esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Case Study
#### 3.4.4 Designed Applications (DAGs)  
The objective of these applications is to collect and process the data obtained from the IoT layer. Specifically, we selected the Overall Equipment Effectiveness (herein OEE) as the Key Performance Indicator (KPI) to measure the productivity of the assets. The OEE is recognized as one of the most insightful KPIs to assess the overall productivity of a given manufacturing asset [43]. Two applications are designed, developed and deployed per asset:  
1. Acquisition: This application obtains the data produced by the asset and stores it on a NoSQL database. Its logic is divided into two components: one that subscribes to the MQTT broker and obtains the manufacturing data (C_1), and the other that stores that data inside the eXist database (C_2). Note that the MQTT broker represents the Fog-IoT Message Hub.
2. Processing: It is a data processing application that is composed of three components as follows: the first component (C_1) reads the previously stored data from the eXist database and sends it to the second component; then, the second component (Process)is a permanent component that processes the data and extracts the OEE from it; finally, the OEE is sent to the third component (C_3) that stores it in a InfluxDB NoSQL database.  
To illustrate the design of these applications, Figure 8 represents the two DAGs for the Asset _Assembly Robot_1_ in the application structure designed. To help understand the relations between Application Components and Support Infrastructure Components, a bottom layer has been added to show Support Infrastructure Components and their use by the Application Services.  
These applications can be also represented as DAGs, as shown in Figure 9. In this view, the usage of Support Infrastructure Components is inherent to the programming of the individual application components and, thus, is not represented. Figure 9 focuses on the communication between components, which is based on the Intra-Fog Message Hub implemented using Kafka. Therefore, the links between components represent the Kafka topics where data are transmitted.  
The complete case study, with the four applications designed for the two Assets, is represented in the application structure in Figure 10. The addition of the second Asset, _Assembly Robot_2_, is an exemplification of horizontal scaling applied to an already-existing structure. As the _Process_ permanent component offers a service used in both applications, it is represented as a component of both applications.  
Figure 8: Applications are designed per asset that forms the Assembly Lines. Permanent component C_2 in the Processing application is highlighted in green.  
Figure 9: Assembly Robot_1 applications as DAGs.
# A Method of Enhancing Silk Digital Printing Color Prediction through Pix2 Pix GAN-Based Approaches
## 3 Results
### Pix2 Pix GAN Based Color Prediction Model Results
The representative predicted results in two types of silk fabrics are shown in Figure 8. As shown in the figure, the predicted images (Generated Print Fabric) are visually similar to the corresponding real samples (Ground Truth) in color and style, with most of the measured \(\Delta\)E\({}_{00}\) values below 0.7. The colors of the fabrics are visually similar to the color card; the texture details are clear and realistic. Experimental results show that the proposed Pix2 Pix GAN-based method for predicting colors in digital silk printing is effective and feasible. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Average** & **Maximum** & **Standard** & **Average** & **Average** & **Average** \\  & \(\Delta\)**E\({}_{00}\)** & \(\Delta\)**E\({}_{00}\)** & **Deviation** \(\Delta\)**E\({}_{00}\)** & **\%\(\Delta\)**L\({}_{00}\)** & **\%\(\Delta\)**C\({}_{00}\)** & **\%\(\Delta\)**H\({}_{00}\)** \\ \hline Predicted image vs. silk crepe satin & 2.37 & 11.84 & 1.50 & 19.69\% & 43.35\% & 36.96\% \\ \hline Predicted image vs. silk twill & 1.85 & 7.37 & 1.04 & 18.74\% & 42.30\% & 38.96\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CIEDE2000 color differences and their components in percentages (%\(\Delta\)L\({}_{00}\), %\(\Delta\)C\({}_{00}\), %\(\Delta\)H\({}_{00}\)) between 300 pairs of predicted images and actual samples of silk crepe satin and 300 pairs of predicted images and actual samples of silk twill. Figure 7: Individual color difference distribution of silk crepe satin and silk twill predictions. The upper plots show individual dots as color differences between predicted image vs. actual printed sample. The lower plots show the \(\Delta\)E\({}_{00}\) value distribution binned by 0.7 CIEDE2000 unit, showing the percentage of predictions that fall into each bin.
