esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Machine Learning Methods to Up-Scale Gross Primary Production
## 4 Discussion  
### Sensitivity of the Input Data to the Accuracy of Up-Scaled GPP  
The uncertainty of the input datasets would have some influence on the accuracy of the up-scaling process. To study how the uncertainty in the output of the up-scaled GPP from machine learning methods can be apportioned to different sources of uncertainty in the inputs data (NDVI, FVC, SWR, RH, Ta), global sensitivity analysis was carried out by using an extended Fourier amplitude sensitivity test (EFAST) [75; 76] in this paper. From Table 2, we found that NDVI was the most sensitive variable to the up-scaled GPP, using the five machine learning methods; it was followed by FVC and SWR. RH had the least sensitivity to the up-scaled results. Local sensitivity analysis of the input data to the up-scaled GPP was shown in Table 3. A 0.60-0.86% change would happen in the up-scaled GPP when RH increased by 10% or decreased by 10%, and a 5.77-7.35% change would happen when RH increased by 50% or decreased by 50%. More than 5% change would happen with the corresponding change of 10% in NDVI, SWR and FVC, and about 16.21-36.81% change would happen with the corresponding change of 50% in NDVI, SWR and FVC. From Tables 2 and 3, we found that up-scaled GPP was sensitive to NDVI and FVC, which were considered to be the indicators to describe the vegetation phenology, and to monitor the terrestrial photosynthetic vegetation activity. SWR had an impact on the photosynthetic intensity; therefore, it was well related to the temporal changes in GPP. While RH and Ta were not sensitive to the changes in GPP compared to NDVI, FVC and SWR.  
Figure 12: Pearsons of up-scaled GPP using (**a**) Cubist, (**b**) RF, (**c**) ANN, (**d**) SVM, (**e**) DBN with MODIS GPP.
# Simultaneous Transmission of Photonic Services over One Fiber with an ITU 100 GHz Grid
## 3 Measurement Setup
### Accurate Time Measurement Method
In the first case, there will be a challenge with the asymmetry of the physical length of the fibers; in the latter case, the optical propagation velocity will slightly differ, as it is given by the coefficient of chromatic dispersion, which generally depends on the wavelength. In both of these cases, there will be an asymmetry of delay \(\Delta\):  
Figure 1:1  
which can be calculated in the case of a bidirectional transmission in a single fiber. The difference in propagation times is shown in Equation (12). \(L\) is the fiber length, \(d\) is chromatic dispersion coefficient and D is the chromatic dispersion of passive components. Figure 1:2  
As mentioned above, the wavelength and polarization sensitivity of the refractive index need to be taken into account. For example, \(L=95\,\mathrm{km}\), \(d=17.5\,\mathrm{ps/nm/km}\), \(\Delta\lambda=0.8\,\mathrm{nm}\), and \(D=200\,\mathrm{ps/nm}\) results in the propagation time difference of \(1.49\,\mathrm{ns}\). Otherwise, in the case of the time transfer in a pair of unidirectional fibers, there is no possibility of calculating the delay asymmetry, as we cannot guarantee the same physical length of both fibers--the delay asymmetry must be evaluated during the phase of the calibration of the transmission route. Therefore, the time offset of the clock is given as follows:  
Figure 1:3
## 2 Fixed Tilt Angle System vs. Horizontal Single-Axis Tracker
### Differences Between PV Module Mounting Systems Under Study
Therefore, horizontal single-axis trackers are more susceptible to this type of damage. * Limitation of geographical location. It has been demonstrated that the efficiency of fixed tilt angle systems decreases considerably in locations far from the equator [18], as their capacity to capture solar resources is not adequate throughout the year. In contrast, horizontal single-axis trackers are better suited to different latitudes [18], expanding the range of suitable geographical locations. * Land occupation. A report by Mordon Intelligence [38] indicates that the density of \(PV\) modules per square metre using horizontal single-axis trackers is between 25% and 35% higher than the use of fixed tilt angle systems. * Budgetary considerations. When making a decision about the choice of \(PV\) module mounting system, the initial investment must be weighed against the expected return on investment. Although horizontal single-axis trackers offer higher performance, they also offer higher initial costs. In contrast, fixed tilt angle systems offer the opposite. Therefore, it may happen that the higher initial cost of a project makes it unviable. * Environmental impact. From the point of view of environmental impact, horizontal single-axis trackers offer greater environmental benefits, as they generate more energy [18].
