esa-sceva/satcom-chunk-collection huggingface dataset:
# From Knowledge to Leverage: How to Use Musculoskeletal Simulation to Design Exoskeleton Concepts
## 3 Simulation-Based Design
### Conceptual Knee Exoskeleton Design  
Based on the selected joint reaction forces, the initial analysis indicated that an exoskeleton for this work task should offload knees and the spine, and since these are different body parts, an obvious initial approach was to design two exoskeleton components for the two knees and one for the spine.  
We began with the knees and added to the model costless reaction moments, i.e., category 2, for knee extension. In the context of the dynamic equilibrium Equations (1)-(3), these become motors that perfectly offload the knee extensor musculatures. The motors' moment contributions are shown in Figure 3 as functions of the knee angles. It is not surprising that such an idealized knee extension contribution almost eliminates the patella-femoral forces, as shown in Figure 4. The small remaining patella-femoral forces are due to activation of the biparticular rectus femoris muscle. Figure 3 shows the ideal motor compensations for the knee extension moments. They are complicated but can be approximated by a linear regression trend line. The trend line represents knee moments provided by a linear torsional spring with stiffness indicated by the slope, i.e., 1.38 Nm/degree. To avoid any action of the springs in the upright posture, we set the neutral position to zero degrees knee flexion. Replacement of the idealized motors in the model (category 2) with such springs (category 1) results in the patella-femoral forces depicted in Figure 5. As expected, this intervention has no influence on the spinal forces, but the patella-femoral forces in both legs are drastically reduced despite the crude approximation by simple, linear springs.  
Figure 2: Patella–femoral and L5–S1 reaction forces for the box-lifting task.  
The initial placement of the exoskeleton's knee extension moments among the dependent reaction forces of category 2 allowed us to arrive at optimal spring parameters in a single iteration. In the absence of musculoskeletal simulation, this progress would have required physical user tests and production of prototypes with multiple spring configurations.
# Propagation, Scattering and Defect Detection in a Circular Edge with Quasi-Edge Waves
## 4 Directivity Pattern of Scattered Waves  
In this section, the experimental validated FE model was employed to investigate the scattering characteristics of \(QES_{0}\) at a defect in the curved edge. The configuration of measurement points for calculating the scattering directivity pattern (SDP) is presented  
Figure 12: Comparison between numerical and experimental time domain data of MP1 in (**a**) intact and (**b**) presence of a 2 mm deep through-thickness edge defect.  
Figure 13: Ratio of (**a**) reflection and (**b**) transmission against the ratio of defect depth to wavelength.  
in Figure 9. To isolate scattered waves from the incident wave, the baseline subtraction method was employed. The normal displacement of scattered waves \(U_{S}\) is defined as  
\[U_{S}=U_{D}-U_{I} \tag{3}\]  
where \(U_{I}\) and \(U_{D}\) are the normal displacements of the measurement point in intact and damaged conditions, respectively. It should be noted that all SDPs in this study are normalised by the maximum absolute amplitude of the incident wave at the measurement point of \(0^{\circ}\) direction.
