esa-sceva/satcom-chunk-collection huggingface dataset:
# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 3 Experimental Validation
### Lab-Scale Test
#### 3.1.2 Power Consumption Validation  
To verify the low-power characteristics and monitor the power consumption during actual system operation, a current tester was used to measure the current while the system was running. The measured results are presented in Figure 8 as a graph showing the current variation over time in mA.  
Assuming the system operates at 30 min intervals throughout the day, the process for calculating the daily current consumption is as follows:  
\[I_{day}=N\times\int_{0}^{T_{w}}I_{w}(t)dt+I_{sleep}\times T_{w} \tag{7}\]  
where N is the number of operations in a day, \(T_{w}\) is duration of a single operation, \(I_{w}(t)\) is the current consumption during operation, \(T_{sleep}\) is the total time spent in mode per day, and \(I_{sleep}\) is the current consumption in sleep mode. Here, the unit of time is seconds and the unit of current is milliamperes (mA). When the sampling period is configured to 4 h intervals, the daily current consumption (\(I_{day}\)) is determined to be 133.17 mAh.  
By setting the battery capacity to 10,000 mAh and introducing a compensation factor of 0.8 to account for potential reductions in battery life due to environmental conditions, the battery life can be calculated as shown in Equation (8), demonstrating that the battery can operate for 60 days without recharging.  
\[BatteryLife=0.8\times\frac{BatteryCapacity}{I_{day}} \tag{8}\]  
Additionally, with the charging current provided by the solar panel (e.g., 200 mA), the sensor can operate continuously. Consequently, the system is suitable for long-term environmental monitoring applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Moved Axis**} & \multicolumn{3}{c}{**RMSE for Each Axis (mm)**} \\ \cline{2-4}  & **X** & **Y** & **Z** \\ \hline X & 0.011 & 0.012 & 0.015 \\ Y & 0.027 & 0.015 & 0.032 \\ Z & 0.023 & 0.031 & 0.012 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RMSE of the three-axis independent displacement value in lab-scale validation.  
Figure 8: Result of the power consumption measurement.
## 3 Results and Discussion
### Antioxidant Enzymatic Activity  
ROS, which result in oxidative damage to skin cells, are a significant factor in the aging process. The skin is particularly vulnerable to these processes because there are so many possible cellular targets for oxidative injuries. A crucial defense against oxidative cell damage is the antioxidant mechanism [27]. _SOD_, _CAT_, _GPx_, _GST_, and other intrinsic and extrinsic antioxidant enzymes and nutrients make up the antioxidant system, which reduces the toxicity of free radicals [28]. _SOD_ slows down the aging process by removing the superoxide anion, which are created in the initial stages of oxidative injury [84]. Superoxide is converted by _CAT_ to hydrogen peroxide and oxygen. The fast breakdown of hydrogen peroxide is typically catalyzed by cells using _CAT_ and _GPx_. _GSTs_ primarily operate as cytoprotectors by activating the conjugation reaction of reduced glutathione (_GSH_), which results in the production of reactive electrophiles. HaCaT cells were medicated with both A.A and A.M, and antioxidant enzyme functions such as _SOD_, _GPx_, _CAT_, and _GST_ were assessed to determine whether the antioxidant effects of A.A and A.M are connected to the induction of antioxidant enzyme activity. As displayed in Figure 11, the activities of the 4 enzymes were downregulated by H\({}_{2}\)O\({}_{2}\) (100 uM) treatment during treatment with A.A and A.M. restored the activities of the enzymes more than the control. Through promoting the activity of endogenous antioxidant enzymes, A.A and A.M seem to have antioxidant capabilities.  
Figure 10: Effects of A.A and A.M on pro-inflammatory mediators (**A**) _COX-2_, (**B**) _TNF-a_, (**C**) _iNOS_, (**D**) _IL-6_, (**E**) _IL-8_ in LPS induced RAW 264.7 cells. The mRNA expression was determined by qPCR analysis. Data presented as \(\pm\)SEM, *** \(p<0.01\) vs. normal. All treatment was observed three times.  
