esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Applications for Short Reach Optical Communication
## 4 Machine Learning Applications for Indoor Optical Wireless Communication and Positioning
### Machine Learning Applications for Indoor Optical Wireless Communication System
#### 4.1.2 Machine Learning Applications for Indoor Near-Infrared Communication  
In the studies reviewed above, they all use the visible light range for data transmission. In addition to VLC systems, the ML technique has also been explored in OWC systems using the near-infrared wavelength band. In high-speed near-infrared OWC systems, the spatial diversity scheme has been considered to overcome the channel blockage problem and to avoid possible communication interruptions. However, the delay between different channels may cause a severe ISI problem. The ML technique has been investigated to solve this issue, where the study [134] demonstrated a RNN-based symbol decision scheme to solve fractional symbol-period delays (i.e., delays less than one symbol period) induced ISI. The proposed RNN algorithm has three layers: the input layer (IL), the bi-directional LSTM layer (BL) and the output layer (OL). At the IL, each symbol sequence is divided into multiple groups, which are fed into the BL sequentially. BL consists of two LSTM sub-layers: a forward layer and a backward layer for learning the dependency among the data. Finally, the outputs from the BL are cascaded and passed through a softmax layer in the OL to obtain the symbol decision. The capability of RNN has been experimentally demonstrated, where more than one-order-of-magnitude BER improvement has been achieved when compared with traditional method (i.e., selecting the optimum sampling points), and the RNN also performs better than the previously studied FC-NN method.  
To handle longer channel delays (i.e., delays more than one symbol period), an attention-augmented long-short term memory (ALSTM) RNN symbol decision scheme has been further developed [135], where an additional attention layer is added before the output layer. The attention layer keeps the states of LSTM cells at all processing steps, and hence, the dependencies among the received symbols can be better captured, especially among non-neighboring symbols. The ALSTM based method shows over one-order-of magnitude BER improvement compared with the RNN without attention layer.  
In addition, previous studies mainly implement the ML algorithms using high-end GPUs, which are typically expensive and power hungry, limiting practice applications (particularly the short-range indoor applications). To solve this problem, a FPGA-based RNN hardware accelerator has been proposed for indoor near-infrared OWC systems [136].  
Results show that compared with GPU-based implementations, similar BER performance can be obtained, while the latency and power consumption can be reduced by 61% and 58.1%, respectively.
# Construction Schedule versus Various Constraints and Risks
## 3 Materials and Methods
### Multivariate Method of Statistical Models--Mmsm  
Even the most effective task scheduling method will be ineffective if the data used are not reflective of actual conditions. Accurately estimating the duration of construction processes is crucial. Determining the implementation time of construction processes with certainty is challenging due to high levels of uncertainty. Therefore, the Multivariate Method of Statistical Models (MMSM), an artificial intelligence-based methodology, was used to develop a new calculation method for schedules. This method allows for the determination of construction process times in the form of a probability density function.  
The Multivariate Method of Statistical Models differs significantly from previous methods. Determining the implementation times of construction processes involves using statistical and forecasting methods and models while considering factors that affect the execution time of tasks. These factors include organizational, technical, technological, cubature, and resource factors in numerical or linguistic form. When predicting the duration of the construction process, objective measurements and data collected from real conditions or construction company databases can be used. Based on these data, a regression equation can be generated to predict the implementation time of the construction process in new conditions. The regression equation can be affected by various variables, which should be taken into consideration when making predictions. The more data and types of data we have, the more accurate (as close to real as possible) the time can be determined. For example, when wanting to determine the time of reinforced concrete works, influence factors such as the height, length and width of the element, the reinforcement area, the degree of reinforcement, the team size and other factors are determined. After performingthe calculations, a regression equation is obtained, which generates the work completion time, which depends on many influencing factors.  
The following stages of work can be distinguished in the Multivariate Method of Statistical Models:  
* Defining the problem;
* Obtaining and analyzing data;
* Variable analysis;
* Analysis of the occurrence of linear correlations of variables;
* Predictive modeling with application: Multiple Regression, Multivariate Adaptive Regression Splines, Generalized Additive Methods, Spiking Neutral Network, Support Vector Machine;
* Checking the correctness of calculations.
