogram evaluated on a test image: if this distance is greater than a threshold, the test image is labeled as anomaly, i.e., the scan belongs to a patient affected by COVID-19 disease. Extensive experimental results and comparisons with some benchmark state-of-the-art methods support the effectiveness of the developed approach, as well as demonstrate that, at least when the images of the considered datasets are homogeneous enough (i.e., a few outliers are present), it is not really needed to resort to complex-to-implement DL techniques, in order to attain an effective detection of the COVID-19 disease. Despite the simplicity of the proposed approach, all the considered metrics (i.e., accuracy, precision, recall, and F-measure) attain a value of 1.0 under the selected datasets, a result comparable to the corresponding state-of-the-art DNN approaches, but with a remarkable computational simplicity.  
inter-histogram distance; Computed Tomography (CT); Chest X-ray (CXR); COVID-19; pneumonia; anomaly detection Article
###### Abstract  
The security of communication and computer systems is an increasingly important issue, nowadays pervading all areas of human activity (e.g., credit cards, website encryption, medical data, etc.). Furthermore, the development of high-speed and light-weight implementations of the encryption algorithms is fundamental to improve and widespread their application in low-cost, low-power and portable systems. In this scientific article, a high-speed implementation of the AES-128 algorithm is reported, developed for a short-range and high-frequency communication system, called Wireless Connector; A Xilinx ZCU102 Field Programmable Gate Array (FPGA) platform represents the core of this communication system since manages all the base-band operations, including the encryption/decryption of the data packets. Specifically, a pipelined implementation of the Advanced Encryption Standard (AES) algorithm has been developed, allowing simultaneous processing of distinct rounds on multiple successive plaintext packets for each clock period and thus obtaining higher data throughput. The proposed encryption system supports 220 MHz maximum operating frequency, ensuring encryption and decryption times both equal to only 10 clock periods. Thanks to the pipelined approach and optimized solutions for the Substitute Bytes operation, the proposed implementation can process and provide the encrypted packets each clock period, thus obtaining a maximum data throughput higher than 28 Gbit/s. Also, the simulation results demonstrate that the proposed architecture is very efficient in using hardware resources, requiring only 1631 Configurable Logic Blocks (CLBs) for the encryption block and 3464 CLBs for the decryption one.  
Keywords:field programming gate array; encryption; advanced encryption standard; wireless connector; 5G communication; experimental testing +
Footnote †: journal: Accepted: 10 October 2020
## 5 Conclusions
Equation (49) is written at times \(t\) and \(t+\Delta t\), the first equation is multiplied by \(\left(1-\beta\right)\) and the second equation is multiplied by \(\beta\) to give  
\[\left(1-\beta\right)\left(\left[K_{1}^{t}\right]\left\{\dot{\theta}\right\}_{ t}+\left[K_{2}^{t}+K_{3}^{t}\right]\left\{\theta\right\}_{t}\right)=\left(1- \beta\right)\left\{F^{t}\right\}_{t} \tag{11}\]  
\[\beta\left(\left[K_{1}^{t}\right]\left\{\dot{\theta}\right\}_{t+\Delta t}+ \left[K\right]\left\{\theta\right\}_{t+\Delta t}\right)=\beta\left\{F^{t} \right\}_{t+\Delta t} \tag{12}\]  
These two equations are added and Equation (10) is used to eliminate the time derivatives of \(\left\{\theta\right\}\). The resulting equation is solved for \(\left\{\theta\right\}_{t+\Delta t}\), which yields  
\[\left(\frac{1}{\Delta t}\left[K_{1}^{t}\right]+\beta\left[K_{2}^{t}+K_{3}^{t} \right]\right)\left\{\theta\right\}_{t+\Delta t}=\left(\frac{1}{\Delta t} \left[K_{1}^{t}\right]-\left(1-\beta\right)\left[K_{2}^{t}+K_{3}^{t}\right] \right)\left\{\theta\right\}_{t}+\left(1-\beta\right)\left\{F\right\}_{t}+ \beta\left\{F^{t}\right\}_{t+\Delta t} \tag{13}\]  
For \(\beta=\frac{1}{2}\), we obtain the Crank-Nicolson algorithm, which is unconditionally stable, regardless of the value of \(\Delta t\).
