esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential Synergistic Inhibition of _Enterococcus faecalis_ by Essential Oils and Antibiotics
## 3 Results
### Selection of Antibiotics and EOs Solution for the Combination Study on Inhibiting E. faecalis  
All concentrations of cinnamon (CN10%, CN5%, CN2.5%, and CN1.25%) and clove (CL50% and CL25%) showed ZOIs that were comparable to the ZOIs of antibiotics (10% and 5%), and only CN10% solution had ZOI that was near the CLSI Intermediate Breakpoint for _Enterococcus_ spp. Therefore, for the antimicrobial susceptibility test of combining EOs with antibiotics, we chose to combine the CN 5%, 2.5%, 1.25%, and CL50% for EO solutions with 5% antibiotic solutions as individually they produced the ZOIs that were below the CLSI Intermediate Breakpoint values.  
Figure 2: Antimicrobial effects of EOs ((**A**), cinnamon and clove) and antibiotics (**B**) on _E. faecalis_ were quantitated using the disk diffusion test. The cinnamon (1.25% to 5%) and clove (25% to 50%) EOs showed significantly graded responses in inhibiting the growth of _E. faecalis_ (Panel **(A)**). The cinnamon EO showed stronger antimicrobial effect than clove EO at much lower concentrations. Each antibiotic was diluted to 5% and 10% concentrations that produced a ZOI similar to that of EOs (**B**). This paired antibiotic concentration was selected for the Antibiotic + EO combination experiment. All antibiotics showed significant increase in ZOI between the 5% and 10% concentrations. CN = cinnamon, CL = clove, EO = essential oil, K = kanamycin, S = streptomycin, G = gentamicin, and A = ampicillin. Different letters (a to e) above the columns indicate significant difference between the groups (\(p<0.005\)). \({}^{*}p<0.05\) and \({}^{**}p<0.005\).
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 2 Technology Overview
### Transmission Parameters
#### 2.3.3 Coding Rate  
LoRa uses FEC error coding to improve the robustness of the wireless connection. This type of error coding results in additional bits within the LoRa physical layer payload which is controlled by the CR parameter. The LoRa modem uses CR to provide increased protection against bursts of interference and decoding errors. LoRa permits CR settings to be either 4/5, 4/6, 4/7 or 4/8. Setting a high CR value implies that there are more error correction bits which provide better protection for the transmitted data. However, on the downside, it increases ToA which in turn decreases battery life. Receivers which vary CR and hold SF and BW constant, can still communicate between them by using an explicit header, since the CR of the payload resides in the packet header, that is encoded at CR 4/5 by default [17].
## 4 Case Study: MIMO Magnetic Levitation System
### System Analysis and Results
#### 4.2.1 Feedforward (\(\mathrm{G_{R}}\)) and Feedback Controller (\(\mathrm{G}\)) design  
The inversion-based strategy suggests that the diagonal tracking feedforward controller \(G_{R}=diag(g_{r11};\,g_{r22})\), as given in Table 1. The feedback controller bounds generated using the proposed method (inequality 17) are combined with the stability margin bounds, as shown in Figure 5, along with the loop shaping plot for channel-11. The bound comparison between the proposed method and the pure feedback design is shown in Figure 5. Figure 6 shows the nominal loop shaping plot for channel-22. The designed diagonal feedback controller is  
\[G(s)=\left[\begin{array}{cc}\frac{1\left(\frac{s}{2}+1\right)\left(\frac{s}{ 2}+1\right)}{s\left(\frac{s}{200}+1\right)}&0\\ 0&\frac{0.6\left(\frac{s}{2}+1\right)\left(\frac{s}{2}+1\right)}{s\left(\frac{ s}{200}+1\right)}\end{array}\right] \tag{18}\]  
In the loop shaping plot of channel-11 in Figure 5, the nominal loop transmission function (\(l_{110}\)) at \(\omega\approx 22.7\) makes the phase shift of \(180^{\circ}\). Similarly, the phase shift occurs at frequencies \(\omega\approx 25\), \(27.3\). The phase change is due to the phase shift of \(p_{11}\) at these frequencies as seen in the bode diagram (Figure 4). Similarly, the same situation occurs for the nominal loop shaping (\(l_{220}\)) plot of channel-22. Next, just for comparison purposes, a pure feedback design is done (independent design) [12]. The nominal loop shaping plots for the pure feedback controller (\(G_{fb}\)) are omitted for simplicity and the designed controller is given in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feedforward Controller (\(G_{R}\))** & **Pure Feedback Controller (\(G_{fb}\))** \\ \hline \(g_{r11}(s)=\frac{9166^{4}+1.18606^{2}+3.67\omega 08}{1565^{8}+6.26 04^{4}+1.6\omega 06^{5}+4.34\omega 07^{3}+3.6\omega 08+9.5\omega 08}&g_{fb11}(s)=\frac{13.75\left(\frac{s}{2}+1 \right)\left(\frac{s}{2}+1\right)}{s\left(\frac{s}{200}+1\right)}\\ \hline \(g_{r22}(s)=\frac{9166^{4}+1.18606^{2}+3.67\omega 08}{28196^{5}+1.12\omega 05 ^{4}+2.8\omega 06^{5}+7.3\omega 07^{5}+6.3\omega 08+1.6\omega 09}&g_{fb22}(s)=\frac{7.75\left( 1+1\right)\left(\frac{s}{2}+1\right)}{s\left(\frac{s}{200}+1\right)}\\ \hline \hline \end{tabular}
\end{table}
Table 1: Tracking feedforward controller and pure feedback controller (\(G_{fb}\)).  