Enhancing Antioxidative Gene Expression on Hacat Cells  
The RT-PCR presented the upregulation of antioxidant genes such as \(SOD\), \(CAT\), \(GPx\), and \(GST\) expression in a dose-dependent method via A.A and A.M extracts. Figure 12 shows that pretreatment with plant extracts and ascorbic acid has significantly increased the gene expression levels in the presence of H\({}_{2}\)O\({}_{2}\). These outcomes showed that A.A and A.M extract effectively suppress oxidative stress by enhancing antioxidative gene expression in H\({}_{2}\)O\({}_{2}\)-induced Hacat cells.
## 2 Materials and Methods  
### Chemicals and Characterization  
Tetrachloroauric (III) acid trihydrate (HAuCl\({}_{4}\).3H\({}_{2}\)O, \(\geq\)99.5%), sodium borohydride (NaBH\({}_{4}\), \(\geq\)96.0%), hydroquinone (HQ, \(\geq\)99.0%), silver nitrate (AgNO\({}_{3}\), \(\geq\)99.0%), cetyltrimethylammonium bromide (CTAB, \(\geq\)99.0%), sulfuric acid (H\({}_{2}\)SO\({}_{4}\), 98%), and hydrogen peroxide (H\({}_{2}\)O\({}_{2}\), 30%) were purchased from Merck. Deionized (DI) water (\(>\)18 MW, Millipore) was used during the synthesis of the gold nanorods. A commercial fungicide product, called TOPSIN M 70 WP (containing 70% of thiophanate methyl), was purchased from Nippon Soda Company, Ltd. (Tokyo, Japan). All reagents were used without further purification.  
Scanning electron microscopy (Hitachi, Horiba S-4300, Japan), coupled with energy-dispersive X-ray (SEM-EDX) spectroscopy operated at 20 KV of the incident electron beam energy, was performed to study the morphologies of the as-synthesized AuNRs. Transmission electron microscope (TEM) and high-resolution TEM (HRTEM) images were obtained with a JEOL JEM (JEOL Ltd., Tokyo, Japan, 120 kV). X-ray diffractometer (XRD, Bruker D8 advance powder diffractometer model) with Cu K\(\alpha\) radiation (\(\lambda\) = 1.54056 A) operated at 40 kV and 30 mA with a scanning rate of 0.02\({}^{\circ}\) per step in the 2\(\theta\) range of 10\({}^{\circ}\)\(\leq\) 2\(\theta\)\(\leq\) 90\({}^{\circ}\) has been carried out to characterize the crystal structure of AuNRs. The plasmon resonance spectra of AuNRs were recorded on UV-Vis spectroscopy (UV-Vis-NIR-V670, JASCO). A LabRam HR micro-Raman instrument with a 532 nm Ar\({}^{+}\) ion laser was utilized to obtain Raman spectra of AuNR samples at room temperature.
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 4 Results
### Test Results and Analysis
#### 4.2.3 Performance Comparison with Other Networks  
To further verify the performance of the improved model at detecting grassland animals, the improved YOLOv5s model was compared with other models in the test set. mAP_0.5, mAP_0.5:0.9 and average detection speed were taken as the evaluation indicators of the model, and a comparison of the results is shown in Table 3. The test device was NVIDIA-TX2.  
Figure 13: (**a**) Test results for horse with the original YOLOv5s model; (**b**) test results for giraffe with the original YOLOv5s model; (**c**) test results for horse with the improved YOLOv5s model; (**d**) test results for giraffe with the improved YOLOv5s model.  
