esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Parametric Studies
#### 4.2.1 Effects of Train Speed
The location of the mean value of the maximum negative bending moment shifts from the sleeper under the crossing nose to sleeper at the closure panel. Consequently, neglecting track irregularities could lead to miscalculations. The longitudinal distributions of the maximum positive and negative bending moments are significantly influenced by speed variations; therefore, the bending moment distributions of specific slelepers under various speed are portrayed in Figure 8. The common point of each graph is that the maximum positive bending moments occur at rail seats for all speed variations. Conversely, maximum negative bending moments are observed at different locations. For instance, the location of the maximum negative bending moment for some sleepers, such as the sleepers at the entrance, under the closed switch blades, under the crossing nose and the shortest sleeper, is the sleeper section in the vicinity of the midpoint of track. This is in contrast to the other locations, particularly at the rail seats close to the loaded rail seats, for the sleepers at the closure panel, and the longest sleeper. Figure 8 also demonstrates that the effects of various speeds on the maximum positive bending moment distributions seem to be diverse for each sleeper. As an example, maximum velocities cause higher bending moments at sleeper sections under one rail or both rails for several sleepers, but occasionally lower bending moments for specific sleepers. This is due to the time step when the maximum positive bending moment occurs. At this time step, the location of the wheels could be different for each case; therefore, the longitudinal bending moment distribution, which is affected by speed and irregularities, shows variations that also affect the bending moment distribution along a sleeper. As a result, it is a challenge to make a reliable comment on the effects of irregularities on the bending moment distributions along a sleeper. On the contrary, it is obvious that higher speeds cause higher positive and maximum bending moments at the sleeper section under the crossing nose owing to the discontinuity at this section. The gap between minimum speed and maximum speed, in terms of the maximum positive bending moment at the section under the crossing nose, is about 11 kNm. Likewise, it is 8 kNm for maximum negative bending moment.
###### Abstract  
Symbiotic radio (SR), which has recently been introduced as an effective solution for 5G wireless networks, stands out with system models that include hybrid devices that share the frequency spectrum and transmit information to the same receiver. However, the low bit rate and the small amount of energy harvested in SR, where backscatter communication systems are integrated, make the system vulnerable to eavesdropping. To ensure security, the secrecy rate is defined as the difference between the number of bits transmitted to the receiver over the information channel and the number of bits reaching the eavesdropper (ED) over the wiretap channel. This paper is the first work that aims to maximize the secrecy rate for friendly jammer-aided SR networks with EDs over time allocation and power reflection coefficient in the presence of sensing errors. The proposed model consists of a base station (BS), a hybrid transmitter (HT) in symbiotic relationship with the BS, a WiFi access point used by the HT for energy harvesting, a jammer cooperating with the HT and BS, an information receiver, and EDs trying to access the information of the HT and BS. The simulation results provide valuable insights into the impact of system parameters on secrecy rate performance. Although taking the sensing error into account degrades the system performance, the real-world applicability of the system with sensing error is more realistic. It is also observed that the proposed system has higher performance compared to the wireless powered communication networks in the literature, which only use the energy harvest-then-transmit protocol and the power reflection coefficient is assumed to be zero.  
title: **Secrecy Rate Performance Analysis of Jammer-Aided Symbiotic Radio with Sensing Errors for Fifth Generation Wireless Networks.**_Appl. Sci._** 2025**, _15_, 289.
## 6 Discussion
### The Influence of SRP  
Furthermore, let us consider the influence of the other perturbation. As mentioned in Section 2, SRP is the largest perturbation besides the non-spherical gravity. In test 8, the influence of the SRP is considered in the simulated measurement data. In addition, we still only consider the second non-spherical gravity in the orbit determination. Here, because the value of SRP is relatively small, we use a simple model. It can be expressed as [21].  
\[F_{SRP}=\rho(1+\eta)\frac{S}{m}\frac{\mathbf{r}}{r} \tag{23}\]  
where \(\rho\) is the solar radiation pressure at r, \(\eta\) is the spacecraft's reflectance, \(S/m\) is the spacecraft's area to mass ratio and \(r\) is the spacecraft position vector to the sun. We set \(\eta\) as 0.5 and \(S/m\) as 0.01. The initial conditions are identical to test 3: The position errors of both the spacecraft are set as 10 m in all three directions randomly and the initial estimate of the gravity field is \(C_{20}=-5.20\times 10^{-2}\) and \(C_{22}=8.30\times 10^{-2}\). For an arc length of 50(T), the result is shown in Figure 11 and Table 8.  