Figure 5: _Cont._Figure 5: (**a**) The nominal loop-shaping for channel-11 (lower magnet). Here, \(B(\omega)\) denotes the bounds at each design frequency \(\omega\) and the numbers denoted in bold (1,2,3,4) at the nominal loop transmission function path denotes the phase shift, and (**b**) bound comparison between the proposed method and the pure feedback design method.  
Figure 6: Channel-22 (upper magnet) nominal loop-shaping plot. Here, \(B(\omega)\) denotes the bounds at each design frequency \(\omega\) and the numbers denoted in bold (1,2,3,4) at the nominal loop transmission function path denotes the phase shift.
# Study of the Error Caused by Camera Movement for the Stereo-Vision System
## 4 Experiments and Results
### Relative Translation Experiment  
Figure 10 shows the experimental setup for the relative movement between the two cameras. The left camera is fixed directly onto the optical isolation table while the right camera is mounted onto the electronic control platform. The position or orientation of the right camera is changed by controlling the translational or rotational stage.  
Figure 10: Experimental setup for relative movement between the two cameras.  
Figure 9: The 3D coordinates and distance measurement errors introduced by the identical rotation: **(a,b)**: the average 3D coordinate errors of all corner points in each rotational pose while the two cameras are rotated around the X and Y-axes, respectively; **(c,d)**: the comparison between the measured and real distance between the adjacent corner points for different rotational poses.  
In the experiment for relative translation, the right camera is translated in 4 mm increments along the X and Z-axes, respectively. Eleven image pairs are captured at 11 different positions by the two cameras. Figure 7c shows the stereo image pairs captured in the initial state and the state of relative translation (44 mm) along \(X\)-axis.  
Figure 11a,b shows the 3D coordinate error introduced by the relative translation. Two points from the checkerboard (Corner A and B in Figure 9) are selected to analyze the error variation trend. It can be seen that the 3D coordinate error of any point and the mean error of all the corner points in three directions keep linearly increasing with the increasing translation. Specifically, the 3D coordinate errors vary for different measured points on the object. These results effectively verify the theoretical analysis given in Section 3.3.  
Figure 11c,d shows the comparisons of the measured distance between the two adjacent points and the real value of 25 mm. It can be seen that the distance error varies linearly with the increasing translation. This trend shows a good agreement with the prediction using [PERSON]'s formula. In Figure 10, the 3D coordinate and distance error caused by the translation along the \(X\)-axis are larger than those along the \(Z\)-axis (its optical axis).
# An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi
###### Abstract  
Patients diagnosed with diabetes mellitus must monitor their blood glucose levels in order to control the glycaemia. Consequently, they must perform a capillary test at least three times per day and, besides that, a laboratory test once or twice per month. These standard methods pose difficulty for patients since they need to trick their finger in order to determine the glucose concentration, yielding discomfort and distress. In this paper, an Internet of Things (IoT)-based framework for non-invasive blood glucose monitoring is described. The system is based on Raspberry Pi Zero (RPi) energised with a power bank, using a visible laser beam and a Raspberry Pi Camera, all implemented in a glove. Data for the non-invasive monitoring is acquired by the RPi Zero taking a set of pictures of the user fingertip and computing their histograms. Generated data is processed by an artificial neural network (ANN) implemented on a Flask microservice using the Tensorflow libraries. In this paper, all measurements were performed in vivo and the obtained data was validated against laboratory blood tests by means of the mean absolute error (10.37%) and Clarke grid error (90.32% in zone A). Estimated glucose values can be harvested by an end device such as a smartphone for monitoring purposes.  