It can be seen from Table 3 that the mAP_0.5 and mAP_0.5:0.9 values of the improved YOLOv5s model are the highest, indicating that the performance of the improved YOLOv5s was the best among the YOLOv3, EfficientDet-D0, YOLOv4, YOLOv5s and improved YOLOv5s models. As far as the detection speed of the network model is concerned, the improved YOLOv5s model has an average detection speed of 26 fps in NVIDIA-TX2, which is a bit slower than the initial YOLOv5s model, but is better than the YOLOv3 model, EfficientDet-D0 model and the YOLOv4 model, which meets the requirements of drones for real-time detection of grassland animals. At the same time, it can be seen from Table 3 that the size of the improved YOLOv5s model is only 12.8 MB, which is smaller than the other models. Experiments have proved that the improved YOLOv5s model not only ensures the accuracy of object detection, but also ensures that the network model is lightweight. In summary, among the four network models proposed in Table 3, the improved YOLOv5s model has the highest mAP_0.5 value and mAP_0.5:0.9 value, and the scale of the model is also relatively small. At the same time, the detection speed is also better than that of the YOLOv3 model, EfficientDet-D0 model and the YOLOv4 model. Although the detection speed is lower than that of the initial YOLOv5s model, it can meet the needs of real-time detection using UAVs.
applied sciences Article  
The Development of a Novel Direct-Expansion Ground Source Heat Pump (DE-GSHP) for Embankment Heating in Cold Regions [PERSON] 1, [PERSON]  
cleaning and maintaining the ditches only within the track area is insufficient [9]. The anti-freezing effect of the above-mentioned measures is based on the effects of soil property improvement, moisture content control, and heat insulation; therefore, these measures cannot effectively adjust the embankment temperature during extremely cold winters. Consequently, considerable unfavorable frost heaving cases still exist in cold regions, especially in deep-frozen soil regions. For example, monitoring data from various units along newly constructed high-speed railways in China revealed that frost heaving occurs under some anti-freezing embankments [10; 11]. Therefore, these conventional measures are not applicable to extremely cold regions.  
Recent studies have proposed several methods for increasing the efficiency of anti-freezing technologies. Among these, artificial heating technologies have received considerable attention and also been practically applied. In ref. [12], electric heat tracing was used and a method for preventing frost heave damage in tunnels was proposed. In refs. [13; 14], an active heating technique using a heat pump for applications in tunnel engineering was developed, resulting in an efficient freeze-proof effect. In ref. [15], a geothermal heating technology for deicing and snow removal on bridges was created. Their technique was based on passive anti-freezing that differed from active ground heating, which can increase heat input more effectively using external heat sources. However, there has been no research on the application of artificial heating methods in embankments located in seasonally frozen regions.  
In this study, heating methods using a heating boiler, electric heating, and renewable-energy-based heating were compared. Furthermore, a novel heating method based on geothermal energy was introduced, and heat pump technology was adopted to collect and transfer geothermal heat. A direct-expansion ground source heat pump (DE-GSHP) was designed and manufactured for embankment heating, and experiments were carried out to evaluate its feasibility. Furthermore, the distribution of soil temperature and heat supply temperatures nearby, as well as the coefficient of performance (COP), were investigated.
# A Novel Test Rig for the Basic Nonlinear Characterization of Bolted Joints
## 2 Rationale  
The nonlinear dynamic behavior of a friction joint is affected by the amount of tangential relative displacement at the contact interfaces. This suggests the importance of the contact kinematics involved by the mode shapes for the analysis of the stiffness and damping characteristics of the joint. The dynamical characteristics of a bolted joint are studied on a beam-like experimental test rig whose flexural mode shapes resemble those occurring at the bolted flanges of turbomachinery casings, where the out-of-plane (i.e., normal to the contact plane) vibration modes involve in-plane (i.e., tangential) relative displacements of the contact interface.  
The existence of traveling-wave vibrations in cylindrical-shell components of gas turbine engines has been clearly demonstrated [28]. Typical traveling-wave vibration modes of the casing with bolted flanges are shown in Figure 3a, where the cyclic distribution of the bolts has not been considered. Figure 3b shows how the traveling-wave propagates in the bolted flange, exciting the flexural mode shapes that become the main responsibility of relative in-plane displacement at the contact interface.  