## 4 Conclusions  
Predicting the remaining useful life of bearings has been an important task for condition-based maintenance of industrial machines, as bearings are one of the most critical components in these machines. To meet the challenge of automatically processing massive data and accurately predicting RUL of the bearings in the era of the Internet of Things and Industrial 4.0, this paper addressed the limitations of traditional data-driven prognostics by presenting a new method that integrates a deep belief network and a particle filter for RUL prediction of hybrid ceramic bearings. Real vibration data collected from hybrid ceramic bearing run-to-failure tests were used to test and validate the integrated method. The performance of the integrated method was also compared with DBN and particle filter-based approaches. In comparison with the RUL prediction results obtained using the particle filter-based method, the prediction accuracy measured by RMSE and MAPE of the integrated method is slightly lower. Also, based on the previous comparison between DBN and the particle filter methods for the same dataset reported in [28], the RUL prediction performance of the integrated method is considered better than the DBN method. The validation and comparison results showed the promising RUL prediction performance of the integrated method.  
Since the integrated approach was a purely data-driven approach, future work should focus on increasing user confidence in the method. This may be accomplished by combining the integrated approach with other techniques such as employing the use of fuzzy similarity [41]. This would require a large database of run to fail trajectories that can be compared based on their similarity with the observed data. This large database should not only increase the accuracy of the integrated approach, but also further validate it. Additional work may also include the processing of the raw vibration signals at each sampling interval by a DBN into a single dimension signal feature that can be used for prognostics.  
[PERSON] conceived the idea and formulated the mathematical models behind the provided approach. In addition, he was responsible for all parameter tuning, data processing, figures, and calculations. [PERSON] was responsible for the run to fail trajectories of the hybrid ceramic bearings in the experimental setup. [PERSON] was responsible for research idea generation, the introduction, abstract, conclusions, and for the final editing and reviewing of the provided approach.  
The authors declare no conflict of interest.
# Multihop Latency Model for Industrial Wireless Sensor Networks Based on Interfering Nodes
## 2 TSCH and RPL Network Deployment
The nodes that are trying to join the topology will start a process of sending DIS messages to request the necessary information to join the RPL topology. These messages will be transmitted with a fixed period, which will be configured by default in the equipment. On the other hand, the nodes already connected to the network will be sending DIO messages using the Trickle Timer mechanism, which modifies the period with which these messages are sent. The Trickle Timer mechanism allows an increase in the time between DIO messages, so that if the network remains stable, DIO messages are sent much less frequently. The only way to reset the Trickle Timer is when there is a change in the topology, such as a change of parent, or when a DIS message is received from another device. Figure 2 shows the RPL message exchange mechanism after a new node has synchronized. The figure shows how the node that is already part of the network modifies the period with which DIO messages are transmitted, and how when a DIS message is received by the new node the timer used by the Trickle Timer is restarted. In this way, it is forcing DIO messages to be sent more frequently to encourage the RPL tree to be updated. The connection to the RPL topology only occurs when the new nodes receive a DIO message with information about the topology. Finally, for the creation of downstream routes, the nodes that have just joined the RPL topology must use that link to the root node to transmit a DAO message, so that all nodes on the path know the downstream route to the new node. Figure 3 shows the comparison between a multi-hop message exchange, such as the DAO message, and a multicast message exchange, which only reaches nodes within the coverage area. Unlike the previous processes, the DAO message is a unicast transmission, while EBs and broadcast DIO messages are transmitted in multicast to all nodes in the WSN network. This means that both EB and DIO transmissions will be single-hop transmissions to nodes within the coverage area, while the DAO message must be sent all the way to the root, so that in  
Figure 1: Example of the beacon sending process using four channels. multi-hop meshed networks it must be forwarded over several hops until it reaches its destination. The RPL protocol allows this process to be configured so that, in addition to sending a DAO message from the new node to the root, a DAO-ACK is sent to confirm that the root node has correctly received the message.
## 5 Novel Treatment Strategies Using Phyto-Nanomedicines
### Phyto-Nanomedicines for the Treatments of Cancer
The BRN demonstrated that HB-AuNPs produced from _Cyclopia intermedia_ (honeybush) significantly increased the cytotoxic effects of doxorubicin in certain human cancer cell lines and that there is a possible synergistic effect between the HB-AuNPs and doxorubicin [36]. The significance of this study is that the therapeutic effects of doxorubicin, when used in combination with HB-AuNPs, were six-fold lower than the concentrations required when doxorubicin was used alone. In addition to the drug-sensitizing effects of the HB-AuNPs, we have also shown that the HB-AuNPs have selective anti-cancer effects on the brain (U87) and prostate (PC-3) cancer cell lines [36]. These studies suggest that the dose of chemotherapeutic drugs can be significantly reduced if used in combination with phyto-nanomedicines. Similarly, MNPs synthesized from _Terminalia mantaly_[40], Acai berry, Elderberry [46], _Xylopia aethiopica_[53], and _Sargassum incisifolium_[51] demonstrated anti-cancer activity. The production costs of phyto-nanomedicines, such as HB-AuNPs, are very low compared to chemotherapeutic drugs. In addition to reducing the cost of cancer treatment, this approach can potentially also reduce some of the side effects caused by chemotherapeutic drugs.