# MAC/PHY Comprehensive Visible Light Communication Networks Simulation
## 4 Proposed Simulation Scheme
### Channel Simulation
To provide an estimation of \(H_{0}\) it is necessary to aggregate the contributions from all the received rays. Moreover, using the link loss it is possible to determine the Signal-to-Noise Ratio (SNR) at the receiver considering both shot and thermal noise (Equation (8)):  
\[SNR=\frac{\left(P_{tx}H(0)R_{\lambda}\right)^{2}}{\sigma_{th}^{2}+2\eta(P_{tx}H(0 )R_{\lambda}+i_{d}+i_{b})B_{N}}. \tag{8}\]  
\(R_{\lambda}\) is the responsivity of the receiver, \(\sigma_{th}^{2}\) is the Johnson noise, \(q\) is the electron charge, \(i_{d}\) is the receiver's dark current, \(i_{b}\) is the background-induced current, and \(B_{N}\) is the noise bandwidth of the receiver. Traditional OWC systems work using a single wavelength (typically IR). Nonetheless, VLC systems use broadband optical emitters and the whole range of wavelengths should be used to calculate the signal power (numerator of the SNR). Nonetheless, an approximation can be carried out using the average wavelength [27]. The more scattered the CIR contributions are, the lower the channel bandwidth. The Root Mean Square (RMS) delay spread \(\tau_{rms}\) describes the channel's time dispersion (Equation (9), and can be used to get an approximation of the channel bandwidth \(B\) (Equation (10)). \[\tau_{rms} = \left(M_{2}-M_{1}^{2}\right)^{1/2} \tag{9}\] \[M_{i} = \frac{1}{H(0)}\int_{0}^{\infty}\tau^{i}h(\tau)\,\mathrm{d}\tau\]  
\[B\approx\frac{1}{5\tau_{rms}} \tag{10}\]  
Most MAC simulations do not take channel bandwidth into consideration. However, if the channel cannot support the minimum bandwidth requirements, the communication link would not be established. Situations in which a receiver does not have LOS with the access point may suffer from a reduced bandwidth performance. In this work this is taken into account to provide more realistic simulations. Commonly, these simulations use a huge number of rays to obtain high precision. Nonetheless, for MAC evaluation a faster calculation may provide sufficient information to carry out the simulation, since a rougher estimation of the parameters is enough to assert link quality. In this work, the channel estimation has been carried out using a small set of 500 rays. Despite the reduced number of rays, the accuracy of the estimation is not significantly impaired in terms of channel gain and bandwidth thanks to the use of a MMCRT algorithm that forces direct contributions after each rebound. This algorithm has been validated compared to [PERSON]'s recursive method, which is considered as the gold standard in OWC [28]. Although the simulator provides point-to-multipoint impulse responses without a significant time penalty (using a simultaneous calculation in a map of points), the number of simulations that must be performed is equal to the number of elements in the network. Each node (coordinator and users) inside the simulation environment needs to check the channel with all the other nodes. The sampling rate of the CIR between each link evaluation would depend on the nature of the scenario.
## 4 Composite Performance Analysis and Adaptive Performance Tuning  
From the expressions and properties before, in the process of IR and PFR, the performance is determined by composite influences by different parameters, including the inertia constant \(H\) and the speed droop \(R\), as well as the load deviation \(\Delta p\).  
In order to analyze the composite influences, four key performance indices (the magnitude of ROCOF, the frequency nadir, the settling frequency, and the settling time) are chosen. Figures 7-10 show the relations between the performance indices and the varying parameters. The range selections are set as follows: \(H\) varies from 3 to 10, \(R\) varies from 0.03 to 0.08, and the step increase of load varies from 0 to 0.06.
# Energy Efficiency Improvement of Diesel-Electric Trains Using Solar Energy: A Feasibility Study
## 6 Conclusions  
This paper presents a straightforward approach to evaluating the profitability and the impact of installing PV panels on the wagon's roof on the train's energy efficiency. The solution has been considered for six different journeys worldwide, with various solar resource potentials and meteorological weather conditions. The solar resource and the wagon's usage determine the level of solar energy percentage and economic feasibility. The minimum annual fuel reduction of auxiliary generators allowed using PV solar panels is above 50% in all cases and wagon classes, proving this solution's feasibility.  
Moreover, the payback period (ROI, years) is inversely proportional to the annual solar production (MWh) as well as to the CO\({}_{2}\) emission reduction revenues (\(\$\)). The payback period ranges from 2.5 years to 9.1 years, while the CO\({}_{2}\) emission reduction's revenue values range from $460 to $998 per year/wagon. These results are acceptable and appropriate for such projects.  
On the other hand, the effect of the low temperature on the profitability of this technique is insignificant. The profitability is strongly related to the solar resource (light) and travel duration, and the results for the London and Sydney cases prove that. Future research can perform tests on a batch of data using artificial intelligence or statistical analysis to pave the way toward intelligent management of energy systems in the Industry 4.0 era.  
Conceptualization, A.I. and H.I.; methodology, A.F., H.I. and A.I.; validation, H.I., A.I., S.S.K. and M.I.; formal analysis, A.F., H.I., A.I., S.S.K. and M.I.; writing--original draft preparation, A.F., S.S.K. and M.I.; writing--review and editing, A.F., H.I., A.I., S.S.K. and M.I.; visualization, A.F., S.S.K. and M.I.; supervision, H.I. and A.I.; project administration, H.I., A.I. and S.S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Government of Canada through Transport Canada \"Clean Transportation Program--Research and Development\".  