The displacements of the two contact flanges are different in tangential direction due to the flexural deformation of the system (Figure 3c), and the slip may start where the relative tangential displacement is higher. The relative tangential displacement is different at different positions, which can be explained by taking the flange included in the \(\pi/2\) h\(\sim\)\(\pi/\)h sector as an example. The out-of-plane displacement amplitude w0 is at position \(\pi/2\) h, but the tangential displacements are the same for two flanges, which means there is no slip. As the position changes from \(\pi/2\) h to \(\pi/\)h, the tangential slip displacement increases due to the flexural deformation, which reaches the maximum value at position \(\pi/\)h.
# Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning +
## 2 Noise-Limited Path Loss and Positioning
In [3], the data are binned and truncated, i.e., rounded to the closest dBm and only a limited number of strongest signals are available, and the incomplete data are taken into account in both phases. In this paper, unlike in [3; 19; 20; 21; 22; 23], we frame both the training and the positioning phase as Tobit MLE, and we examine the influence of including or not including the censored PL in both phases. It should be noted that (nearly) all positioning methods have the potential to include the noise-limited censored data. In this work, we focus on the probabilistic PL positioning since censored PL has a simple interpretation as the probability of measuring only noise. Any measurement, e.g., Doppler and angle-of-arrival (AoA) [26], not just PL or RSS, is potentially unavailable due to noise and the likelihood that a censored measurement could in principle be included. Notably, at a relatively short distance, e.g., in case of neighbor discovery [27], censoring does not happen. Additionally, e.g., in wireless sensor networks (WNSs) [28; 29] positioning is based on measuring link distance estimates with RSS and/or TOA. As such measurements are limited by noise, the availability or unavailability has a similar link distance-dependent probability as does censored PL.
## 4 Method  
### Manifold Regularization  
Manifold regularization is a graph-based semi-supervised learning method that constructs an undirected graph from labeled and unlabeled data. The vertices of the graph are data samples, and the edges are the similarities between data samples to correlate labeled data with unlabeled data. Since the manifold regularization framework must satisfy the manifold assumption, the smoothness assumption is enforced on the data by minimizing the loss function, which leads to Equation (1):  
\[\mathrm{L}_{\mathrm{m}}=\frac{1}{2}\sum_{\mathrm{i,j}}\mathrm{w}_{\mathrm{i,j }}\|\mathbf{P}(\mathbf{y}|\mathbf{x}_{\mathrm{i}}\,)-\mathbf{P}\big{(} \mathbf{y}|\mathbf{x}_{\mathrm{j}}\,)\|^{2} \tag{1}\]  
where \(\mathrm{w}_{\mathrm{i,j}}\) is the similarity \(\mathrm{i}\) between \(\mathrm{x}_{\mathrm{i}}\) and \(\mathrm{x}_{\mathrm{j}}\) data, \(\mathrm{j}=1,2,\cdots 1+\mathrm{u}\), \(\mathrm{l}\) is labeled data, \(\mathrm{u}\) is unlabeled data. \(\mathrm{P}(\mathrm{y}|\mathrm{x}_{\mathrm{i}})\) and \(\mathrm{P}(\mathrm{y}|\mathrm{x}_{\mathrm{j}})\) are conditional probabilities.  
Since the conditional probability calculation is relatively difficult, Equation (1) can be replaced with Equation (2):  
\[\widetilde{\mathrm{L}}_{\mathrm{m}}=\frac{1}{2}\sum_{\mathrm{i,j}}\mathrm{w}_{ \mathrm{i,j}}\|\widetilde{\mathrm{y}}_{\mathrm{i}}-\widetilde{\mathrm{y}}_{ \mathrm{j}}\|^{2} \tag{2}\]  
where \(\widetilde{\mathrm{y}}_{\mathrm{i}}\) and \(\widetilde{\mathrm{y}}_{\mathrm{j}}\) are the prediction results based on the data \(\mathrm{x}_{\mathrm{i}}\) and \(\mathrm{x}_{\mathrm{j}}\).  