internet of things; raspberry pi zero; non-invasive glucose monitoring; health-care; medical computing; artificial neural network +
Footnote †: journal: Medical Engineering Research
# Predictive Power Management for Wind Powered Wireless Sensor Node
## 3 System Model
### Hardware Model
#### 3.1.1 Wtg  
Similar to our previous study [27], a plastic four-bladed horizontal-axis wind turbine is used here, which has a radius of 6 cm and an AC peak voltage of 5.5 V. In addition, it is known: as for a fixed airflow speed, there exists a load value, which could maximize the power generated by the WTG. Therefore, we have tested this WTG under common wind speed and worked out that the load value with maximal output lies between 370 \(\Omega\) and 420 \(\Omega\). Thus, we choose a trade-off value of 400 \(\Omega\) as \(R_{Optimal}\) (the optimal load resistance maximizing the power generation); i.e., once the external followed-up load matches with \(R_{Optimal}\), the output power of WTG is always maximized towards any incoming wind speed.
## 6 Future Applications
### F-RAN Scenario  
In Reference [32], the authors present a system level architecture of NOMA-based F-RAN. In that work, multiple F-RAN systems co-exist within a macro-cell. In such an architecture, NOMA can be implemented between the F-UEs served by the same Fog Access Point (F-AP), as well as between different F-APs which share the same sub-channel. In this type of multi-layered architecture, a group of F-UEs in device-to-device (D2D) mode can transmit various contents like audio, video and text through multiple bandwidth channels. Evidently, channel conditions are different among the different F-UEs. The received signal at any F-UE includes the desired signal, AWGN, cross-tier interference from the M-RRH, co-tier interference (i.e., from F-UEs served by other F-AP) as well as interference from NOMA based F-UEs served by the same F-AP. In this work, the authors formulate the power and sub-channel allocation in a F-RAN system as a sum rate maximization problem, from a game-theoretic point of view. Similarly to the underlay cognitive NOMA concept, the authors in Reference [32] propose to protect the Macro Remote Radio Head (M-RRH) by setting a maximum tolerable interference temperature (IT) threshold. Moreover, it is proposed to use SIC to remove co-tier interference. Their proposition is to constrain the maximum number of Fog User Equipments (F-UEs) occupying the same sub-channel at the same time, in order to reduce the decoder complexity. However, in such an environment where each F-UE encounters both co-tier and cross-tier interference, the FRESH-SIC scheme described in our work might prove beneficial. By taking into account the modulation format and the signal repetition patterns of the individual component signals, the proposed neuromorphic FRESH-SIC receiver can be incorporated in an F-RAN scenario to provide better BER performance and SINR improvements in a low SNR regime.
# Object-Level Semantic Map Construction for Dynamic Scenes
## 3 Methodology
### Object Model Fusion, Semantic Map Optimization, and Construction  
When the first RGB-D data frame is input, a background model and object model database are created through image data segmentation and recognition with the camera pose as the identity matrix. Then, an object-level map representation is built for the scene. When the subsequent frame comes in, our proposed method is performed step by step. At first, the object motion information is obtained through 3D tracking against the rough camera pose in the current frame.  
Next, the object instance segmentation results produced by the fusion segmentation algorithm combining semantic and geometric cues are used to associate them with the object model database, aiming to find the dynamic part in the current frame, transfer the object semantic category information, and create a new object. Accurate camera pose tracking can be achieved by culling the dynamic part in the current frame data. Each object model pose is updated based on the accurate camera pose. When \(C_{i}\) is associated with \(m\) in the database, \(m\) will be updated using \(C_{i}\) data. \(C_{i}\) data are transferred to the camera coordinate system in which the object is observed using object pose \(T_{m}\). Then, the fusing algorithm provided by [3; 7] is performed between the data from \(C_{i}\) and the raw data stored in \(m\). Note that dynamic and static objects share the same fusion algorithm, but the dynamic object model, a pedestrian, as a non-rigid object, was created directly using \(C_{i}\) data regardless of the model created previously. The new object model, treated as a static object, is created directly using \(C_{i}\) data, which are not associated with any object model in the database.  
Due to the existence of accumulated camera pose error, we adopted the ElasticFusion [3] strategy to optimize the deformation map generated by static data, includingthe static object model and background model. Finally, we constructed the object-level semantic map accurately.