## 4 Result and Discussion
### Glonass PPP for Different End Users
Usually, the coordinates of the receivers located in the time keeping laboratory are known values. Unlike the time keeping laboratory, they may be regarded as estimation parameters for the time users. To assess the influence of the IFCBs on the GLONASS PPP without coordinate-fixed, the STDs of the series of the one-day and ten-day arc solutions in different schemes are depicted in Figures 11 and 12, respectively, without coordinate-fixed. Table 7 presents the STDs of the GLONASS PPP at the 95 and 68% levels for the one-day and ten-day arc solutions, respectively. Similar to the GLONASS PPP with coordinate-fixed, the IFCB2 performs best. Compared with IFCB0, the STD of IFCB2 is reduced by 30.59% going from 0.304 to 0.211 ns, and by 10.46% going from 0.411 to 0.368 ns in one-day arc solutions and ten-day arc solutions at the 95% level, respectively. Besides, the different users (with and without coordinates fixed) are to be the same level. It is of great interest found that the improvement of the IFCBs is not obvious in the one-day arc solutions at the 68% level. We take this fact as an indication that the STDs of one-day arc solutions mainly depend on the accuracy of carrier phase observations. We further found that the STD of 10-day arc solutions is more notably improved at the 68% level than at the 95% level. This is not unexpected given that few bad results are obtained when considering IFCBs, and we do not attempt to cover all of them; rather, without a loss of generality and for the sake of clarity, the bad results have been depicted in Figures 8 and 12. In the 266 tests of 10-arc solutions, there are 34 and 10 bad results for IFCB1 and IFCB2, respectively. These results further demonstrate that IFCB2 performs best. Since observation residuals contain measurement noise and other unmodeled errors, they can be used as important indicators to evaluate the PPP model. The pseudorange observation residuals at the two selected IGS stations (AGGO and BRUX) for the GLONASS PPP processing on DOY 227, 2017, are depicted in Figure 13. In the figure, the RMS statistics of each satellite in the different schemes are displayed in each panel. Neglecting GLONASS IFCB, larger RMS values can be observed, compared to the pseudorange residuals of IFCB1 and IFCB2. Overall, the statistical results clearly demonstrate that IFCB2 has the smallest proper handled in the IFCB2 model.
## 4 Media Access Control
### Cooperative Sensing
#### 4.2.2 Sensing Scheduling  
MAC does not only coordinate the medium access for regular data transmission, but also for spectrum sensing when cooperation is involved [73]. Akin to medium access which can be either random or synchronous, the spectrum sensing can also be scheduled to take place following either of the two approaches [74]. Because of the limitations of the random access approach in typical applications, such as dedicated antennas, PHY layer techniques and the size of cooperation, many adaptive sensing networks adopt synchronous spectrum sensing, which is essential for those employing energy detection at PHY [20].  
The synchronization for cooperative sensing introduces additional complexities, and one of the most notable solutions is the adoption of time-division multiplexing (TDMA), which divides time-space into indefinite series of consecutive timeslots. Inspired by the TDMA-based channel-hopping technique described in [49, 75, 76], the work in [3, 77] were able to conveniently coordinate the cooperative activity by assigning timeslots to tasks, such as suspending normal communication, in order to create quiet periods during which the channel noise floor can be sensed without interference among fellow motes.
## 3 Results
### Droplet Size Prediction of Overlapping Area for Twin Nozzles Condition  
In order to predict the droplet size of the overlapping area for twin nozzles condition, we established a quantitative model based on different machine learning models. As shown in Figure 9, 'a' and 'b' represents the VMD value of nozzle A and B for the same measuring point, which were obtained by the REGRESS model of TEEJET XR110015 VS nozzle. 'c' represents the actual VMD value of twin nozzle measured in the corresponding measuring point of 'a' and 'b'. In order to achieve a better fitting effect of two independent variables and dependent variables with more options, we used Levenberg-Marquardt + Universal Global Optimization (LM-UGO) instead of REGRESS. Software package 1 stOpt was used to realize LM-OGO. Quantitative models based on LM-UGO, LS-SVM, ELM, and RBFNN were built to quantify the relationship between 'a', 'b' and 'c'. The numbers of the calibration set and prediction set were 66 and 22, respectively with a scale of 3:1.  
Figure 7: The VMD model of twin nozzles based on REGRESS. (**a**) spray height = 1 m; (**b**) spray height = 1.5 m; (**c**) spray height = 2 m; (**d**) nozzle spacing = 0.5 m; (**e**) nozzle spacing = 0.6 m; (**f**) nozzle spacing = 0.7 m.  