## 2 Materials and Methods
### Case Study Results  
Considering the 3495 flight hours of four \(Z\)-142 aircraft and 33 failures that occurred on the airframe, which can be seen in Table 2, it was possible to calculate the MTBF for the airframe over monitored period, which represented 105 h.  
\[MTBF_{Z\text{-142 }Airframe}=105\text{ h} \tag{12}\]  
The airframe of the aircraft showed the highest failure rate, as can be seen in Figure 3, as it was possible to claim from the total number of failures that were recorded on it. The airframe represents 117% higher intensity of failures than other monitored aircraft's critical parts. The reason might be that the airframe includes much more elements than other monitored critical parts of the aircraft. The second reason might be its higher operating load during pilots' training, which affects the components' service life significantly. Figure 2 shows that the aircraft damage was found both on the outer surfaces and in structural construction.  
During the monitored period, the propellers showed the highest probability of failure-free operation, which is related to their lowest failure rate. The lowest FFOP of all critical parts represented the airframe, because of its frequent failures [24], as shown in Figure 4.  
The most common cause of failures of the Z-142 aircraft was mainly the operational wear of components, and it accounted for up to 90% of all reported failures. Based on this phenomenon, it could be noticed that the aircraft is used frequently and requires regular maintenance and inspections, especially of critical elements and elements with a limited service life. Only three failures had no identified cause.
# Comparative Analysis of LiDAR and Photogrammetry for 3D Crime Scene Reconstruction
## 4 Results and Discussion
### Model 2: Photogrammetry with an iPhone and pix4d Catch 3D App Results  
The first scan was conducted during daytime for 10 min, and 1483 images were captured, with 8,450,137 point clouds registered. It had an overall mean absolute error (MAE) of 0.017 m, which could be considered the highest accuracy achieved among all four scans of the photogrammetry approach. In the second scan, the duration was reduced to 5 min, capturing 745 images, which reduced the gathered point clouds to 4,546,991, resulting in an overall error (MAE) of 0.020 m. This result is expected since the scanning duration was reduced, which affected both the collected point clouds and produced lower performance than the scan under the same lighting conditions with a longer duration (the first scan). The third scan was taken during the nighttime to assess the effect of the lighting conditions on the 3D model results while maintaining the same duration as the first scan (10 min), resulting in 994 images with 2,219,352 point clouds. The resulting error was 0.030m, showing that the poor lighting conditions degrade performance. The fourth scan was performed during the nighttime, while the scanning duration was reduced to 5 min, capturing 483 images with 1,529,689 point clouds. It had an overall error of 0.032 m.  
Figure 4: Three-dimensional map results of Model 1 using LiDAR with four different scanning scenarios. Although the nighttime scans are visually poor, their maps are more accurate due to the minimal sunlight interference.  
In this scan, the darkness during the nighttime lighting conditions and reducing the scanning duration led to fewer point clouds, significantly reducing the accuracy. Figure 5 compares the mapping results from photogrammetry, showing that Scenario 1 has the most accurate map in terms of accuracy and visual representation, whilst Scenario 4 has the worst performance.
## 4 Proposed Framework
### Handling Emergency Access  
Different techniques can be used to handle emergencies:  
* The decentralized identifier (DID) used in Indy is a W3C standard [30]. The main information in a DID record is the DID number and its owner's public key. However, it may contain many other information useful for different contexts. For example, we find the authentication protocols that the owner can use with verifiers. Additionally, the DID record may contain delegation information giving other public keys of other subjects that can act on behalf of the owner and can be used for emergency access. In addition, the DID record is designed to be extensible so that we can add other fields useful for our specific contexts. In particular, we can add fields for the contact information related to delegated people for emergency; we gave their public keys in the delegation field. Another interesting feature provided by the DID is the ability to specify different verification and authentication methods. We can, for example, use a threshold signature. In this case, we can specify \(n\) keys in the delegation part, and require that at least \(k\) of them need to sign a challenge to able to behave on behalf of the owner. Using this mechanism, it is even possible to revoke a key according to the owner requirement. * We can simply use the secret splinting technique to achieve this goal. The idea is as follows: To be able to act on behalf of [PERSON] during an emergency, trustees need to know the DID of the vital attributes and the private key \(k_{b}^{-1}\) associated to it. However, [PERSON] does not want to give \(k_{b}^{-1}\) to a particular trustee, but he allows any subgroup of trustees, with a predefined size, to reveal this private key. To this end, we can use the threshold cryptography (also called secret splitting) like [31]: the key \(k_{b}^{-1}\) is encrypted by a random secret key \(k\) that can be revealed only by a group of trustees with the appropriate size. A common way to achieve this goal is to generate a random polynomial \(P(x)\) of degree \(l-1\), where \(k=P(0)\), then we generate \(n\) points in the polynomial. Every trustee receives only one point on the polynomial. In this case, we are sure that at least \(l\) users are needed to rebuild the polynomial and find the value of \(k=P(0)\) using the famous Lagrange equations [32].