# Mountain Muon Tomography Using a Liquid Scintillator Detector
## 2 Materials and Methods
### Principle and Analysis Process of the Muon Tomography  
We obtain the ratio radar image by dividing the measured and referenced event number radar images. The ratio radar image shows the locations of the density variation regions inside the mountain. The above radar images are two-dimensional (2D); the radial and angular dimensions are \(\cos\theta_{\rm zen}\) and \(\phi_{\rm azi}\), respectively. The \(\theta_{\rm zen}\) and \(\phi_{\rm azi}\) are, correspondingly, the zenith angle and the azimuth angle of the muon direction in a spherical coordinate system with the detector as the center and a vertically upward direction as the positive Z-axis. The event number radar image or the ratio radar image indicates that the bin content is the event number or the ratio of the event numbers.  
Figure 4 shows the analysis flow chart. The radar images can be obtained in the following way: We place the detector in a position and then take cosmic-ray muon data for a given time to obtain the measured event number radar image. Based on the satellite image of the mountain terrain, the reference density obtained by measuring the mountain rock, the \(R_{0}\), the data taking time, Formula (2), and the direction reconstruction method, we can obtain the referenced event number radar image via simulations. After normalization by the data taking time, dividing the measured image by the reference one, yields the ratio radar image, which displays the 2D locations of the density variation regions. In those locations, we require the quotient to deviate from one with a statistical significance of at least three standard deviations. The detector can be placed at two or more measurement positions to obtain a 3D image of the density variation regions by combining the 2D images if one is interested.  
In Section 3, we will present the imaging of Jinping Mountain based on the JNE's published results, as mentioned in Section 2.2.1, and the simulated data. For the imaging with the JNE's published results, the total number of events is 343, too few to give a statistically significant ratio radar image, so we give an event number radar image. For the imaging with simulated data, we will give the ratio radar images of two scenarios in which the detector is placed inside and outside the mountain.
# A Traffic Equilibrium Model for Multi-Modal Networks with Uncertain Demands
## 3 Generalized Travel Cost Function
### Physical Links
#### 3.1.2 Transport Mode 2 (Bus)  
The routes of buses are fixed, but traffic volumes depend on the network congestion. In the physical network, transport mode 2 shares the congestion effect with mode 4 (car).  
Therefore, the travel time \(t_{l}^{2}\) considering congestion in road traffic can be formulated as follows:  
\[t_{l}^{2}=t_{l}^{2^{0}}\left[1+\alpha\left(\frac{fv_{l}^{2}+gv_{l}^{4}}{C_{l}} \right)^{\beta}\right] \tag{7}\]  
where \(t_{l}^{2^{0}}\) (min) is the free-flow travel time of mode 2 on link \(l\); \(v_{l}^{2}\) and \(v_{l}^{4}\), respectively, are the passenger flow of modes 2 and 4 on link \(l\); \(C_{l}(pcu\cdot h^{-1}))\) is the road capacity of the physical link for mode 2 and mode 4; \(a\), \(\beta\) are modal parameters; \(f\), \(g\) are, respectively, the passenger equivalents for modes 2 and 4, and passenger equivalents for transport mode 2 (bus) \(f\). Supposing that the average number of bus passengers is 20 and the average number of passengers in a car is 2, we can obtain values of 0.5 and 0.05.  
There are assumed to be three bus lines on the road network with corresponding vehicle types and frequencies. Each vehicle type operates with a specific number of seats and capacity, which incur a specific crowd discomfort. Thus, the crowd discomfort for mode 2 is formulated as a function of the number of seats, per-vehicle capacity, and travel time.  
\[u_{l}^{2}=\gamma u^{2^{0}}\left[1+\alpha^{\prime}\left(\frac{v_{l}^{2}-S_{j}}{ A_{j}}\right)^{\beta^{\prime}}\right]t_{l}^{2} \tag{8}\]  
where \(u^{2^{0}}(min^{-1})\) is the crowd's discomfort without passengers per time unit; \(S_{j}\) (passengers) is the number of seats in bus \(j\); \(A_{j}(passengers)\) is the vehicle capacity of bus \(j\); \(\alpha^{\prime}\), \(\beta^{\prime}\) are model parameters, of a dimensionless unit, with usual values of 0.02 and 1.8.  