For test 8, the residuals of observation are less than 0.3 m and the position error is less than 10 m. The accuracy of the gravity field is about 0.07%. Compared with test 3, the  
Figure 11: Residual in the observation data (**left**) and norm of the position error of the determined orbit from the true one (**right**) (test 8).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Initial Estimate** & **Test 4** \\ \hline \(C_{20}\) & \(-5.20\times 10^{-2}\) & \(-5.2475\times 10^{-2}\) \\ \hline \(C_{22}\) & \(8.30\times 10^{-2}\) & \(8.2476\times 10^{-2}\) \\ \hline
**Initial position error of** & 10 m & 9.88 m \\
**Satellite 1** & & \\ \hline
**Initial position error of** & & \\
**Satellite 2** & 10 m & 0.14 m \\ \hline
**RMS** & 8.1752 & 0.1306 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Recovery result of test 8.  
accuracy of the result is similar. It also demonstrates that the other perturbations, such as SRP, only have little influence in our case since we have a large asteroid.
## 2 The MPwise System  
### General  
The MPwise is an advancement from previous activities that set out to develop a wireless system for EEW and structural monitoring (e.g., [1; 3; 4]). One of the primary ideas behind this work was for the sensing units or nodes (when employed in a network) to be \"low cost\". That is, they are made up of off-the-shelf components, whereby they may be deployed in much denser networks than is possible with conventional seismic sensing equipment. Another of the concepts behind the development of this system was to shift part of the processing burden from a centralized location to the nodes themselves, connected via a wireless mesh network. There are several advantages of such a configuration:  
* The resulting system would be more resilient to external disturbances,
* Useful information could be already available at the node level for undertaking automatic (unsupervised) emergency measures,
* By processing data at the node level, potentially no raw-data needs to be transmitted across the network, freeing it for the circulation of only the resulting (higher-level) information while allowing a narrower communication band and prompter response of the system.  
The resulting network would be able to determine the optimal wireless communication path which would also be able to automatically adapt itself given changing circumstances (e.g., nodes becoming inoperable, interference, etc.).  
Since these initial stages, the units have advanced in step with the increasing capacity of off-the-shelf components, while ideas about how such a system may be exploited have also developed. Figure 1 shows the latest version of the MPwise, including an example of how its internal software may be used and displayed on the unit's screen (in this case for on-site early warning), while Table 1 lists examples of its possible use, such as pre- and post-event building monitoring, site amplification assessments and on-site EEW. Table 2 in turn describes how the different roles that may be played by MPwise units and networks will involve their various components (sensing, communications and processing). Details of the hardware, software, communications and processing characteristics of the current system are provided in the following sections.  
Figure 1: A MPwise unit. This version includes a touchscreen embedded in the housing. While this arrangement is not standard, an external touchscreen can be connected to all units. In this image, the touchscreen display shows the operation of the onsite EEW software described in [5; 6; 7]. The dimensions of the latest version are: length 20.5 cm, width 16 cm, and height 8 cm.
# Benefit Analysis of Grid-Connected Floating Photovoltaic System on the Hydropower Reservoir
## 2 Environmental, Technical, Financial Specifications of the Plant  
### Solar Radiation Data at the Plant Area  
Solar radiation data can be obtained from a variety of sources, for example [PERSON] et al. [36] from the National Aeronautics and Space Administration (NASA). The floating PV plant at Da Mi hydropower reservoir, Binh Thuan province is built in Tanh Linh district and Ham Thuan Bac district, Binh Thuan province. From the software Meteonorm 7, this calculated data is synthesized and interpolated from radiation measuring stations around the world. Combined with measured radiation data of satellites, the global horizontal irradiation GHI at the hydroelectric reservoir area is about 1777 kWh/m\({}^{2}\)/year, which is equivalent to 4.87 kWh/m\({}^{2}\)/day (Figure 1).  
Figure 1: GHI Map of theoretical average day recording of project area.  
The data collected from NASA's free public data source (at website: [[https://eosweb.larc.nasa.gov/sse/](https://eosweb.larc.nasa.gov/sse/)]([https://eosweb.larc.nasa.gov/sse/](https://eosweb.larc.nasa.gov/sse/)), accessed on 13 April 2017), the global horizontal irradiation in the plant area is 1874 kWh/m\({}^{2}\)/year, which is equivalent to 5.13 kWh/m\({}^{2}\)/day.  