## 5 Conclusions  
Fatigue tests were conducted on rail and wheel steel specimens to obtain FCG rates under mixed mode I/II loading cycles, which can simulate rolling contact conditions, by using an in-plane biaxial fatigue machine. Simplified cycles were applied to the analysis of RCF cracks, including the effect of fluid trapped inside the cracks. To elucidate the experimental results, a FEA was also performed. The main findings can be summarized as follows.  
(1) When the stress intensity range ratio between mode II and I increased, the crack tended to branch. This happened because, when this ratio increased, the direction of the maximum tangential stress range changed from coplanar growth to branch one.  
(2) As the degree of overlap between the mode I and II stress intensities increased, the crack easily branched; in particular, the MTS range became superior to the maximum shear stress range and its plane oriented toward the branch direction.  
(3) Coplanar crack growth rates were lower in head hardened rail steel than in normal rail steel. This was due to the smaller CTDs in head hardened rail steel, which can influence the growth rate, compared to normal rail steel.  
(4) The branch crack growth rate varied considerably depending on the stress intensity range ratio even within the same rail steel and, unlike the coplanar growth rate, could not be correlated by a single line because the plastic zone size increased with the shear part of the loading as a result of the \(T\)-stress and this larger plastic zone gave higher growth rates.  
(5) Clear striation patterns were not found near the crack tip region. Therefore, the main driving force for the crack growth under the investigated non-proportional mixed mode I/II loading was thought to be the mode II loading.  
This research received no external funding.  
The author thank [PERSON] of the Japan Aerospace Exploration Agency (JAXA) for his support in developing the test machine.  
The author declares no conflict of interest.
## 2 Literature Review
### Data Acquisition Process for PCAM  
Data acquisition has three aspects, namely, registration, noise removal, and data reduction that make a point-cloud meaningful to the CAD systems [36]. The registration process combines all the fragmented point-clouds into a coordinate system. The noise removal process removes the unwanted noises from the point-cloud. The data reduction process removes the redundant points from the point-cloud. It is worth mentioning that registration and noise removal processes are sometimes inseparable. These two aspects are described as follows.  
Point-cloud registration methods are classified broadly into three categories: greedy searching-based, global feature-based, and local feature-based methods. See [37] for the details. To ensure an accurate and efficient point-cloud registration and noise removal, numerous researchers have developed different techniques or algorithms. Some of the selected research are briefly discussed as follows. [PERSON] and [PERSON] [38] have developed a modified iterative closest point algorithm based on dynamic adjustment factor for the registration of point-clouds and CAD modeling. The registration time is reduced significantly and registration accuracy is improved considerably, but the method's convergence process is very slow and needs many iterations. [PERSON] and [PERSON] [39] have implemented machine learning algorithms combined with the shape descriptor to classify the 3D point-clouds. [PERSON] et al. [31] have developed a photogrammetry color segmentation based technique as an alternative approach to scan and construct 3D point-cloud for RE applications. [PERSON] et al. [40] have shown various ways for aligning the short-range point-clouds (captured by using a portable laser scanner) to a large-scale point-cloud (captured by using a terrestrial laser scanner). They aligned the point-cloudsin a reasonable time with good precision. [PERSON] et al. [6] have implemented different types of data acquisition methods, such as engineering drawing, computed tomography, and optical coherence tomography, to build the 3D printed bio-models for medical applications. They observed that the data acquisition and conversion technique had a significant effect on the quality of the model. [PERSON] et al. [41] have investigated the influence of the surface characteristics on the point-cloud. They acquired the point-cloud by using a laser scanning probe and observed that the surface characteristics affect the quality of the point-cloud, no matter the scanning accuracy. On the other hand, regarding the data reduction process, [PERSON] and [PERSON] [42] have developed an algorithm to remove the redundant points from a point-cloud to reduce the execution time for integration of RE and RP. Removing the redundant points from the point-cloud saves the computation time to a large extent.
# Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network
## 3 Methodology  
### BiLSTM Networks  
The structure of BiLSTM is shown in Figure 4a, and the structure of long short-term memory network (LSTM) is shown in Figure 4b. In the LSTM structure, the output result at time t is only related to time t - 1, whereas in the BiLSTM structure, the output result at time t is related to both t - 1 and t + 1, and this structure can effectively use all information of the past and future for training [37]. In [38] and [39], BiLSTM was used for time-series data processing, and it was found that BiLSTM is faster and more accurate than LSTM and standard recurrent neural networks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ST Amplitude Forecast Task** & **Daily Average ST Forecast Task** \\ \hline Hour of the day & Month \\ Air Temperature Maximum & Day of the month \\ Air Temperature Minimum & Air Temperature Observed \\ Wind Speed Average & Air Temperature Maximum \\ Solar Radiation Average & Air Temperature Minimum \\ Relative Humidity Minimum & Wind Speed Average \\ Relative Humidity Maximum & Solar Radiation Average \\ Vapor Pressure & Dew Point Temperature \\  & Relative Humidity Minimum \\  & Vapor Pressure \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input variables for the forecast models.  
Figure 3: Correlation coefficients of daily average (x) weather parameters and (y) ST for different depths.  
Equations (2)-(5) describe the calculation process of BiLSTM, where \(h_{t}\) is the output result of the forward layer at time t, \(h^{\prime}_{t}\) is the output result of the backward layer at time t, \(\hat{y_{t}}\) is the network output that combines \(h_{t}\) and \(h^{\prime}_{t}\), \(f\) and \(g\) are nonlinear activation functions, and the goal of network training is to minimize the mean square error (MSE) function \(s\) based on the target value \(y_{t}\).  
\[h_{t}=f(\text{UX}_{t}+\text{W}h_{t-1}) \tag{2}\]  
\[h^{\prime}_{t}=f(U^{\prime}X_{t}+W^{\prime}h_{t+1}) \tag{3}\]  
\[\hat{y_{t}}=g\big{(}V^{\prime}h^{\prime}_{t}+Vh_{t}\big{)} \tag{4}\]  
\[s=\frac{\sum_{t=1}^{n}(y_{t}-\hat{y_{t}})}{n} \tag{5}\]
# Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization
## 6 Results and Discussion
The DEKF-DR performed better as the average localization error is about 0.3 m on the x-axis, 0.1 m on the y-axis, and 0.18 on the z-axis, as shown in Figure 6b-d, respectively. In the same figure, the box plot represents the median of the localization error. Here, the median error for x position is less than 0.2 m compared to 1.3 m and 0.92 m for DEKF and EKF-Centr, respectively. Similarly, the median error for the z position is less than 0.29 m for the DEKF-DR compared to its counterparts. These results further verifies the superiority of the DEKF-DR to detect the jammer location in three-dimensional space. It can be seen from these results that the conventional EKF has reached sometime a very low error close to zero. However, its error is highly fluctuating and sometimes reaches to very high value. This is also illustrated in the statistical information provided by the box plot. These results show highly inconsistent behavior of estimation by the EKF, which is not desirable in real practice. On the other hand, the proposed EKF-DR has consistent estimation performance with lesser median localization error. Moreover, the DEKF-DR has lesser computational complexity than its counterparts. Furthermore, Figure 7a shows that the Euclidean distance error to the jammer. Here, it can be observed that the DEKF has poor performance, among others. This is due to the fact that it only uses one node information, which is insufficient for the Kalman filter to estimate the unknown position. On the other hand, the EKF-Centr is designed to receive the localization information from all four different nodes and, hence, it performed better than the DEKF. Finally, the DEKF-DR showed lowest distance error. as illustrated in Figure 7b. Its median error is about 0.3 m when compared to 0.7 m and 0.4 m for the DEKF and the EKF-Centr, respectively. In order to measure the performance of the proposed method, we run the simulation 500 times and 1000 times, as reported in Figure 8a,b, respectively. The average error of the DEKF-DR is around 0.56 m. We estimated the jamming power at different points using the distance ratio as shown in Figure 9. The jamming signal received by boundary node presented in Figure 9a and jamming signal at the edge node and from the edge to the boundary node presented in Figure 9b,c, respectively. Figure 6: _Cont._  
Figure 8: Average Error (**a**) 500 iterations and (**b**) 1000 iterations. Figure 6: Representation of position error. (**a**) express the location errors at each time step on x, y, and z coordinates. the median error for three coordinates represented by the box plot in (**b–d**). Figure 7: Distance Error. (**a**) Euclidean distance error; (**b**) lowest distance error.
