esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Rhythm-Level Training Phase  
This section delves into the rhythm-level training phase, illustrated in Figure 2b. In this stage, the PI-BSM is generated, and the rhythm classifier is trained using PI-BSM as an input. We utilize a target dataset \(D_{t}=\{(\mathbf{x}_{i}^{t},\mathbf{r}_{i}^{t},\mathbf{p}_{i}^{t})\}_{i=1}^{m}\), where m represents the number of rhythm samples. The target dataset \(D_{t}\) comprises the 1D ECG signal \(\mathbf{x}_{i}^{t}\) with a duration of 10 s, along with corresponding rhythm annotations \(\mathbf{r}_{i}^{t}\) and patient identification label \(\mathbf{p}_{i}^{t}\). \(\mathbf{x}_{i}^{t}\) is segmented with windows of size 2.4 s at intervals of 25 milliseconds, resulting in 360 beat segmentations for each 10 s \(\mathbf{x}_{i}^{t}\) sample. The segmented signal undergoes denoising and transformation into a 2D scalarogram image using CWT.  
Once the preprocessing stage is complete, scalogram images are fed into the pre-trained PI-beat classifier to generate d-dimensional beat-score vectors. Here, d is equal to the number of beat classes in the source data. Through the pre-trained PI-beat classifier, 360 beat-score vectors are generated for each \(\mathbf{x}_{i}^{t}\). Arranging these beat-score vectors in chronological order produces a beat-score map, called the PI-BSM for a 10 s ECG signal.  
Figure 4 illustrates the PI-BSMs for different rhythm types, where (a) represents AFib (atrial fibrillation), (b) GSVT (grouped supraventricular tachycardia), (c) SB (sinus Bradycardia), and (d) SR (sinus rhythm). The PI-BSMs for each rhythm type reflect reasonably well the distinct characteristics of ECG signals with different rhythm types. Table 1 provides a summary of the configuration of the rhythm classifier using SE-ResNet.  
Figure 4: _Cont._
# Secure Partitioning of Cloud Applications, with Cost Look-Ahead
## 2 Related Work
### Information-Flow Security  
Information-flow security assigns labels to variables of a program to follow its data flow in order to verify the desired properties (e.g., non-interference [37]) and avoids covert channels. Labels are ordered in a security lattice to represent the relations of the labels from the highest ones (e.g., top secret) to the lowest ones (e.g., public data). Security lattices can be arbitrarily complex and define total or partial orders, e.g., a three-label total order that represents data secrecy from top secret to low secret is represented as top \(\succ\) medium \(\succ\) low.  
Some approaches use information-flow security to address problems that are complementary to our techniques, for example, checking the correct labelling of software or monitoring inputs and storage accesses by the applications. [PERSON] and [PERSON] [38] propose a framework to deliver Information-Flow-Control-as-a-Service (IFCaaS) in order to protect the confidentiality and integrity of the information flow in a Software-as-a-Service application. The framework works as a trusted party that creates a call graph of an application from the source code and applies information-flow security based on dependence graphs to detect violations of the non-interference policy.  
At the Function-as-a-Service (FaaS) level, [PERSON] et al. [39] present an approach for dynamic information-flow control, monitoring the inputs of serverless functions to tag them with suitable security labels in order to check access to data storage and communication channels to prevent leaks of the data managed by the functions. Similarly, [PERSON] et al. [40] propose to monitor serverless functions by starting to learn the information flow of an application, showing the detected flows to the developers and enforcing the selected ones.  
In the Cloud-Edge continuum, our previous work [41] exploits information-flow security to place FaaS orchestrations on Fog infrastructures. Functions are labelled with security types according to the input received and infrastructure nodes are labelled according to user-defined security policies. The placements are considered eligible if every node involved has a security type greater than or equal to the security type of all the hosted serverless functions. Developers assign a level of trust to infrastructure providers that concurs to rank the eligible placements.  