# Influence of Some Additives on the Properties of OPC Solidified Sandy Silt
## 3 Results
### Indirect Tensile Strength
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Additives**} & \multirow{2}{*}{**Content/\%**} & \multirow{2}{*}{**Mixture**} & \multicolumn{5}{c}{**Curing Days**} \\ \cline{3-8}  & & & **7** & **14** & **28** & **60** & **90** \\ \hline None & 0 & S11 & 1.46 \(\pm\) 0.13 & 2.03 \(\pm\) 0.19 & 2.43 \(\pm\) 0.18 & 3.39 \(\pm\) 0.22 & 3.44 \(\pm\) 0.23 \\ \hline \multirow{3}{*}{Gypsum} & 2 & SS2 & 1.43 \(\pm\) 0.21 & 2.26 \(\pm\) 0.18 & 2.89 \(\pm\) 0.24 & 3.45 \(\pm\) 0.21 & 3.46 \(\pm\) 0.25 \\  & 3 & SS3 & 0.81 \(\pm\) 0.09 & 1.42 \(\pm\) 0.12 & 2.61 \(\pm\) 0.18 & 3.41 \(\pm\) 0.21 & 3.37 \(\pm\) 0.23 \\  & 4 & SS4 & 0.78 \(\pm\) 0.11 & 1.14 \(\pm\) 0.14 & 2.29 \(\pm\) 0.16 & 3.26 \(\pm\) 0.20 & 3.28 \(\pm\) 0.24 \\ \hline \multirow{3}{*}{Clay particles} & 2 & SN2 & 1.39 \(\pm\) 0.18 & 2.07 \(\pm\) 0.21 & 2.83 \(\pm\) 0.22 & 3.09 \(\pm\) 0.26 & 3.08 \(\pm\) 0.25 \\  & 3 & SN3 & 1.74 \(\pm\) 0.16 & 2.26 \(\pm\) 0.19 & 2.94 \(\pm\) 0.20 & 3.19 \(\pm\) 0.18 & 3.17 \(\pm\) 0.19 \\  & 4 & SN4 & 1.79 \(\pm\) 0.08 & 2.45 \(\pm\) 0.11 & 3.03 \(\pm\) 0.21 & 3.32 \(\pm\) 0.23 & 3.30 \(\pm\) 0.20 \\ \hline \multirow{3}{*}{Lime} & 2 & SH2 & 0.80 \(\pm\) 0.12 & 1.11 \(\pm\) 0.08 & 1.33 \(\pm\) 0.11 & 1.50 \(\pm\) 0.12 & 1.53 \(\pm\) 0.14 \\  & 3 & SH3 & 0.61 \(\pm\) 0.13 & 0.75 \(\pm\) 0.09 & 0.87 \(\pm\) 0.11 & 1.07 \(\pm\) 0.10 & 1.12 \(\pm\) 0.08 \\  & 4 & SH4 & 0.44 \(\pm\) 0.09 & 0.59 \(\pm\) 0.05 & 0.66 \(\pm\) 0.08 & 0.74 \(\pm\) 0.11 & 0.79 \(\pm\) 0.12 \\ \hline \multirow{3}{*}{Fly ash} & 2 & SF2 & 1.28 \(\pm\) 0.12 & 2.00 \(\pm\) 0.18 & 2.91 \(\pm\) 0.19 & 3.44 \(\pm\) 0.24 & 3.64 \(\pm\) 0.25 \\  & 3 & SF3 & 1.52 \(\pm\) 0.13 & 2.35 \(\pm\) 0.22 & 2.90 \(\pm\) 0.19 & 3.70 \(\pm\) 0.25 & 3.81 \(\pm\) 0.28 \\ \cline{1-1}  & 4 & SF4 & 1.55 \(\pm\) 0.13 & 2.28 \(\pm\) 0.18 & 2.97 \(\pm\) 0.21 & 3.73 \(\pm\) 0.23 & 3.96 \(\pm\) 0.25 \\ \hline \end{tabular}
\end{table} 
Table 4: Test results for unconfined compression strength (MPa). Figure 5: Curve of unconfined compressive strength with optimal content of the additives. Figure 6: Curve of indirect tensile strength with different replacement ratios of the additives.
# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 2 Literature Review
### Research on Itinerary Planning Algorithms  
In terms of itinerary planning, [PERSON] (2018) proposed a tourism route planning method that comprehensively considers distance, the number of transfers between public transportation systems, and average road conditions based on a dynamic programming algorithm. [PERSON] (2018) proposed a tourism route planning algorithm based on a reinforcement learning algorithm to avoid areas with frequent traffic accidents and traffic congestion. [PERSON] (2018) constrained tourist travel time by introducing time window coefficients into the ant colony algorithm heuristic function. [PERSON] (2018) comprehensively analyzed factors such as travel distance, costs, time, operating hours, and fees of tourist attractions to propose a city suburban self-driving tour route planning method focused on enhancing the overall tourist experience. [PERSON] (2018) proposed a tourism experience utility objective function comprising two parts: tourism activity utility and tourism travel utility, considering various optional transportation modes and solving it based on the ant colony algorithm. Additionally, some works focus on recommending the top-K or next POI (point of interest), mainly based on collaborative filtering (CF) or matrix factorization methods, recommending the top-ranked or best tourist attractions to tourists. [PERSON] (2018) provided an overview of this aspect of work.  
The above studies have considered multiple factors for tourist itinerary planning and recommendation, implementing various algorithms. Overall, existing methods contribute to enhancing tourists' travel experiences. However, tourists can further benefit from a more sophisticated model and algorithms with higher levels of personalization. This paper proposes a tourist itinerary recommendation algorithm that considers a wide range of factors and supports various personalized needs.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 4 Analysis of Simulation Result  
In this part, Python 3.6 is used to simulate the proposed scheme and evaluate its performance. The medical image set was chosen for experimental testing to provide a better estimation of the algorithm's performance. As shown in Figure 7, eight medical images of different sizes, different body parts, and different types were selected for simulations to verify the effectiveness of the scheme.  
Figure 6: The process of data reorganization and region recovery.  
Figure 7: The standard medical images used for testing. (**a**) Brain 01 MRI (512 \(\times\) 512); (**b**) Brain 02 MRI (512 \(\times\) 512); (**c**) Pulmonary 01 CT (256 \(\times\) 256); (**d**) Pulmonary 02 DICOM (256 \(\times\) 256); (**e**) Pancreas MRI (180 \(\times\) 180); (**f**) Chest CT (256 \(\times\) 256); (**g**) Knee X-ray (300 \(\times\) 162); (**h**) Hand X-ray (64 \(\times\) 64); (**i–p**) The ROS region of extracted. (**i**) Brain 01 (352 \(\times\) 352); (**j**) Brain 02 (464 \(\times\) 464); (**k**) Pulmonary 01 (176 \(\times\) 176); (**l**) Pulmonary 02 (176 \(\times\) 176); (**m**) Pancreas (120 \(\times\) 120); (**n**) Chest (228 \(\times\) 188); (**o**) Knee (212 \(\times\) 162); (**p**) Hand X-ray (46 \(\times\) 60).
# Enhanced Spatial Stream of Two-Stream Network Using Optical Flow for Human Action Recognition
## 5 Experimental Results
### Spatial Stream
#### 5.3.1 Fusion  
In this section, we assess the overall two-stream model. Multiple strategies have been adopted in recent years by researchers to combine the temporal and enhanced spatial streams. One possible approach is to make a stack of joint layers on top of classification layers and then train it, but this leads to overfitting, so we fused the two streams by averaging their softmax scores. The results in Table 6 show the significance of combining both streams, as the overall accuracy is 9.43% greater than the temporal stream results and 12.93% greater than the spatial stream.
# Usage of V2X Applications in Road Tunnels
## 3 Methodology
### Test Conditions  
Within the Blanka tunnel test, hybrid OBUs (on-board units) were used. These units support local communication over the ITS-G5 (European standard for vehicular communication operating at 5.9 GHz) and cellular communication over the LTE (Long-Term Evolution) networks. Each of them was installed in the vehicles that were part of the experiment. In Figure 3, photos of the installed equipment are presented. Both units were set with properties that allowed the use of the Dead Reckoning method based on data from the 3D accelerometer and the Map Matching when the V2X use case [36] was generated. This has become a basic prerequisite for the test scenario of whether V2X communication occurs without GNSS signal reception.  
The V2X communication assessment in the tunnel must be carried out under specific safety precautions. Due to this fact, it was feasible to perform the test only at the time of tunnel closure, which, on the other hand, made it possible, for example, to test scenarios such as a stationary vehicle in the tunnel and beyond.