## 3 Results  
### MOD17 Validations  
In this study, we first compared the MOD17 with the measured GPP (GPP_EC) (Figure 2). The R\({}^{2}\) equals 0.58 and RMSE equals 67.19% for the overall data between the GPP_EC and MOD17. For the five land cover types, MOD17 for grasslands generally shows a better correlation with GPP_EC, with R\({}^{2}\) being 0.90. MOD17 for croplands and MF also show relative high correlations with the measured fluxes (R\({}^{2}\) are 0.8 and 0.76, respectively); however, low correlations have also been found for ENF and EBF (R\({}^{2}\) are 0.44 and 0.21, respectively). These differences may be due to the fact that the MOD17 algorithm generally has a good performance when the measured GPP value is relatively small (GPP_EC \(<\) 40 gC/m\({}^{2}\)/eight-day or lower). With increasing GPP_EC values, MOD17 gets more dispersive, especially in ENF and EBF. Although a previous study indicated that the MODIS algorithm was poor at estimating GPP in MF or open scrubland [26], our results show that MODIS-GPP performed better in MF but worse in EBF and ENF.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\mathrm{T}_{\mathrm{min}}\)**(\({}^{\circ}\)C) [8]** & \(\mathrm{T}_{\mathrm{max}}\)**(\({}^{\circ}\)C) [8]** & \(\mathrm{T}_{\mathrm{opt}}\)**(\({}^{\circ}\)C) [25]** \\ \hline CBS & 0.0 & 50.0 & 20.7 \\ DHS & 0.0 & 50.0 & 23.4 \\ QYZ & 0.0 & 50.0 & 22.4 \\ XSBN & 0.0 & 50.0 & 23.4 \\ YC & 0.0 & 50.0 & 24.8 \\ HB & 0.0 & 50.0 & 15.4 \\ XLGL & 0.0 & 50.0 & 15.4 \\ DX & 0.0 & 50.0 & 15.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Models’ temperature parameters in different land cover types. \(\mathrm{T}_{\mathrm{min}}\), \(\mathrm{T}_{\mathrm{max}}\), and \(\mathrm{T}_{\mathrm{opt}}\) are the biological minimum temperature, maximum temperature, and optimal temperature during the growing season.
## 3 Results and Discussions
### Photodegradation on BHA
#### 3.3.3 Stability of RE/TiO\({}_{2}\)  
Reusability is an important indicator in evaluating the photocatalyst except for their photoactivity in the practical application. RE/TiO\({}_{2}\) composites were used four times to test their reusability on the photodegradation of BHA. As seen in Figure 9, during the last three runs, the removal rate only decreased about 0.8%-3.4% compared with the first run, which indicates that RE/TiO\({}_{2}\) continuously keeps the good photodegradation effect on BHA, and confirms the potential ability in engineering applications.  
Figure 8: Effect of calcination temperatures on the photodegradation of BHA by RE/TiO\({}_{2}\). (**a**) 0.75%La/TiO\({}_{2}\); (**b**) 0.20%Ce/TiO\({}_{2}\); (**c**) 0.70%Gd/TiO\({}_{2}\); (**d**) 0.50%Yb/TiO\({}_{2}\).