Differently from our approach, all these proposals but [38] consider Cloud-Edge providers reliable. Recently, a few proposals have leveraged information-flow analyses to enforce data security in Cloud applications when the Cloud-Edge provider is untrusted. For example, [PERSON] et al. [42] have extended Java with information-flow annotations that allow verification of whether partitioning an application into components that run inside and outside an SGX enclave violates confidentiality security policies. In this proposal, partitioning is decided by the programmer.
# Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
###### Abstract  
In recent years, discriminative correlation filters (DCF) based trackers have been widely used in mobile robots due to their efficiency. However, underground coal mines are typically a low illumination environment, and tracking in this environment is a challenging problem that has not been adequately addressed in the literature. Thus, this paper proposes a Low-illumination Long-term Correlation Tracker (LLCT) and designs a visual tracking system for coal mine drilling robots. A low-illumination tracking framework combining image enhancement strategies and long-time tracking is proposed. A long-term memory correlation filter tracker with an interval update strategy is utilized. In addition, a local area illumination detection method is proposed to prevent the failure of the enhancement algorithm due to local over-exposure. A convenient image enhancement method is proposed to boost efficiency. Extensive experiments on popular object tracking benchmark datasets demonstrate that the proposed tracker significantly outperforms the baseline trackers, achieving high real-time performance. The tracker's performance is verified on an underground drilling robot in a coal mine. The results of the field experiment demonstrate that the performance of the novel tracking framework is better than that of state-of-the-art trackers in low-illumination environments.  
visual object tracking low illumination; image enhancement; computer vision; mobile drilling robot; coal mine robot +
Footnote †: journal: Medical Imaging
## 4 Discussion
### Trophic Status  
The calculated trophic indexes for other studies within hydropower plants (Table 15) indicate that the greater the damming height and capacity, the greater the impact on the trophic status of the water [59; 60]. In the case of hydropower plants with a damming height of fewer than 5 m and a capacity of 0.095-0.6 MW, the change in value was respectively: TSI\({}_{\text{P}}\)--2.79% and TLI--1.26%; for a damming height of 5-15 m and a capacity of 0.15-1.3 MW, the change in value was respectively:10.84% and 5.73%; and for a damming height of 105 m and a capacity of 900 MW, the change in value was respectively: 41.75% and 16.79%. Contrary to the research presented here, storage hydropower plants deteriorated in the points downstream compared with the upstream structures. Overall results indicate either mesotrophic or eutrophic (taking TSI\({}_{\text{P}}\) into account) or supertrophic (taking TLI into account). In five out of six cases, the factor deteriorating the result of the calculated TLI was TN (consistent with the results of these studies), and in one case the factor was TP.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Trophic** \\ **Index** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Point** \\ **Location** \\ \end{tabular} } & \multicolumn{3}{c}{**References**} \\ \cline{3-5}  & & **[PERSON] et al., 2015 [59] & **[PERSON] et al., 2019 [60]** \\ \hline \multirow{2}{*}{TSI\({}_{\text{P}}\)} & Upstream HP & 58.05 & 58.05 & 53.20 \\ \cline{2-5}  & Downstream HP & 59.67 & 64.34 & 75.41 \\ \hline \multirow{2}{*}{TLI\({}_{\text{P}}\)} & Upstream HP & 4.96 & 4.96 & 4.53 \\ \cline{2-5}  & Downstream HP & 5.10 & 5.51 & 6.48 \\ \hline \multirow{2}{*}{TLI\({}_{\text{N}}\)} & Upstream HP & 6.19 & 7.60 & 5.95 \\ \cline{2-5}  & Downstream HP & 6.19 & 7.76 & 5.76 \\ \hline \multirow{2}{*}{TLI} & Upstream HP & 5.57 & 6.28 & 5.24 \\ \cline{2-5}  & Downstream HP & 5.64 & 6.64 & 6.12 \\ \hline \multicolumn{5}{c}{**Information about hydropower plants**} \\ \hline Location of hydropower plants & Lithuania & China \\ \hline Type of hydropower plants & & storage \\ \hline Hydropower plants’ capacity & 0.095–0.6 MW & 0.15–1.3 MW & 900 MW \\ \hline Damming height & \textless{}5 m & 5–15 m & 105 m \\ \hline \hline \end{tabular}
\end{table}
Table 15: Calculated results of trophic indices for other hydropower plants (HPs).