The travel fare of mode 2 on transfer links is assumed to be fixed. Therefore, the travel cost function of link \(l\) can be expressed as follows:  
\[c_{l}^{2}=\left\{\lambda_{t}+\lambda_{u}u^{2^{0}}\left[1+\alpha\left(\frac{v_ {l}^{2}-S_{j}}{A_{j}}\right)^{\beta}\right]\right\}t_{l}^{2^{0}}\left(1+\alpha \left(\frac{fv_{l}^{2}+gv_{l}^{4}}{C_{l}}\right)^{\beta}\right) \tag{9}\]
## 2 Materials and Methods
### Two Outliers Generation Methods
#### 2.3.1 Search Module Method  
The search module has a search criterion and a search scope. The search criterion is a value randomly extracted from a uniform distribution in the range of 40 kg or more and 120 kg or less. The values 40 kg and 120 kg are the minimum and maximum values of the original data set, respectively. The minimum and maximum values could be changed depending on the data set used. The search range was created based on the extracted search criterion value. After that, a random number was extracted from the uniform distribution on (0, 2), and we defined the upper limit to be the search criterion value + random number, and the lower limit as the search criterion value--the random number. Next, the searched values were extracted from the entire data set, and an outlier was generated by randomly extracting one of them. We proceeded with each of the search criteria and the search range because if you simply search with the search criteria, there is a high probability that the entire dataset does not have the same value as the search criteria. By adding a search range, it is possible to search in a slightly wider range while being similar to the search criterion value, thereby increasing the search probability. If it is not in the corresponding range, it is not in the original dataset, so the search is not performed. This does mean that the number of outliers generated may be insufficient; the remainder were added from the second module, the generation module. Figure 2 shows the overall flow for the search module.  
1. Set the search range (GRN \(-\) RN, GRN + RN) based on the random number (GRN) generated with a uniform distribution of (40 kg, 120 kg). A. RN is set as a real value in the range (0, 2).
2. Search and extract all values in the search range from all RAW data sets within the search range. A. If there is no searched data, the generation module creates the remaining required outliers.
3. Create a data set by finding the BMI, FAT, WATER, and MUSCLE values corresponding to the searched body weight values.
4. Create a set of outliers by adding noise corresponding to each variable of the data set created in 3. The noise of each variable is summarized in Table 1.
5. The number of preset outliers is converted into the number of weekly generations.
6. Obtain an index for the day of the week according to the number of generations per week and replace the corresponding normal data with an outlier table.
7. Repeat steps 1-6 for each week to generate outliers by substituting them.  
Figure 2: The Search module: An outlier generation module based on data set exploration, this is a flowchart of the search module.
## 3 Methods  
### Spatial Aggregation  
Data derived from MODIS, Sentinel-3, and ERA-5 products were spatially aggregated for arable land (using a crop mask) within administrative units (NUTS-2 and LAU) (Table 1). The aggregation followed the approach proposed by [PERSON] et al. (2001) [31] where the products were initially masked whenever a fraction of arable land within a pixel is less than 30%. The remaining pixels were averaged within administrative units using the fraction of arable land as weights. Additional adjustment of weights at the borders between administrative units was performed to reduce the importance of pixels covered by more than one unit. The aggregation resulted in a database consisting of predictors at NUTS-2 and LAU administrative levels averaged from MODIS, Sentinel-3, and ERA-5 products at a native temporal resolution (see Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Source** & \
\begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} &
\begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} \\ \hline \multicolumn{4}{c}{Satellite indices} \\ \hline NDVI (-) & MODIS & 8 day & 250 m \\ NDVI (-) & Sentinel-3 & 1 day & 300 m \\ LST (K) & MODIS & 8 day & 1000 m \\ LST (K) & Sentinel-3 & 1 day & 1000 m \\ \hline \multicolumn{4}{c}{Agro-meteorological parameters} \\ \hline Air temperature (K) & ERA-5 & 1 h & 0.25 deg \\ Precipitation (m) & ERA-5 & 1 h & 0.25 deg \\ Surface radiation (J m\({}^{-2}\)) & ERA-5 & 1 h & 0.25 deg \\ Soil moisture 0–7 cm (m\({}^{3}\) m\({}^{-3}\)) & ERA-5 & 1 h & 0.25 deg \\ Soil moisture 7–28 cm (m\({}^{3}\) m\({}^{-3}\)) & ERA-5 & 1 h & 0.25 deg \\ \hline \multicolumn{4}{c}{Crop mask} \\ \hline Fraction of arable land & \multicolumn{1}{c}{CLC * 2018} & static & Polygons ** \\ \hline \multicolumn{4}{c}{Administrative units} \\ \hline NUTS-2/LAU & \multicolumn{1}{c}{GUGk ***} & static & Polygons \\ \hline \hline \end{tabular}  
* Corine Land Cover; ** With minimum area of 25 h; *** Head Office of Geodesy and Cartography.  