By simplifying Equation (2), Equation (3) can be obtained:  
\[\widetilde{\mathrm{L}}_{\mathrm{m}}=\mathrm{Tr}\Big{(}\widetilde{\mathrm{Y}} ^{\mathrm{T}}\mathrm{L}\widetilde{\mathrm{Y}}\Big{)} \tag{3}\]  
where \(\mathrm{Tr}(\cdot)\) is the trace of the matrix, \(\widetilde{\mathrm{Y}}\,=\,\big{[}\widetilde{\mathrm{y}}_{1},\widetilde{ \mathrm{y}}_{2},\cdots,\widetilde{\mathrm{y}}_{\mathrm{l}+\mathrm{u}}\big{]}^{ \mathrm{T}}\).  
\begin{table}
\begin{tabular}{c c} \hline
**Gas Type** & **Number of Samples** \\ \hline Ethanol & 150 \\ Methane & 146 \\ Vinyl & 154 \\ Carbon Monoxide & 150 \\ \hline \end{tabular}
\end{table}
Table 4: Number of different gas samples.  
According to the principle of Laplacian, \(\mathrm{L}=\mathrm{D}-\mathrm{W}\), \(\mathrm{D}\) is a diagonal matrix, and the elements in the matrix are shown in Equation (4):  
\[\mathrm{D}_{\mathrm{ii}}=\sum_{\mathrm{j=1}}^{1+\mathrm{u}}w_{\mathrm{i}\mathrm{ j}} \tag{4}\]  
W is an adjacency matrix. Set the set of k nearest neighbors of the data x to \(\mathrm{N}_{\mathrm{k}}(\mathrm{x})\), then the elements in the adjacency matrix can be obtained by Equation (5):  
\[w_{\mathrm{i}\mathrm{j}}=\begin{cases}\exp\left(-\frac{\|x_{\mathrm{i}}-x_{ \mathrm{j}}\|^{2}}{2\sigma^{2}}\right)&x_{\mathrm{i}}\in\mathrm{N}_{\mathrm{ k}}\left(x_{\mathrm{j}}\right)\text{ or }x_{\mathrm{j}}\in\mathrm{N}_{\mathrm{k}}\left(x_{\mathrm{i}}\right)\\ 0&\text{otherwise}\end{cases}. \tag{5}\]
## 2 Basic Theoretical Parameters of FE-SEA Method
### Modal Density
#### 2.1.2 Modal Density of Two Dimensional Plates  
Thin plate modeling is often used in hybrid model modeling. For a two-dimensional flat plate, the modal density is expressed as [10; 11; 12; 13; 14]:  
\[n(f)=\frac{A}{2 RC_{l}} \tag{7}\]\[C_{l}=\sqrt{\frac{E}{\rho(1-\mu^{2})}} \tag{8}\]  
In the above formula, \(A\) is the total area of the thin plate, \(R\) is the radius of bending rotation, \(C_{l}\) is the two-dimensional longitudinal wave number of the flat plate, \(E\) is the elastic modulus of the corresponding flat plate, \(\rho\) is the density of the corresponding material, and \(\mu\) is the Poisson's ratio of the corresponding material. For curved plate, the empirical formula of modal density is as follows [10; 11; 12; 13; 14]:  
(1) When the system frequency ratio meets the condition \(f/f_{r}\leq 0.48\):  
\[n(f)=\frac{5A}{\pi hC_{l}}\left(\frac{f}{f_{r}}\right)^{1/2} \tag{9}\]  
(2) When the system frequency ratio meets the condition \(0.48<f/f_{r}\leq 0.83\):  
\[n(f)=\frac{7.2A}{\pi hC_{l}}\left(\frac{f}{f_{r}}\right) \tag{10}\]  
(3) When the system frequency ratio meets the condition \(f/f_{r}>0.83\):  
\[n(f)=\frac{2A}{\pi hC_{l}}\left\{2+\frac{0.596}{F-\frac{1}{F}}\left[F\cos \left(\frac{1.745f_{r}^{2}}{F^{2}f^{2}}\right)-\frac{1}{F}\cos\left(\frac{1.74 5F^{2}f_{r}^{2}}{f^{2}}\right)\right]\right\} \tag{11}\]  
In Equations (9)-(11), \(F\) is the center frequency of \(1/3\) octave, \(f_{r}\) is the frequency, and \(F\) is the frequency factor. In \(1/3\) octave, \(F=1.122\), and:  
\[f_{r}=\frac{C_{l}}{2\pi r} \tag{12}\]  
\[F=\left(\frac{f_{upper}}{f_{lower}}\right)^{1/2} \tag{13}\]  