# Secrecy Enhancing Scheme for Spatial Modulation Using Antenna Selection and Artificial Noise
###### Abstract  
In this paper, we present a new secrecy-enhancing scheme for the spatial modulation (SM) system, by considering imperfect channel state information (CSI). In the proposed scheme, two antennas are activated at the same time. One of the activated antennas transmits information symbols along with artificial noise (AN) optimized under the imperfect CSI condition. On the other hand, the other activated antenna transmits another AN sequence. Because the AN are generated by exploiting the imperfect CSI of the legitimate channel, they can only be canceled at the legitimate receiver, while the passive eavesdropper will suffer from interference. We derive the secrecy rate of the proposed scheme in order to estimate the performance. The numerical results demonstrated in this paper verify that the proposed scheme can achieve a better secrecy rate compared to the conventional scheme at the same effective data rate.  
Keywords:spatial modulation (SM); antenna selection; artificial noise (AN); channel state information (CSI); physical layer security (PLS); secrecy rate +
Footnote †: journal: Energy Communications Letters
# An FPGA Design with High Memory Efficiency and Decoding Performance for 5G LDPC Decoder
## 4 Results and Discussion
### Performance Results  
We consider the BG1 matrix with code rates of 1/2, 2/3, codeword lengths N of 8832, 6720, and the expansion factor Z of 192. The HOMS is set with \(\beta=0.5\) and \(\delta=0.375\)[19]. For comparison purposes, the performances of the NMS (\(1/\alpha=0.75\)) [14], OMS (\(\beta=0.5\)) [14], IOMS ( \(\gamma=0.875\) and \(\eta=0.5\)) [16], SMA-MSA ( \(\alpha=0.25\) and \(\gamma=0.75\)) [17], S2 DS [18], and VOMS ( \(\beta=0.5\) and \(\tau=0.875\)) [19] algorithms are also evaluated.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Conventional CN-MB** & \multicolumn{4}{c}{**Proposed CN-MB**} \\ \hline
**Width** & **Depth** & **Total** & **Width** & **Depth** & **Total** \\ \hline
30Z & 24 & 720Z & 27Z & 24 & 648Z \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between the proposed and conventional CN-MB.  
Figure 7: Decompress block architecture.  
First, to demonstrate the modification of the CTV messages, we have compared the CTV messages among the HOMS, BP, and MS algorithms along with various reported MS-based decoding algorithms, as illustrated in Figure 8.  
In this demonstration, we calculate four CTV messages with the same data inputs for some algorithms such as the BP, MS, OMS, NMS, IOMS, SMA-MSA, VOMS, and HOMS. These four CTV messages correspond to Figure 8a,b,c and d, respectively. There are 11 tested samples. Looking closely, we can see that the gap between the HOMS and the BP algorithm is large. There are two reasons for this overestimation: first, the HOMS algorithm is based on the MS algorithm that has self-existing errors; second, the updated CTV messages in the HOMS algorithm are calculated only based on the \(min1\) value, while the \(min2\) is approximated. To overcome this disadvantage, another offset factor is applied to the VTC messages of the HOMS in variable node processing. A solution is offered to modify VNU instead of continuing to modify CNU because the CNU is one of the most complex blocks, therefore, adding a modified offset factor may increase the hardware complexity. Meanwhile, the VNU/APB block is very simple, using only adders/subtractors. Next, to verify the error correction capacity of these algorithms, we proceed with the Monte Carlo simulations for several 5G LDPC codes. Some simulation results are shown in Figures 9 and 10.  