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 4 Challenges of VNF Placement in Edge Environments  
Placing different types of VNFs at the edge tier should consider SFC requirements, users' SLA, and resources available in the infrastructure. Therefore, VNF placement must deal with the heterogeneity of edge nodes' computational capacity and SFC requirements. Moreover, the available resources in edge nodes are dynamic since the CPU and memory utilization increases or decreases when VNF instances are allocated or deallocated. In addition, VNFs consume bandwidth to communicate with other VNFs that make part of an SFC when placed in different edge nodes.  
Another concern in the VNF placement problem is the optimization of resource usage. This is essential to serve the maximum number of users without violating their SLAs. Therefore, the following challenges are highlighted: the decision-making for deploying VNFs considering the VNF requirement and SLAs, the heterogeneity and dynamism of available resources in edge nodes, and the heterogeneity of SFC requirements. We discuss each of them in more detail in the following sections.
## 4 Determination of Indicator Weights Based on Combination Weighting Method
### Entropy Weight Method  
Entropy weight method is a common method for calculating the objective weights of indicators. It reflects the ability of evaluation objects to provide effective information by determining the relative proximity [34,35]. Weights that are given by the subjective weighting method can be adjusted and corrected by means of entropy weight method, which improves to get the scientific and accurate evaluation results. The determination of the objective weights of indicators based on entropy weight method is shown, as follows:  
1. Construct the standardized judgment matrix The standardized judgment matrix constituted by the pre-processed indicator data is \(X^{*}\).
2. Measure the information entropy The total number of evaluation objects is \(m\) and that of indicators is \(n\). Then the information entropy \(H_{i}\) of indicator \(i\) is calculated, as follows: \[H_{i}=-k\sum\limits_{i=1}^{m}{{f_{ij}}{Inf_{ij}}},\;i=1,2,\cdots,m\] (7) \[{f_{ij}}={x_{ij}}/\sum\limits_{i=1}^{m}{{x_{ij}}}\] (8) \[k=\frac{1}{\ln{m}}\] (9)  
where \(x_{ij}\) is the normalized value. When \(f_{ij}=0\), there is \(f_{ij}Inf_{ij}=0\).  
The objective weights of indicators are given by Equation (10).  
\[w_{i}=\frac{1-H_{i}}{\sum\limits_{i=1}^{m}{(1-H_{i})}} \tag{10}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scale Values** & **Implications of \(a_{ij}\)** \\ \hline
1 & Indicator \(i\) is as important as indicator \(j\) \\
3 & Indicator \(i\) is slightly more important than indicator \(j\) \\
5 & Indicator \(i\) is more important than indicator \(j\) \\
7 & Indicator \(i\) is strongly more important than indicator \(j\) \\
9 & Indicator \(i\) is extremely more important than indicator \(j\) \\
2,4,6,8 & The middle value of the above two adjacent scale judgment \\ Reciprocal & Ratio of the importance of indicator \(j\) to indicator \(i\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The 1–9 degree scale implications of analytic hierarchy process (AHP).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(r\) & **Values of \(RI\)** & \(r\) & **Values of \(RI\)** \\ \hline
1 & 0 & 9 & 1.46 \\
2 & 0 & 10 & 1.49 \\
3 & 0.52 & 11 & 1.52 \\
4 & 0.89 & 12 & 1.54 \\
5 & 1.12 & 13 & 1.56 \\
6 & 1.24 & 14 & 1.58 \\
7 & 1.36 & 15 & 1.59 \\
8 & 1.41 & — & — \\ \hline \hline \end{tabular}
\end{table}
Table 3: The assignment table for \(RI\).  
where \(0\leq w_{i}\leq 1\), \(\sum\limits_{i=1}^{m}w_{i}=1\).
# High-Resolution Digital-to-Time Converter Implemented in an FPGA Chip
## 2 Principle of the Proposed DTC  
### Principle of the Vernier Delay Line  
The Vernier delay line is widely used in time-to-digital converters (TDCs) [11]. The schematic of a TDC based on a Vernier delay line is shown in Figure 1. In the figure, time interval signals (Start and Stop) are delayed by two delay lines, respectively. The Start signal is delayed by a delay line with a cell delay of \(\tau_{1}\), and the Stop signal is delayed by another delay line with a cell delay of \(\tau_{2}\) (\(\tau_{1}<\tau_{2}\)). When edge coincidence is detected, the measurement result can be calculated from Equations (1) and (2).  