The data collected from SolarGIS's free publicly available data source (at website: [[http://solargis.com/](http://solargis.com/)]([http://solargis.com/](http://solargis.com/)), accessed on 13 April 2017), the global horizontal irradiation in the plant area is 1846 kWh/m\({}^{2}\)/year, which is equivalent to 5.06 kWh/m\({}^{2}\)/day.  
Through research and evaluation, the PV plant is proposed to use data sources purchased from SolarGIS. This is a commercial database that is measured from satellites over a long time series, from which, the data are aggregated and calculated to produce a typical meteorological value of a typical year for the plant. The SolarGIS data source has been rated by many independent research organizations as the most reliable source of solar data.
## 2 Materials and Methods
### Total Protein Content  
The total number of proteins was determined using two methods: Bradford, which is a dye-binding assay, and Biuret, which is a copper-based method, measured against a bovine serum albumin (BSA) standard curve. In the case of Bradford, 200 \(\upmu\)L of 10\(\times\) diluted sample was mixed with 50 \(\upmu\)L of Bradford reagent in a 96-well microplate. The 96-well microplate was orbitally mixed at 100 rpm and read spectrophotometrically at \(\uplambda=595\) nm using a CLARIOstar microplate reader (BMG LABTECH, Ortenberg, Germany) after 5 min of incubation at room temperature. The calibration curve was prepared from a standard stock solution of 246 \(\upmu\)g/mL BSA in distilled water, which was subjected to the same treatment [37]. In the case of the Biuret assay, 40 \(\upmu\)L of undiluted sample were mixed with 200 \(\upmu\)L of Biuret reagent in a 96-well plate. The 96-well plate was incubated 30 min at room temperature. Absorbance was read with the microplate reader at \(\uplambda=540\) nm. The calibration curve was made from a standard stock solution of 10 mg/mL BSA in distilled water, which underwent the same preparation steps as in [38]. Because of the known interferences of certain compounds such as polyphenols and flavonoids with both assays [39; 40], the average value between the two methods was calculated.
## 5 Analysis of Experimental Results
### Discussion on LPM  
LPM demonstrates strong temperature compensation performance across different temperature regions, such as TTZ and HTZ. This is because LPM divides MS into multiple  
Figure 19: Variation curves of damage index for the BSS method and LPM method over time with the defect located in the heated area.  
Figure 18: The reflected signal of Defect 2 in pipeline at 24 \({}^{\circ}\)C.  
signal sub-sequences, allowing each to be locally matched with nearby BS. Cosine similarity effectively measures these local similarities, enabling the LPM to accurately match the phases despite a certain phase delay. Under the influence of an NTF, the local phase of the signal may lag or lead, but this change is localized. Therefore, precise phase information can be obtained by matching each local change, effectively overcoming temperature-induced phase variations.  
However, LPM primarily relies on calculating the local similarity between two signals sequentially for phase matching. When the temperature is too high, the signal phase delay increases significantly, requiring a broader local range to capture these changes. This can lead to incorrect matches and false positives during local matching.  
Since LPM is a signal processing technique, it can be applied across various pipeline materials and environments. For complex environments or structures, an appropriate signal excitation and reception system should be in place. Once a stable UGW signal is collected, LPM can be used to compensate for temperature variations. Consequently, the LPM method has broad applicability.