Figure 8: Comparison of the Check Node unit outputs among various LDPC decoding algorithms.  
It can be seen that at the target BER of \(10^{-8}\), the HOMS algorithms improve the decoding gain up to 0.15 dB compared to the NMS, IOMS, SMA-MSA, S2 DS, and SOMS algorithms.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Biomarkers** & **CON** & **GUA** & **WBV-E** & **GUA+WBV-E** & \(p\)** & \(\epsilon^{2}\) \\ \hline Albumin (g/L) & \(34.7\pm 1.0\) & \(34.2\pm 1.3\) & \(36.2\pm 2.5\) & \(37.4\pm 2.3\) & 0.1138 & 0.3308 \\ Calcium (mmol/L) & \(2.38\pm 0.07\) & \(2.36\pm 0.02\) & \(2.52\pm 0.20\) * & \(2.48\pm 0.08\) * & 0.0449 & 0.4475 \\ Creatinine (\(\mu\)mol/L) & \(39.78\pm 8.84\) & \(37.13\pm 3.54\) & \(40.66\pm 7.96\) & \(37.13\pm 3.54\) & 0.83241 & 0.0484 \\ Cholesterol (mmol/L) & \(1.07\pm 0.15\) & \(1.21\pm 0.21\) & \(1.07\pm 0.23\) & \(1.16\pm 0.13\) & 0.5365 & 0.1209 \\ Glucose (mmol/L) & \(6.22\pm 0.45\) & \(5.69\pm 0.44\) & \(5.76\pm 0.18\) & \(5.85\pm 0.94\) & 0.4270 & 0.1853 \\ HDL (mmol/L) & \(1.09\pm 0.07\) & \(1.16\pm 0.15\) & \(1.07\pm 0.17\) & \(1.13\pm 0.11\) & 0.7040 & 0.0781 \\ Magnesium (mmol/L) & \(1.10\pm 0.14\) & \(1.00\pm 0.06\) & \(1.27\pm 0.36\) & \(1.17\pm 0.11\) & 0.0661 & 0.3995 \\ Direct Bilirubin (\(\mu\)mol/L) & \(0.34\pm 0.17\) & \(0.51\pm 0.17\) & \(0.34\pm 0.17\) & \(0.34\pm 0.17\) & 0.2657 & 0.2330 \\ Total Bilirubin (\(\mu\)mol/L) & \(0.86\pm 0.51\) & \(0.68\pm 0.51\) & \(0.51\pm 0.17\) & \(0.68\pm 0.34\) & 0.9386 & 0.0291 \\ Total Protein (g/L) & \(57.7\pm 2.5\) & \(59.0\pm 1.9\) & \(62.2\pm 4.0\) & \(61.8\pm 3.8\) & 0.1185 & 0.3257 \\ Triglyceride (mmol/L) & \(0.47\pm 0.26\) & \(0.52\pm 0.16\) & \(0.42\pm 0.14\) & \(0.38\pm 0.11\) & 0.6177 & 0.0993 \\ Urea (mmol/L) & \(7.84\pm 1.01\) & \(7.07\pm 0.32\) & \(7.47\pm 0.42\) & \(7.57\pm 0.53\) & 0.3153 & 0.1968 \\ \hline \end{tabular}
\end{table} 
Table 2: Blood biomarkers concentrations of the animals submitted to some interventions. The study of the effect of guarana extract and/or WBV on the food intake and body mass for 6-weeks of intervention, revealed that the animals treated with guarana and WBV (the GUA + WBV-E group) had a significant decrease (\(p<0.05\)) in food intake as compared with the CON group. However, the animals treated with guarana (the GUA group) had no statistical decrease in food intake as compared with the CON group but had a feed consumption higher than that of the GUA + WBV-E group. The animals treated with only WBV-E (the WBV-E group) had a statistical increase in food intake as compared with the CON group (Figure 4). The \(\varepsilon^{2}\) (not shown in the figure) was 0.1216, indicating a small relationship. Table 4 shows that the body mass of animals from all groups increased (about 20%) during the period of the interventions but not significantly (\(p<0.05\)) among the groups. The values of \(\varepsilon^{2}\) varied from 0.1294 to 0.3657 (small relationship). Figure 4: Food intake (g) of animals exposed to different treatments (CON, control; GUA, group treated with guarana; WBV-E, group exposed to mechanical vibration generated in platform, GUA + WBV-E, group treated with guarana and exposed to mechanical vibration). Adjusted \(p\) values (Kruskal–Wallis) were considered significant at \({}^{*}p<0.05\), and \({}^{***}p<0.001\).