Table 6 shows the results of four models. The scatter plot of measured values vs. Predicted values of twin nozzle VMD are shown in Figure 10. For VMD, the prediction of overlapping area, RBFNN achieved the best performance with an \(Rc^{2}\) value of 0.9567 and RMSE of 3.2595 \(\upmu\)m, indicating the good potential of this machine learning method. The results obtained by LS-SVM and ELM methods showed expected performance, LS-SVM with an \(Rc^{2}\) value of 0.9116, RMSEP of 3.5471 \(\upmu\)m, and ELM with an \(Rc^{2}\) value of 0.8120, RMSEP of 3.8389 \(\upmu\)m. The large RMSE values and the scatter  
Figure 8: Scatter plot of measured deposition values vs. predicted deposition values of twin nozzles. (a) REGRESS; (b) LS-SVM; (c) ELM; (d) RBFNN.  
Figure 9: Schematic diagram of corresponding measuring points of overlapping area.  
points deviated from the fitting line indicated the LM-UGO was not a good candidate for quantitative modeling of VMD relationship. The results indicated that the regression model of a single nozzle could be used to predict the droplet size of the overlapping area for twin nozzles condition.
# A Novel STDM Watermarking Using Visual Saliency-Based JND Model
## 2 Perceptual Spread Transform Dither Modulation  
STDM is an important extension of quantized index modulation and uses dither modulation (DM) to modulate the projection of host vector along a given direction. It can provide superior performance compared to DM. It has both the effectiveness of QIM and the robustness of a spread-spectrum system.  
STDM differs from regular DM. The host vector \(x\) is first projected onto the random direction vector \(u\) to obtain the projection \(x_{u}\), so we can use the traditional dither modulation to embed the watermark bit \(m\) to modulate \(x_{u}\). Finally, we get the watermarking information \(y\):  
\[y=x+(Q(x^{T}u,m,\Delta,\delta)-x^{T}u)\cdot u,m\in(0,1) \tag{1}\]  
where the quantization modulation \(Q(\cdot)\) is expressed as  
\[Q(x^{T}u,m,\Delta,\delta)=\Delta\cdot round\left(\frac{x_{u}+\delta}{\Delta} \right)-\delta,m\in(0,1) \tag{2}\]  
where \(m\) represents the watermark bit that is generated by the pseudo random sequence with number \"0\" and \"1\". \(\delta\) is the dither signal corresponding to the message bit \(m\), which is a pseudo-random number distributed uniformly in \([-\Delta/2,\Delta/2]\). \(\Delta\) is the quantization step that will be detailed later.  
In the extraction procedure, the received vector \(y^{{}^{\prime}}\) can be attacked and projected onto the direction vector \(u\) such that we can get \(y^{{}^{\prime}}_{u}\). Then the watermark \(m^{{}^{\prime}}\) is detected according to the minimum distance detector as follows:  
\[m^{{}^{\prime}}=\arg\min_{b\in\{0,1\}}\mid y^{{}^{\prime}}_{u}-Q(y^{{}^{\prime} }_{u},b,\Delta,\delta)\mid \tag{3}\]  
In general, the motivation of using a perceptual JND model to perform the STDM watermarking scheme is to embed the message bit \(m\) into the host signal \(x\). The error introduced by the quantization should not exceed the distortion visibility thresholds (slack) \(s\). Therefore, the watermark will not become perceptible. Here, we provide a new visual saliency-based JND model to the s associated with each DCT coefficient within the typical STDM watermarking framework.