In the above formula, \(f_{upper}\) represents the upper limit frequency of each frequency band of \(1/3\) octave, and \(f_{lower}\) is the lower limit frequency of each frequency band corresponding to it. According to the above equation, a common feature of the modal density of flat and curved plates can be obtained, that is, the modal density does not change with frequency.
## 5 Results and Analysis
### Voronoi Tessellation Plot  
A typical Voronoi tessellation plot from multi-tier HetNet is shown in Figure 8. The key idea behind the use of stochastic geometry research is that it can fairly capture the randomness and unpredictable nature of both TRX ends.  
It enables us to derive expressions in closed and semi-closed forms for critical performance metrics such as Signal-to-Interference Ratio (SIR), data throughput, channel capacity, and coverage probabilities without utilizing traditional deterministic models. Sleep mode strategies involve turning off or putting a few components or the entire BS or network into sleep mode when the network is underutilized [55].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**BS Distribution** & **PPP** \\ \hline Number of simulations & 500 \\ \hline Tier-1 (macro BSs) density & \(1/500\) m\({}^{2}\) \\ \hline Tier-2 (femto BSs) density & \(4/500\) m\({}^{2}\) \\ \hline The power consumption of macro BSs & 400 W \\ \hline The power consumption of femto BSs & 40 W \\ \hline SINR threshold of Femto \(\beta_{f}\) & 1.1 \\ \hline SINR threshold for macro \(\beta_{m}\) & 1.3 \\ \hline Path loss exponent & 2 \\ \hline System bandwidth & 10 MHz \\ \hline Path loss model for macro & L = 128.1 + 37.6 log10(R) (R in km) \\ \hline Path loss Model for small cell & L = 140.7 + 36.7 log10(R) (R in km) \\ \hline MU rate requirement for macro BS & 400 kbps \\ \hline MU rate requirement for small-cell BS & 400 kbps \\ \hline The minimum distance between macro BS and MU & 35 m \\ \hline The minimum distance between macro BS and small-cell BS & 75 m \\ \hline The minimum distance between macro BS and MU & 35 m \\ \hline The minimum distance between small-cell BS and MU & 10 m \\ \hline The minimum distance between two small-cell BSs & 40 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for Simulations.  
Figure 8: Voronoi tessellation plot for multi-tier network (Macro, \(\triangle\)Pico and mMU).  
Figure 9 represents the average daily traffic profile [56], which indicates the potential of energy saving by turning off a few or all BS components to sleep mode when the network load is comparatively low.  
In addition to that it is also important to note that before enabling the sleep mode in particular BSs, the intended sleep-enabled BSs must coordinate with other BSs and release its channel resources to active neighboring BSs. The active BS must be able to provide extended coverage and data rate services to users located inside sleeping BSs. The exchange of traffic information between BSs is usually managed by the BS Controller. However, switching a BS to sleep mode will eventually result in an increase in outages which eventually leads to a reduction in coverage probability.