## 3 Data and Methods
### Research Methods  
#### 3.2.1 Net Primary Productivity  
NPP is defined as the rate of atmospheric carbon uptake through the process of net photosynthesis minus dark respiration [29; 30]. As the most important part of the surface carbon cycle, NPP can not only directly reflect the productivity of plant communities in the natural environment and show the quality of the terrestrial ecological system but also can define the main factor of the carbon source/sink and the process of regulating ecosystem [31]. The recent research on NPP has mainly focused on the measurement of NPP and the calculation model of NPP, and some research has undertaken the dynamic simulation of regional NPP [32].
## 2 Problem Description and Methodology  
### Optimal Design of LEO Navigation Constellation  
#### 2.1.1 Configuration of Constellations  
The Walker constellation is characterized by three elements: the total number of satellites \(N\), the orbital planes P, and the phase factor \(F\). The satellite parameters are defined by six elements: semi-major axis \(a\), eccentricity \(e\), inclination \(i\), right ascension of ascending node \(\Omega\), argument of perigee \(\omega\), and mean anomaly \(M\). The phase factor \(F\) is dependent on \(P\): \(0\leq F\leq P-1\). The number of the first satellite in the initial orbital plane is 1, and for any satellite indexed as \(J\) on orbital plane \(P\) within the constellation, its \(\Omega\) and phase angle \(u\) are as follows:  
\[\begin{cases}\Omega_{j}=\frac{2\pi}{P}\left(P_{j}-1\right)&P_{j}=1,2,\cdots P\\ u_{j}=\frac{2\pi}{5}\left(L_{j}-1\right)+\frac{2\pi}{N}F\left(P_{j}-1\right)&L _{j}=1,2,\cdots\ \ S\ \ \ S=\frac{N}{P}\end{cases} \tag{1}\]  
where \(P_{j}\) represents the orbital plane number, \(S\) represents the number of satellites in each orbital plane, and \(L_{j}\) represents the serial number of satellites in orbital plane. In Figure 1, the \(\Omega\) and phase angle of the constellation are depicted when \(P_{j}=2,L_{j}=2\).