\[\tau=\tau_{1}-\tau_{2}(\tau_{1}>\tau_{2}) \tag{1}\]  
\[T_{x}=i\times\tau \tag{2}\]  
The principle of the Vernier delay line can also be applied to DTCs, which generate time interval signal (Start and Stop). The schematic of a DTC based on Vernier delay line is shown in Figure 2. Delay Line A and Delay Line B are adopted, which are with a cell delay of \(\tau_{A}\) and \(\tau_{B}\). \(M\) is loaded into the counters as an input number, which is calculated according to the time interval needed. The output of the \(M\)-th delay tap in Delay Line A is the Start signal and the output of the \(N\)-th delay element in Delay Line B is the Stop signal. Thus, the generated time interval can be calculated from Equation (3).  
\[T_{x}=M\times\tau_{B}-N\times\tau_{A} \tag{3}\]  
Figure 1: Schematic of a time-to-digital converter (TDC) based on a Vernier delay line.
# Application of a New Enhanced Deconvolution Method in Gearbox Fault Diagnosis
## 3 Limitations and Improvements of MOMEDA
### Based on Improved MOMEDA Fault Diagnosis Method  
On the basis of the limitations of the MOMEDA method in the gearbox fault diagnosis process, the ARMA-MOMEDA method proposed in this paper is used to extract the gearbox fault to verify the rationality of this method. Because MOMEDA cannot identify the fault feature in the noisy environment, and the periodic impact component of the characterizing bearing fault is less correlated with other components in signal, the ARMA model estimated by the autocorrelation function is used for filtering, the periodic impact component in the fault signal can be effectively separated from other unrelated components. After ARMA filtering, the fault signal is mainly composed of a highly correlated periodic impact component and partial noise. The flow chart of the ARMA-MOMEDA method is shown in Figure 4. The method steps are as follows:  
Step 1: Detect signal stability. Firstly, the inverse order test is used to judge the stationarity of the signal. If it is not stable, the d-differential operation is used to enhance the stationarity.  
Step 2: Use the autocorrelation coefficient (ACF) and partial autocorrelation coefficient (PACF) spectra to judge the signal stationarity again.  
Step 3: Given the selection range of (n, m) and estimating the parameters \(\varphi_{i}\) and \(\theta_{i}\) of the ARMA (n, m) model.  
Step 4: Use the AIC criteria to determine the order of ARMA (n, m).  
Figure 3: MOMEDA extraction rendering when \(-3.11\) dB noise is added. (**a**) The fault signal; (**b**) the noise of \(-3.11\) dB; (**c**) a composite signal of noise and fault signal; (**d**) MOMEDA extracts the result when \(-3.11\) dB noise is added; (**e**) envelope spectrum after fault extraction when \(-3.11\) dB noise is added.  
Figure 2: MOMEDA extraction rendering when \(1.74\) dB noise is added. (**a**) The fault signal; (**b**) the noise of \(1.74\) dB; (**c**) a composite signal of noise and fault signal; (**d**) MOMEDA extracts the result when \(1.74\) dB noise is added; (**e**) envelope spectrum after fault extraction when \(1.74\) dB noise is added.  
Step 5: After the signal is denoised by the fixed-stage ARMA filter, the fault is extracted using MOMEDA.  
Step 6: For multi-fault diagnosis, first use the multi-point kurtosis theory to divide the fault extraction interval of the ARMA noise-reduced signal. The MOMEDA filter is used in sequence for the feature extraction.
###### Abstract  
The incipient faults of rolling bearings are dynamically propagating in the service status. However, the bearing material and the size of the fault will affect its expansion trend and direction. Furthermore, bearings manufactured from different materials behave differently when they fail. Therefore, the influence of the fault degree on the dynamic characteristics and crack propagation of rolling bearings is investigated in this paper. First, a dynamic model of the bearing, both under fault-free conditions and with varying fault sizes on the outer ring, is established by considering the actual working conditions of rolling bearings. Then, the reliability of the dynamic model is verified theoretically and experimentally. Finally, the study examined the slip behavior of rolling elements, the variation trends in the maximum shear stress and principal stresses on the outer ring, and the direction of crack propagation under different fault severities. The results indicate that (1) the severity of roller slip becomes more pronounced with the expansion of the fault size; (2) material differences will affect the timing of macro-slip during faults; (3) crack propagation tends to initiate at the edge of the fault exit, with the propagation rate increasing as fault severity escalates; and (4) tensile stress was observed in the first principal stress, which accelerates crack bifurcation at the faulted edge, while both the second principal stress and third principal stress exhibit compressive stress, playing a suppressive role in crack bifurcation at the faulted edge. These findings provide a theoretical basis for further research on the evolution of faults in rolling bearings.  
cylindrical roller bearings; dynamic characteristic; crack propagation; crack bifurcation