# Abnormal Ship Behavior Detection Based on AIS Data
## 1 Introduction
Since the occurrence of marine accidents can usually be attributed to the abnormal behavior of ships, the characteristics of marine accidents in this study were abstracted from three dimensions--spatial attributes, temporal attributes, and thematic attributes--to obtain the data expression of the abnormal behavior of ships. For example, on 14 May 2021, the accident investigation report of the Maritime Safety Administration of the People's Republic of China showed that a ship drifted 2 km away from its original route due to a wind disaster and then finally capsized and sank. Based on the AIS data, it can be observed that the spatial position of the ship deviated from the trajectory point group after the wind accident. The schematic diagram is shown in Figure 1. Therefore, it is feasible and necessary to detect abnormal behavior of ships based on AIS data. On the one hand, it can reduce the workload of maritime surveillance operators and improve the efficiency of maritime surveillance [10]. On the other hand, it has guiding significance for the relevant departments to formulate management measures such as the planning of navigation routes and maritime traffic control. Additionally, it has great socioeconomic and environmental benefits. The purpose of this study was to model and mine the typical motion patterns by analyzing the behavior of ships at sea and to detect and evaluate the abnormal behavior of ships based on the AIS data in the framework of the discovery patterns from the perspectives of spatial attributes and thematic attributes to comprehensively express the maritime traffic situation. First, we extracted the information of the maritime routes based on the AIS data extension main graph structure learning strategy to generate a data-driven representation of the real navigation routes. Then, we detected and evaluated abnormal ship behavior based on [PERSON]'s criterion and the isolated forest algorithm for the spatial attributes and thematic attributes, respectively. The remainder of this paper is organized as follows. Section 2 is a review of the relevant research. Section 3 introduces the study area and the data and describes the detection method for abnormal behavior based on AIS data. Section 4 provides the experimental results and analysis. Section 5 summarizes the content of this article and puts forward future outlooks.
###### Abstract  
Background: The aim of this study is to evaluate and compare, through bidimensional cephalometry, skeletal and dental changes obtained from a rapid maxillary expander (RME) and a Ni-Ti leaf spring expander (Leaf) and compare them with an untreated control group. Methods: Records consisted of lateral cephalograms obtained before and after maxillary expansion of patients that underwent orthodontic treatment at the Department of Biomedical, Surgical and Dental Sciences. The Leaf expander group consisted of 9 males (mean age = 7.5 \(\pm\) 0.9 years old) and 11 females (mean age = 8.2 \(\pm\) 0.6 years old). The RME group of the present study was composed of 11 males (mean age = 7.8 \(\pm\) 0.6 years old) and 12 females (mean age = 8.1 \(\pm\) 0.5 years old). Digital cephalograms were traced using Dolphin Imaging software v.11.1 (Dolphin Imaging and Management Solutions; Los Angeles, CA, USA), which calculated all reported measurements. Each subject was assigned a random identification number, and the examiner was blinded to the subject when measuring. The difference between the two experimental times in all groups was evaluated using the Student's \(t\)-test for dependent variables. The difference between the two evaluation times in each group for all the variables was used to perform a one-way ANOVA test between the three groups. Results: No statistically significant difference was noted, apart from the angle between the upper incisor and the SN and PP planes, which showed an average decrease of 3.25 and 2.55, respectively, and the angle between the lower incisors and the mandibular plane, which showed an average increase of 2.85 degrees. The one-way ANOVA showed no statistically significant difference between the three groups. Conclusions: It appears that the leaf expander and the RME present similar effects such as dental and skeletal changes.  
m _Applied_
## 3 Results and Discussion
### Influence of NH\({}_{3}\) and NO\({}_{x}\) on SOA at High RH (Experiments: Tol-5 and Tol-6)  
3.3.1. Influence of NH\({}_{3}\) and NOx on SOA Mass at 80% RH  
In experiments Tol-5 and Tol-6 (see Tables 1 and 2), the RH was fixed at 80% and the NOx concentration was fixed at 61 ppb and 360 ppb, respectively, corresponding to [NO\({}_{\rm X}\)]/[AHC] ratios of 0.05 (NP SOA regime) and 0.31 (NR SOA regime). The input NH\({}_{3}\) concentration was ramped from 42 ppb, corresponding to an [NH\({}_{3}\)]/[AHC] ratio of 0.04, to 203 ppb, corresponding to an [NH\({}_{3}\)]/[\(\Delta\)HC] ratio of 0.18 at 80% RH. The secondary brown carbon (SBrC) formation in these experiments showed unique trends that were not previously observed. First, for low NOx scenario (NP), the SOA mass against [NH\({}_{3}\)]/[\(\Delta\)HC] ratio increase showed a large peak at [NH\({}_{3}\)]/[\(\Delta\)HC] \(\sim\)0.13 as shown in Figure 8. It is likely due to the formation of ammonium nitrate in toluene-NH\({}_{3}\)-OH system at low NOx reported by [PERSON] et al. [47], where addition of NH\({}_{3}\) enhanced the number concentration, average SOA diameter, and extinction and scattering coefficients due to the formation of significant amounts of condensable ammonium nitrate and nitrogen-containing (NOC) compounds.  