## 5 Case Study for Mitigating Security Attacks in Smart Healthcare System
### Data Layer  
This section discusses various wearables devices (sensors) associated with an individual or critical patient. The sensors collect the data, such as blood pressure, oxygen rate, heartbeat, temperature, from the patient's body and transmit the data to various the healthcare providers such as pharmacies, hospitals, ambulances, and government organizations via the traditional Internet. However, the precarious link between the sensor node and its recipient is impeded by different security attacks, such as malware, communication, and software-based attacks, which disrupt the performance of different healthcare services (e.g., medicine, nursing, telesurgery). Therefore, there is a need for a robust system that detects and alleviates the security threats from the smart healthcare system. Here, the data layer collects the malware samples (malicious and non-malicious binaries) and forwards them to the malware analysis layer.
## 3 Methodology
### Logistic Regression  
Logistic regression is a probability model proposed by [PERSON] [40], which is used for classification and prediction by expressing the relationship between dependent variables and independent variables as a regression equation. It was mainly proposed to classify events  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Elevation** & **Slope** & **Profile Curvature** & **Plan Curvature** & **TWI** & **SPI** \\ \hline
1.552 & 2.679 & 1.099 & 1.114 & 1.477 & 1.259 \\ \hline \end{tabular}
\end{table}
Table 2: Evaluation of vulnerability according to area under the curve (AUC).  
Figure 5: Rainfall interpolation result (2001).  
in which the dependent variable follows a binomial distribution, such as the relationship between test scores and whether they pass the exam, or patient health status and whether they have a disease.  
Odds ratio (\(OR\)) was introduced to utilize logistic regression for binary classification. OR represents the ratio of the probability, \(p\), that an event will occur, and the probability, \(1-p\), that it will not occur, and it is calculated as follows.  
\[OR=\frac{p}{1-p} \tag{5}\]  
In addition, the problem of binary classification is that a linear regression analysis cannot be performed, because the dependent variable is represented as \"0\" or \"1\", and thus the range is different from the independent variable having a continuous distribution. Accordingly, the dependent variable is adjusted to (\(-\infty\), \(\infty\)) in the range of [0, 1] through the logit transformation that applies the logarithm to OR. This can be expressed using the following equation.  
\[\text{Logit}(OR)=\log\biggl{(}\frac{p}{1-p}\biggr{)}=Y=\beta_{0}+\beta_{1}x_ {1}+\cdots+\beta_{n}x_{n} \tag{6}\]  
In this study, for the calculation of the regression coefficient (\(\beta_{n}\)) of Equation (6), the occurrence of flooding events (\(Y\)) for all grids in the study area and 10 hydrological-topographic factors (\(x_{1}\sim x_{10}\)) were used. In addition, the maximum likelihood estimator is used to determine regression coefficients including the constant term [41].  
Next, a logistic function is used to calculate the flooding probability for target grids using the calculated regression coefficients. The logistic function can be calculated as follows by using the inverse function relation in Equation (6).  
\[e^{\beta_{0}+\beta_{1}x_{1}+\cdots+\beta_{n}x_{n}}=\frac{p}{1-p} \tag{7}\]  
\[(1-p)e^{\beta_{0}+\beta_{1}x_{1}+\cdots+\beta_{n}x_{n}}=p \tag{8}\]  
\[e^{\beta_{0}+\beta_{1}x_{1}+\cdots+\beta_{n}x_{n}}=p\biggl{(}1+e^{\beta_{0}+ \beta_{1}x_{1}+\cdots+\beta_{n}x_{n}}\biggr{)} \tag{9}\]  
\[p=\frac{1}{1+e^{-(\beta_{0}+\beta_{1}x_{1}+\cdots+\beta_{n}x_{n})}} \tag{10}\]  
The probability of flooding \(p\) can be obtained by inputting the hydrological-topographic factor for a target grid to Equation (10). This flooding probability \((p)\) corresponds to flood vulnerability in this study. The flood vulnerability estimated through the logistic regression has the range [0, 1], and the closer to 1, the higher the probability of flood occurrence.
## 4 Network and Strategy for Scheduling
### State Parameters  
Due to the complexity of the problem, and to satisfy the constraints, multiple state parameters need to be maintained to assist the network in making decisions. These parameters can be categorized into two types: workflow state and environmental state. The key parameters are listed below:  
1. Resource usage;
2. Scheduling status of sub-tasks: indicates whether the sub-task has been scheduled;
3. Workflow group: used to keep all Workflows;
4. Running Queue: save the running sub-tasks;
5. Waiting Queue: store the sub-tasks that satisfy the constraints.
6. Timestamp.  
Different states will be used in different scheduling scenarios.