## 3 Results and Discussion
### Influence of Different Conditions on the Content of Photosynthetic Pigments
#### 3.3.2 Effect of Nutrient Conditions on the Content of Photosynthetic Pigments
This result robustly indicates that high silicate concentrations (over 240 mg L\({}^{-1}\)) profoundly enhance the fucoxanthin content of diatoms [85]. The HM group incubated in low light exhibited the highest photosynthetic pigment content among all the nutrient conditions (Figure 6). We attribute this to the fact that the enrichment of trace metals can periodically have a stimulatory effect on phytoplankton. An analogous study conducted on trace metals showed that an ethylenediaminettetraacetic acid chelated mixture containing iron, copper, zinc, cobalt, manganese, and molybdenum did effectively promote the chlorophyll-a production in plankton across a span of three days [86]. These results can be strung together for the low light intensity and high trace metals condition, under which the \(F_{\
u}/F_{m}\) values of algal cells increase, indicating an increase in the photosynthetic efficiency, which in turn promotes an increase in the cellular biomass and thus enhances the accumulation of cell-associated photosynthetic pigments. Furthermore, we collected the fucoxanthin content in microalgae in scholarly articles and found that the amount of fucoxanthin extracted from some algae was low, probably due to subpar extraction or culture conditions (Table 1). The findings of our study showed that the centric diatom _C. meneghiniana_ measured 7.76 \(\pm\) 0.30 mg g\({}^{-1}\) DW, preserved in low light with high trace metals. In comparison, the cultivation environment was fine-tuned for fucoxanthin synthesis using _C. meneghiniana_, which was extracted in relatively high amounts. Going forward, the goal is to determine the ideal conditions for extracting fucoxanthin from _C. meneghiniana_ through the compound study of multiple environmental elements, which will provide a favorable theoretical backing for its commercial manufacture.
# Power Losses Investigation in Direct 3 \(\times\) 5 Matrix Converter Using MATLAB Simulink
###### Abstract  
This article addressed the problem of matrix converters (McC), specifically the investigation of power losses and matrix converter efficiency in a \(3\times 5\) arrangement. In today's modern world, efficiency is very important; hence, power loss and efficiency analysis are important throughout the design process of modern semiconductor converters. The ability to evaluate power losses more quickly using the simulation approach can greatly reduce the amount of time necessary for the design, in comparison with numerical analysis. The described model employed contemporary SiC semiconductors, which offer substantial benefits over IGBT transistors. The \(3\times 5\) converter model was shown, along with a study of power losses in various elements of the converter, such as the power circuit, input filter, and so on. A summary of the simulated findings was offered at the end of the study, along with the benefits and drawbacks of employing SiC semiconductors in bidirectional switches for matrix converters.  
matrix converter; SiC semiconductors; power losses; efficiency; MATLAB Simulink +
Footnote †: journal: _aciences_  
_Applied_  
_Acticle_
# Study on Temperature Characteristics of Lubrication Film of Valve Plate Pair in Axial Piston Pumps
## 4 Results and Discussion
### Temperature Characteristics of the Valve Plate Oil Film
As the working pressure increases from 3 MPa to 7 MPa, the maximum temperature position moves from \(-15^{\circ}\) to \(5^{\circ}\) in the circumferential direction of the valve plate. This indicates that the working pressure increase would cause the azimuth angle to change from \(-15^{\circ}\) to \(5^{\circ}\) in the circumferential direction. The swash plate tilt angle increase causes a decrease in the minimum film thickness, more viscous friction loss, and higher film temperature.
## 3 Selection of Topology Optimization Design Domain and Model Establishment  
### Selection of Topology Optimization Design Domain  
The topology optimization design domain refers to the design space where the car-body structure can be optimized. A reasonable design domain can ensure the rationality and adaptivity of the topology optimization results, which is a crucial prerequisite for topology optimization calculation. In this paper, the topology optimization design domain is determined according to the outer contour size of the CRH vehicle car-body model, and the structure model is established. The car-body structure dimensions of a CRH vehicle are given in Table 1, including the body length, fixed distance, width, height, and other basic contour dimensions of the car-body structure.  
Firstly, the initial topology optimization model is analyzed based on the existing car-body structure, and some structures suitable for topology optimization analysis (mainly considering the conventional extruded profile structure) are selected. Secondly, the selected parts of the car-body structure are classified, dividing it into the end wall, end, bottom frame, side wall, upper beam, and roof structure. Finally, it is necessary to facilitate the welding and manufacturing of the car-body structure and quickly match it with the vehicles already operating. Therefore, some of the necessary structural positions should be set aside, such as the coupler seat, car window, car door, etc. Based on the above analysis, the car-body structure model required for the initial topology optimization can be established. The geometric model of the car-body structure is shown in Figure 2.