On the other hand, SOA mass was relatively lower for a high NOx scenario that in a low NOx scenario and varied insignificantly (apparently constant \(\sim\)435 ug m\({}^{-3}\)) when  
Figure 8: SOA mass as a function of the ([NH\({}_{3}\)]/[AHC]) ratio at 80% RH under low and high NOx scenarios. Blue and red dotted lines are the eye guiding lines for low and high NOx scenarios, respectively.  
[NH\({}_{3}\)]/[\(\Delta\)HC] increased as shown in Figure 8. This is consistent with the SOA mass formed in NP and NR regimes in experiments Tol-1 to Tol-4. These suggest that overall NH\({}_{3}\) had little influence on SOA mass at high NOx scenario (e.g., NR regime).
## 3 Results  
### Oral Microbiota  
#### 3.1.1 Bacterial Composition Ratio  
Sequences were clustered into 13 phyla, 27 classes, 39 orders, 65 families, 135 genera, and 435 species. In all bacterial groups identified at each level, from the phylum to the species level, slight changes were observed at T0, T1, and T2. However, none of the bacteria exhibited a significant increase or decrease (\(p>0.05\)). The six major phyla at the phylum level (Firmicutes, Bacteroidetes, Fusobacteria, Proteobacteria, Saccharomyces, and Actinobacteria) accounted for 99.07% at T0, 98.78% at T1, and 98.72% at T2, and no significant changes were observed. The differences in T0, T1, and T2 between the major oral bacteria at the phylum and genus levels are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **T0** & **T1** & **T2** &
\begin{tabular}{c} **T0-T1-T2** \\ **ANOVA FDR** \\ **\(p\)-Value** \\ \end{tabular} \\ \hline \multicolumn{5}{c}{(a) Phylum level} \\ \hline Firmicutes & 29.86\% & 29.93\% & 29.01\% & 0.70 \\ \hline Bacteroidetes & 27.49\% & 27.34\% & 27.04\% & 0.95 \\ \hline Fusobacteria & 11.85\% & 11.63\% & 12.64\% & 0.70 \\ \hline Proteobacteria & 19.64\% & 19.39\% & 19.65\% & 0.99 \\ \hline Saccharomyces (TM7) & 3.21\% & 3.38\% & 3.21\% & 0.93 \\ \hline Actinobacteria & 7.01\% & 7.10\% & 7.18\% & 0.98 \\ \hline \multicolumn{5}{c}{(b) Genus level} \\ \hline _Streptococcus_ & 13.51\% & 12.98\% & 13.18\% & 0.83 \\ \hline _Prevetella_ & 15.91\% & 16.00\% & 15.51\% & 0.91 \\ \hline _Fusobacterium_ & 6.83\% & 6.85\% & 6.58\% & 0.89 \\ \hline _Lepotrichia_ & 5.01\% & 4.78\% & 6.05\% & 0.70 \\ \hline _Haempophilus_ & 5.12\% & 5.16\% & 5.28\% & 0.96 \\ \hline _Vellomella_ & 5.54\% & 6.12\% & 5.49\% & 0.70 \\ \hline _Saccharibacterium_ & 2.08\% & 2.40\% & 2.18\% & 0.78 \\ \hline _Alloprocutedella_ & 5.07\% & 4.90\% & 4.42\% & 0.74 \\ \hline _Neisseria_ & 8.06\% & 8.28\% & 7.22\% & 0.70 \\ \hline _Porphymomas_ & 3.82\% & 3.95\% & 4.26\% & 0.89 \\ \hline _Rothia_ & 4.15\% & 4.11\% & 3.88\% & 0.91 \\ \hline _Gemella_ & 1.95\% & 1.59\% & 1.65\% & 0.70 \\ \hline _Granmilicatella_ & 1.40\% & 1.26\% & 1.14\% & 0.70 \\ \hline _Serratia_ & 3.33\% & 3.17\% & 1.64\% & 0.76 \\ \hline _Campylobacter_ & 1.57\% & 1.68\% & 2.02\% & 0.70 \\ \hline _Orluterium_ & 1.33\% & 1.65\% & 1.18\% & 0.70 \\ \hline _Capnocytophaga_ & 2.02\% & 1.73\% & 1.82\% & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Differences in T0, T1, and T2 of major oral bacteria at the phylum and genus levels.