# Regression Equations for Estimating Landslide-Triggering Factors Using Soil Characteristics
## 3 Results
### Soil Properties of Landslide and Non-Landslide Areas
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Geology**} & \multicolumn{2}{c}{**Density, g/cm\({}^{3}\)**} & \multicolumn{2}{c}{**Water Content, \%**} & \multicolumn{2}{c}{**Liquid Limit, \%**} & \multicolumn{2}{c}{**Plastic Limit, \%**} \\ \cline{2-9}  & **Range** & **Average** & **Range** & **Average** & **Range** & **Average** \\ \hline Gneiss & 2.6–2.76 & 2.69 & 8.72–33.47 & 17.38 & 20.15–37.55 & 29.66 & 14.74–26.32 & 19.84 \\ \hline Granite & 2.52–2.72 & 2.62 & 9.49–31.84 & 16.23 & 23.97–37.15 & 30.10 & 13.77–27.00 & 18.97 \\ \hline \multirow{2}{*}{**Geology**} & \multicolumn{2}{c}{**Void Ratio**} & \multicolumn{2}{c}{**Porosity, \%**} & \multicolumn{2}{c}{**Dry Unit Weight, kN/m\({}^{3}\)**} \\ \cline{2-9}  & **Range** & **Average** & **Range** & **Average** & **Range** & **Average** \\ \hline Gneiss & 0.75–1.57 & 1.13 & 42.86–61.09 & 52.57 & 10.3–15.3 & 12.8 \\ \hline Granite & 0.76–1.29 & 1.03 & 43.18–56.33 & 50.49 & 11.5–14.7 & 13.0 \\ \hline \multirow{2}{*}{**Geology**} & \multicolumn{2}{c}{**Permeability Coefficient, cm/s**} & \multicolumn{2}{c}{**Cohesion, kN/m\({}^{2}\)**} & \multicolumn{2}{c}{**Internal Fiction Angle, \({}^{\circ}\)**} \\ \cline{2-9}  & **Range** & **Average** & **Range** & **Average** & **Range** & **Average** \\ \hline Gneiss & \(1.33\times 10^{-5}\)–\(2.17\times 10^{-4}\) & \(5.43\times 10^{-5}\) & 1.6–29.6 & 8.8 & 18–38 & 33 \\ \hline Granite & \(1.66\times 10^{-6}\)–\(5.16\times 10^{-4}\) & \(6.00\times 10^{-5}\) & 0.8–13.3 & 5.3 & 30–39 & 34 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Soil characteristics in landslide occurrence areas. Figure 5: Soil particle size distribution curves for soil samples collected from landslides and non-landslide areas in gneiss and granite regions.
## 3 Results and Discussion
### Adsorption Studies
#### Thermodynamic Studies
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Adsorbent**} & \(\Delta H^{o}\) & \(\Delta S^{o}\) & \multirow{2}{*}{**R\({}^{2}\)**} & \multicolumn{3}{c}{\(\Delta G^{o}\) (kJ\(\cdot\)mol\({}^{-1}\))**} \\  & **(kJ\(\cdot\)mol\({}^{-1}\))** & **(J\(\cdot\)mol\({}^{-1}\).K\({}^{-1}\))** & & & **25 \({}^{\circ}\)C** & **50 \({}^{\circ}\)C** & **70 \({}^{\circ}\)C** \\ \hline Fe & \(-\)28.1 & \(-\)86.9 & 0.998 & \(-\)2.16 & \(-\)0.05 & 1.77 \\ FeC & \(-\)38.5 & \(-\)111.7 & 0.999 & \(-\)5.18 & \(-\)2.27 & \(-\)0.17 \\ Fe/FeC & \(-\)74.3 & \(-\)216.9 & 0.988 & \(-\)9.91 & \(-\)3.72 & \(-\)0.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Thermodynamic parameters the adsorption the Acid Red 66 dye over the samples at distinct temperatures.
## 4 Secure Cooperation
### Secure Dispatching Service  
The SDS implements the cryptographic transformations that have to be applied to network traffic segments. A cryptographic transformation specifies the cryptographic processing to be applied to messages before sending or after receiving them. For instance, in order to guarantee both confidentiality and integrity of a message, a possible transformation is \(E_{e}(m\|h(m))\), where \(E\) specifies a symmetric cipher, \(h\) specifies an hash function and \(e\) is an encryption key. Alternatively, a transformation aimed at guaranteeing sole authentication of message \(m\) is \(m\|H_{a}(m)\), where \(a\) is an authentication key and \(H\) is a Message Authentication Code (MAC). The cryptographic suite, namely the actual cryptographic primitives that are used in a transformation, must be properly chosen because of the communication overhead it may imply.  
Figure 8: Area coverage performance with ten vehicles. The HVA is placed at the center of the sonars detection area.