\end{table}
Table 1: Satellite, agro-meteorological, and ancillary data used to derive crop yield forecast predictors.  
Figure 5: Absolute annual crop yields for NUTS-2 units reported by Statistics Poland with a fitted Theil-Sen monotonic trend. Please mind different limits of Y-axes.
# Modeling the Energy Performance of LoRaWAN
## 4 Modeling LoRaWAN End-Device Current Consumption
### Unacknowledged Transmission
In order to capture impact of collisions on \(E[l_{delivery\_unACK}]\), Equation (14) can be extended as follows:  
\[E\Big{[}l_{delivery\_unACK}\Big{]}=\ l_{pay}\cdot(1-b)^{l_{Data}}\cdot(1-p_{ coll}) \tag{15}\]  
Note that, as already introduced, \(b\) corresponds to the residual BER after application of physical layer error correcting techniques, equivalent to the residual BER that corresponds to message loss rate due to non-ideal link quality. In this paper, we assume that CR = 4/5, since it is the default CR in LoRaWAN, except for the 20-bit physical header, where CR = 4/8 is used. For CR = 4/5, a parity bit is added to each group of 4 bits from the physical layer message to be transmitted. Assuming that an error-correcting code is used for CR = 4/8 (e.g., a Hamming code [41]) and the range of BER values for reasonably useful links, and given the short size of the physical header, we approximate the relationship between \(b\) and the physical layer BER, denoted \(b_{phy}\), as shown in the next two equations. Let \(p_{loss}\) be the probability that a transmitted message is affected by at least one bit error, and therefore the message is lost, and let \(l_{phy\_handler}\) be the 20-bit header size. Therefore:  
\[p_{loss}=\ 1-(1-b)^{l_{Data}}=1-\Big{(}1-b_{phy}\Big{)}^{\frac{8}{4}\cdot(l_{ Data}-l_{phy\_handler})} \tag{16}\]  
\[(1-b)^{l_{Data}}=\Big{(}1-b_{phy}\Big{)}^{\frac{8}{4}\cdot(l_{Data}-l_{phy\_header })} \tag{17}\]
# Efficient Processing of \(k\)-Hop Reachability Queries on Directed Graphs
## 4 Experiment
### Query Processing  
In this subsection, _VCRea_ denotes the baseline algorithm, i.e., Algorithm 2, and _VCRea_+ denotes the degree-constraint algorithm with \(d=1\).  
**Query Time:** First, we showed the impacts of the number of vertices on query performance in sparse graphs. The results are shown in Table 5 from which we know that all algorithms consumed more query time when the graph size increased and that, for both \(k=2\) and \(k=10\), our approaches achieved the best performance. The reason lay in that, compared with \(ESTI\) and _kReach_, our approaches could answer all queries without graph traversal. Note that \(ESTI\) could not work successfully for web-uk when \(k=10\) due to performance of costly graph traversal.  
Second, we showed the impacts of the graph density on query performance. Figure 4 shows the results from which we made the following observations: (1) Graph density dominated the query performance of both \(ESTI\) and _kReach_, rather than the number of vertices in the graph, and both \(ESTI\) and _kReach_ consumed much more query time when the graph became dense. For example, \(ESTI\) could not successfully obtain the results for all datasets within 24 h when k = 10, where EX6 contained only 6545 vertices. (2) Our _VCRea_+ algorithm worked much better than both \(ESTI\) and _kReach_. For example, _VCRea_+ was faster than _kReach_ (\(ESTI\)) by more than three orders of magnitude for BerkStan when k = 8.  
**Index Size and Index Construction Time:** Figure 5a shows the comparison of index size from which we know that all algorithms could work successfully for all datasets and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{3}{c}{\(k=2\)} & \multicolumn{3}{c}{\(k=10\)} \\
**ESTI** & _kReach_ & _VCRea_ & _VCRea_+ & _ESTI_ & _kReach_ & _VCRea_ & _VCRea_+ \\ \hline Human & 41 & 101 & 20 & 21 & 41 & 112 & 17 & 17 \\ WikiTalk & 140 & 256 & 47 & 52 & 133 & 259 & 42 & 47 \\ Twitter & 354 & 373 & 85 & 103 & 275 & 375 & 68 & 83 \\ Web-uk & 512 & 883 & 210 & 159 & - & 1191 & 179 & 151 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of query time (ms), where \(d=1\) for the _VCRea_+ algorithm.  
that no one could beat others for all datasets. For sparse graphs, our approaches had much smaller index size; when the graph became dense, _kReach_ had the smallest index size.  