Not applicable.  
Figure 10: The ratio of solar energy to each wagon’s consumption.
# Enhancing Reliability Analysis with Multisource Data:
###### Abstract  
Data collected using sensors plays an essential role in active bridge health monitoring. When analyzing a large number of bridges in the U.S., the National Bridge Inventory data as been widely used. Yet, the database does not provide information about live loads, one of the most indeterminate variables for monitoring bridges. Such asymmetric information can lead to an adverse selection problem in making maintenance, rehabilitation, and repair decisions. This study proposes a data-driven reliability analysis to assess probabilities of bridge failure by synthesizing NBI data and Weigh-In-Motion (WIM) data for a large number of bridges in Georgia. On the resistance side, tree ensemble methods are employed to support the hypothesis that the NBI operating load rating represents the distribution of bridge resistance capacities which change over time. On the loading side, the live load distribution is derived from field data collected using WIM sensors. Our results show that the proposed WIM data-enabled reliability analysis substantially enhances information symmetry and provides a reliability index that supports monitoring of bridge conditions, depending on live loads and load-carrying capacities.  
reliability index; weigh in motion; WIM data; bridge maintenance; operating rating; load rating; machine learning; adverse selection; risk monitoring
# An Indoor DFEC Ranging Method for Homologous Base Station Based on GPS L1 and BeiDou B1 Signals
## 4 Positioning Analysis  
Through the above analysis, we can conclude that the maximum carrier phase difference fluctuation is within \(\pm\) 0.01 cycles, and that the ranging error based on the entire wavelength count estimate of the ranging error meets a probability of 90% within \(\pm\) 1 cycle. The worst ranging error of the distance difference between channels is about 4 cycles. In order to further verify the feasibility of this ranging method in subsequent positioning, we performed static positioning tests at the above five typical test points and compared them with the actual position parameters. The data output frequency was 5 Hz and the sampling time was 5 min. Figure 18 below shows the positioning error of a test point and a range that satisfies the positioning accuracy of 1 m. It can be seen from the figure that although there is a large fluctuation in the estimation of the whole cycles in the previous ranging stability test, it can still provide high-precision positioning by combining with the positioning algorithm. Based on this, we have carried out probability statistics on multiple positioning. Figure 19 shows that in the case of such ranging errors, the final positioning error is mainly about 0.6 m, and about 93% of the points meet the positioning accuracy of 1 m.  
Figure 17: The probability statistical distribution map of 1 and 26 points.  
Figure 18: Positioning accuracy tests.
## 2 Background
### Comparison of LPWAN Communication Methods
NB-IoT uses an existing network to cover a large area and consumes less power; thus, it is suitable for an ultra-low-power IoT business model with multiple devices over a wide area, such as water meter reading or location tracking devices [6]. Some NB-IoT applications include smart meters (gas, electricity), asset management (expensive equipment or personal vehicles), protection of the elderly, smart cities (facility management, environment/disaster monitoring), and industrial IoT (security, safety, environment, logistics) [8]. LTE Cat-M1 is a low-power wide-area technology created by 3 GPP for applications that require greater bandwidth than what NB-IoT can provide, and its download speeds are approximately five times faster. It is being deployed for applications such as smart metering, intelligent street lighting in smart cities, parking sensors and management, and vehicle management including vehicle tracking, telematics, and asset tracking. However, it is difficult to adopt because the scope of its application is not wide [9]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Division** & **Sigfox** & **LoRa** & **NB-IoT** \\ \hline Frequency & \begin{tabular}{c} Unlicensed ISM bands (868 MHz \\ in Europe, 915 MHz in North \\ America, and 433 MHz in Asia) \\ \end{tabular} & \begin{tabular}{c} Unlicensed ISM bands (868 MHz \\ in Europe, 915 MHz in North \\ America, and 433 MHz in Asia) \\ \end{tabular} & \begin{tabular}{c} Licensed LTE frequency \\ bands \\ \end{tabular} \\ \hline Bandwidth & 100 Hz & 250 kHz, 125 kHz & 200 kHz \\ \hline \begin{tabular}{c} Maximum \\ date rate \\ \end{tabular} & 100 bps & 50 kbps & 200 kbps \\ \hline \begin{tabular}{c} Maximum \\ payload length \\ \end{tabular} & 12 bytes (UL), 8 bytes (DL) & 243 bytes & 1600 bytes \\ \hline Range & 10 km (urban), 40 km (rural) & 5 km (urban), 20 km (rural) & 1 km (urban), 10 km (rural) \\ \hline \
\begin{tabular}{c} Interference \\ Immunity \\ \end{tabular} & Very high & Very high & Low \\ \hline
\begin{tabular}{c} Authentication \\ \& Encryption \\ \end{tabular} & Not supported & Yes (AES 128b) & Yes (LTE encryption) \\ \hline Allow private network & No & Yes & No \\ \hline Standardization & ETSI & LoRa-Alliance & 3 GPP \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of LPWAN technologies: Sigfox, LoRa, NB-IoT [2]. Figure 3: Sigfox Characteristics [4].