## 2 Related Works
### Domain Adaptation  
In deep learning cases where the statistics of the trained data and the target are distinct, a deterioration in performance can occur. This is called the domain shift phenomenon. Ideally, this can be prevented by labeling large amounts of data in the target domain to additionally train the model in a method similar to transfer learning. However, in many cases, this solution is not available due to the extensive time and labor it requires. Domain adaptation aims to eliminate the domain discrepancy with little supervision on the target domain and improve model performance with annotated source data. Domain adaptation has been widely applied to classification tasks using distance metrics such as maximum mean discrepancy (MMD) to measure the domain shift and supervise the model to learn domain-invariant features [38]. Afterwards, an adversarial training strategy using domain classifiers and a gradient reversal layer (GRL) can be introduced to learn robust domain-invariant features during the training phase. By this means, the domain classifier and backbone model are mutually trained such that a classifier becomes better at distinguishing source and target domain features, while the backbone learns to generate more domain-invariant features as the gradient of classifier is reversely back-propagated to the base network. Domain adaptation for object detection inherits and extends the same training strategy. As a localization task is added to the classification task, the domain adaptation methods are first introduced to the two-stage detectors by applying the adaptation technique to each task [39; 40; 41]. There are only a few studies on domain adaptation applied to one-stage detectors. [PERSON] et al. [42] merged multiple pre-trained YOLO models using domain adaptation, while [PERSON] et al. [43] introduced multi-scale image level domain adaptation for the YOLO model.
## 3 Fundamental of Infrared Thermography  
In the IRT technique, an IR camera is used to measure the infrared radiation emitted from the structure surface, which then it is converted into the temperature. The total radiation energy received by IR camera (\(W_{tot}\)) includes the emission from the object (\(W_{1}\)), the reflected emission from ambient sources (\(W_{2}\)), and the emission from the atmosphere (\(W_{3}\)), as shown in Equation (1) and in Figure 2 [12; 30; 31].  
\[W_{tot}=W_{1}+W_{2}+W_{3}, \tag{1}\]  
where, \(W_{1}\), \(W_{2}\), and \(W_{3}\) are calculated from Equations (2)-(4), respectively [12; 30; 31].  
\[W_{1}=\tau\times\varepsilon\times\sigma\times T_{obj}^{4}, \tag{2}\]  
\[W_{2}=\tau\times(1-\varepsilon)\times\sigma\times T_{amb}^{4}, \tag{3}\]  
\[W_{3}=(1-\tau)\times\sigma\times T_{atm}^{4}, \tag{4}\]  
where, \(\varepsilon\) is the emissivity of the object, \(\tau\) is the transmittance of the atmosphere, \(\sigma\) is the Stefan-Boltzmann constant (\(\sigma\) = 5.67 \(\times\) 10\({}^{-8}\) W m\({}^{-2}\) K\({}^{-4}\)), \((1-\varepsilon)\) is the reflectance of the object, (\(1-\tau\)) is the emissivity of the atmosphere, \(T_{obj}\) is the temperature of an object, \(T_{abm}\) is the reflected temperature (from ambient sources), and \(T_{atm}\) is the atmosphere temperature. Substituting Equations (2)-(4) in (1), Equation (5) is obtained. Therefore, the object temperature can be determined using Equation (6).  
\[W_{tot}=W_{1}+W_{2}+W_{3}=\tau\times\varepsilon\times\sigma\times T_{obj}^{4 }+\tau\times(1-\varepsilon)\times\sigma\times T_{amb}^{4}+(1-\tau)\times \sigma\times T_{atm}^{4}, \tag{5}\]  
\[T_{obj}=\sqrt[4]{\frac{W_{tot}-\tau\times(1-\varepsilon)\times\sigma\times T _{amb}^{4}-(1-\tau)\times\sigma\times T_{atm}^{4}}{\tau\times\varepsilon\times \sigma}}, \tag{6}\]  
Figure 2: The principle of infrared thermography.  
It should be noted that the values of \(\varepsilon\), \(\tau\), \(T_{\textit{refl}}\), and \(T_{\textit{atm}}\) must be set up in the camera as initial parameters. The surface temperature of the object is automatically computed by the IR camera from Equation (6). In this study, the reflected temperature is considered equal to the ambient temperature as discussed in our previous study [12]. The emissivity of material is influenced by the type of material, surface, geometry, observation direction, wavelength, and temperature [32]. For example, the emissivity of the concrete of 0.92 can be used for a wide range of spectrum from 2 to 20 \(\upmu\)m, while it is around 0.974 for concrete pavement tested in the wavelength band of 6 to 20 \(\upmu\)m, and around 0.95 for dry concrete tested under temperature of 36 \({}^{\circ}\)C with the wavelength varying from 2 to 5 \(\upmu\)m [33]. In this study, the specimen was heated including an electrical tape piece glued on the surface. The temperature on the piece of electrical tape with known emissivity was employed to measure the surface emissivity of the concrete specimen. Thus, the value of measured emissivity of approximately 0.95 is set as a constant value during each test case.