Figure 5b shows the comparison of index construction time for which we had results similar to those of index size, i.e., no one could beat others for all datasets.  
For sparse graphs, our approaches consumed much less index construction time; when the graph became dense, _ESTI_ and _kReach_ were more efficient than our approaches.
## 3 Congestion Prediction Model
### Traffic Prediction
#### 3.4.2 Path Traffic  
During WSN data transfer, the decision-making process for network use is as follows:  
1. Traffic Generation: prediction of the number of transfers in and out of the network per hour.
2. Traffic Distribution: prediction of the number of transfers between the source and destination.
3. Data Transmission Protocol: prediction of percentage by each transfer method.
4. Routing Protocol: prediction of path-specific transmission count according to transfer method.  
The above four elements could be predicted in sequential order when using Step 4 transportation demand analysis techniques. Using this technique, the estimate of the output results becomes the input for the estimation of the following steps. These steps are shown in Figure 4.  
In this paper, we predict traffic generation and traffic distribution; for the rest of the data transmission/routing protocol it was assumed that any step can be used and that network demand was predicted in steps 1 and 2.
## 3 Results
### Interobserver Reliability  
All interobserver reliability variables fulfilled the assumption of normality. Interobserver reliability at one laboratory (GL2), showed excellent reliability for 71% of the parameters observed (22/31). Based on the lower limit of the 95% CI, 58% (18/31) displayed an ICC value of at least 0.75. Nineteen percent (6/31) of the parameters showed fair-to-good reliability, whereas 10% (3/31) were poor (Table 1). The highest ICC value was observed for knee extension in the stance phase (ICC = 0.97, excellent reliability). In contrast, dorsiflexion during the stance phase showed poor reliability (ICC = 0.22) (Table 1). After averaging all parameters for interobserver reliability, an ICCtotal = 0.79 (95% CI: 0.67-0.86) was observed (Table 2). For interobserver reliability, the largest standard error was observed for hip adduction (% gait cycle) during stance phase, as well as for ankle dorsiflexion (% gait cycle) during swing phase, with each presenting with a standard error of \(\sigma^{\text{observer}}\) = 4.4% (Table 3). Plantarflexion during the stance phase had the worst detected angle (\(\sigma^{\text{observer}}\) = 3.8 deg). The largest ratio of interobserver to intrasubject error was observed for knee abduction angle in the stance phase (\(\sigma^{\text{observer}}\) = 2.4 deg, r = 3.2; Table 3).
# Secured Big Data Analytics for Decision-Oriented Medical System Using Internet of Things
## 3 The Proposed IoMT-Enabled Model
#### Security Analysis of SBD-EC Model  
This section provides security analysis achieved by the SBD-EC model against network threats.  
1. Confidentiality All the information that has to be kept confidential among sink node, mobile edges, and the cloud server is encrypted using a lightweight encryption function. Also, the encrypted data is concatenated with the unique identifier to the source node \(Id\). The message \(m\), security key \(K\) along with \(S(Id)\) is passed through the encryption function. Since \(K\) is mathematically related to its private key, the intruder cannot recover the original message. Also, \(Id\) is digitally signed \(S\) by the data owner, which reflects its authentication on the receiving end.
2. Mutual Authentication In the registration phase of the SBD-EC model, sink node, mobile edges, and cloud servers generate security keys and exchange the public keys with each other using the Schmidt-Samoa cryptosystem. The public keys are used for data encryption and their related mathematical private key is used for data decryption. The private keys perform the dual role of authentication along with data confidentiality. The combination of a pair of public-private keys along with a unique identity \(Id\) i.e., \((N,d)+\)\(Id\) denotes the authentication packets for communicating node.
3. Integrity The collected data is divided into n sized blocks with the combination of the padding method. Each block has a unique hash \(h_{i}\) and it is interconnected with hashing value of the previous block \(h_{i-1}\) shown as \(h_{i}=h_{i-1}\) xor \(m\). If an intruder changes any data block, then the receiver recomputes and compares with the received hash value. In case of mismatch, it assumes the incoming data is false. Moreover, the  
Figure 2: Working flow of the proposed model.  
arrangement of hashing values in the form of a sequence makes it not possible for intruders to damage the integrity of data.