# Corrosion and Scaling in Geothermal Heat Exchangers
## 6 Degradation Mitigation for Heat Exchangers
### Failure Cases of Heat Exchangers  
[PERSON] et al. [52] highlighted a CS heat exchanger failure at a plant in the Taupo Volcanic Zone, New Zealand. CS heat exchangers are typically employed in situations where the geothermal fluid's temperature exceeds 180 \({}^{\circ}\)C, minimizing silica scaling, while stainless steel is used for lower-temperature fluids. In this instance, three heat exchanger tubes developed pits and perforations after 24 months of operation, with elevated arsenic levels detected near the pits. The authors attributed the perforation to galvanic corrosion induced by elemental arsenic deposition. Slow flow conditions in the middle tube row of the heat exchanger led to gas buildup (CO\({}_{2}\), H\({}_{2}\)S) in the upper part of the tube and ammonia in the lower part of the liquid phase, resulting in acidification of the condensing water between them. Countermeasures included redesigning the heat exchanger for uniform flow through multiple tubes.  
Stainless steel is a common material for heat exchangers but can experience localized corrosion in Cl\({}^{-}\) rich environments. [PERSON] et al. [10] recently reported a case of SCC failure in a tube and shell heat exchanger made of 316L stainless steel after one year of service (inlet temperature of 120 \({}^{\circ}\)C). The resulting crack is shown in Figure 4a. The failure resulted from multiple factors, including poor material quality and environmental conditions. SCC initiation was influenced by the unstable passive film, which was compromised by lower levels of nickel and molybdenum compared to standards, along with the presence of Cl\({}^{-}\) in the secondary fluid. The authors recommended replacing 316L stainless steel with the more SCC-resistant 2205 duplex stainless steel, relieving residual stresses before service, and reducing Cl\({}^{-}\) content in the secondary working fluid. [PERSON] et al. [115] demonstrated the superior SCC resistance of 2205 duplex stainless steel in a copper chloride environment at elevated temperatures (200 \({}^{\circ}\)C).  
[PERSON] et al. reported the premature failure of a cupronickel (CuNi10 Fe) shell-and-tube heat exchanger after only five months of service. Operating at 144 \({}^{\circ}\)C (inlet) to 27 \({}^{\circ}\)C (outlet) at a pressure of 2 bars, the failure was attributed to thermally induced fatigue, exacerbated by the presence of H\({}_{2}\)S in the geothermal fluid, resulting in sulphide-induced SCC as shown in Figure 4b. Thermal fatigue has also been identified as a cause for early heat exchanger failures in various materials, including copper [116], stainless steel [117], and low ferritic stainless steel (ASTM A213 grade T11) [118]. While these failures did not occur in real geothermal environments, it underscores the importance of considering thermal fatigue in geothermal heat exchangers, particularly when dealing with H\({}_{2}\)S-rich fluids. Residual stresses remain a critical parameter to address in such cases, as highlighted in the failure reported by [PERSON] et al. [10].
## 3 Methodology
### Quality Assessment
#### 3.3.3 Agreement Measures  
The consistency between HJ and the other two sensors was quantified by Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE):  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|f_{i}-y_{i}| \tag{4}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}|f_{i}-y_{i}|/y_{i}\times 100\% \tag{5}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}{(f_{i}-y_{i})^{2}}}{n}} \tag{6}\]  
where \(f_{i}\) and \(y_{i}\) are TOA reflectance at a sample location \(i\) derived from HJ and WELD, or NDVI values derived from HJ and MODIS, respectively, and \(n\) is the number of joint observations in the sample.  
Suppose that the correlation between HJ-1A/B images and WELD is linear. The ordinary least squares (OLS) regression was used to fit the linear model relationship:  
\[Y_{i}=B+AX_{i},\ i=1,2,3,4 \tag{7}\]  
where \(Y_{i}\) is the TOA reflectance of HJ composites in the \(i^{\text{th}}\) band, and \(X_{i}\) is the corresponding TOA reflectance of the same band of WELD. Values for slope (\(A\)), intercept (\(B\)), and fitting R\({}^{2}\) were obtained by regression analysis performed using the linear fit function from the Interactive Data Language (IDL).
## 3 Results and Discussion
### Behavior Study of the MSAMT
#### 3.3.5 Effect of the DSES  
To validate the impact of the DSES on the MSAMT, comparison studies of the proposed MSAMT and two various variants, i.e., the MSAMT-KRG which adopts the KRG model in local search and the MSAMT-fixed which carries out the original UES, were conducted on 20-dimensional test functions. Statistical results for the three variants are provided in Table 14. The MSAMT demonstrated a superior performance, achieving the best solution on 8 of the 14 test functions. The MSAMT-fixed beat the other two variants on four test functions, while the MSAMT-KRG exhibited the best results on two test functions. On all test functions, the MSAMT took less computational time than the other two variants. The results demonstrate the effectiveness of the DSES.
# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 4 A Connectivity Analysis Based on the CNN-BiLSTM-AM Model
### The Data Generation Method  
In maritime networks, vast historical datasets are generated during navigation, which can be used as samples for training. However, in this study, collecting these data within a short period is neither cost-effective nor feasible. To assess the effectiveness of the proposed algorithm and reduce the experimental costs, this paper designs a software-assisted data generation method to implement the channel model presented in Section 2 while simultaneously generating the navigation and hydrological parameters. Based on these data, the input data and output labels are generated and packaged into training and prediction sample sets. The following is a overview of Algorithm 1 in this method.  
**Step 1: Input Data Generation**  
0. Parameter Initialization  
1.1 Generation of Received Signal-to-Noise Ratios (SNRs)  
Generate an instance of \(\lambda\) through the uniform distribution \(U(\lambda_{l},\lambda_{u})\). This distribution models the bounded random fluctuations in the channel conditions.  
Generate the signal-to-noise ratios \(\boldsymbol{\gamma_{1}}\) and \(\boldsymbol{\gamma_{2}}\) using an exponential distribution with the parameter \(\exp(\lambda)\), aligning with the Rayleigh fading model theoretically.  
1.2 Generate \(\boldsymbol{d_{1}}\), \(\boldsymbol{d_{2}}\) using the uniform distribution \(U(d_{l},d_{u})\). This distribution represents the spatial randomness of the deployment of nodal ships within a pre-defined operational area.  
1.3 Generate \(\boldsymbol{h_{1}}\) and \(\boldsymbol{h_{2}}\) using the normal distribution \(N(\mu,\sigma^{2})\). This distribution is commonly applied in oceanography to modeling the variability in the wave heights under diverse sea conditions.  
1.4 Generate \(\boldsymbol{v_{1}}\), \(\boldsymbol{v_{2}}\), and \(\boldsymbol{\theta_{1}}\), \(\boldsymbol{\theta_{2}}\) using the uniform distribution \(U(v_{l},v_{u})\), \(U(\theta_{l},\theta_{u})\). These distributions capture the random characteristics of nodal ship movement while adhering to environmental and operational limitations.  
1.5 Pack data  
Repeat steps 1.1 to 1.4 for a total of 20,000 iterations.  
Reorganize all the data and package them into \(\boldsymbol{X}=[\boldsymbol{X_{t}},\boldsymbol{X_{p}}]\), where \(\boldsymbol{X_{t}}\) is the data used for training the model, and \(\boldsymbol{X_{p}}\) is the data for prediction.  
**Step 2: Output Data Generation**  
2.1 Use the Monte Carlo method to compute the connection probability for \(\boldsymbol{X}\).  
The computed result is packaged into \(\boldsymbol{Y}=[\boldsymbol{Y_{t}},\boldsymbol{Y_{p}}]\), where \(\boldsymbol{Y_{t