esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Computational Complexity  
We evaluate the computational complexity in terms of the time consumed to yield the vehicle detected output image. While evaluating the computational complexity of the methods listed in Figure 10, we manually vary the test image size from 512 \(\times\) 512 pixels up to 1600 \(\times\) 1236 pixels on all the three datasets compared in this study. In addition, all the times shown in Figure 10 are the mean execution time on all the three datasets to process a single image and yield the output image.  
Moreover, in Figure 10, we compare the execution time of 14 state-of-the-art vehicle detection methods with the proposed method. It can be seen that the work of [PERSON] [34] is computationally more expensive than all of the compared methods. Clearly, the proposed method is computationally most economical and consumes slightly more than 0.50 s to yield a vehicle detected output image. Furthermore, the works reported by [PERSON] et al. [31], [PERSON] et al. [39], and [PERSON] et al. [40] consume nearly 1 s to yield the output image with detected vehicles.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Backbone** & **Image/s** & **Fog** & **Rain** & **Snow** & **Sand** \\ \hline
[28] & CSPDarkNet53 & 0.085 & 26.40 & 31.55 & 39.95 & 24.10 \\ \hline
[29] & CNN & 0.085 & 24.00 & 21.10 & 38.32 & 23.80 \\ \hline
[30] & R-CNN & 0.085 & 27.20 & 21.30 & 28.30 & 18.00 \\ \hline
[31] & BottleneckCSP & 0.085 & 29.31 & 41.21 & 43.00 & 24.02 \\ \hline
[33] & VGGNet-16 & 0.085 & 23.40 & 24.60 & 37.90 & 15.83 \\ \hline
[34] & ResNet-101-FPN & 0.085 & 28.95 & 41.10 & 43.00 & 24.09 \\ \hline
[35] & VGGNet-16 & 0.085 & 23.10 & 27.65 & 34.00 & 24.10 \\ \hline
[36] & ResNet-101 & 0.085 & 29.70 & 40.10 & 43.00 & 23.99 \\ \hline
[37] & ResNet-101 & 0.085 & 28.10 & 40.40 & 43.02 & 24.10 \\ \hline
[38] & ResNet-101-FPN & 0.085 & 16.50 & 14.08 & 15.38 & 10.69 \\ \hline
[39] & Hourglass-104 & 0.085 & 25.08 & 19.14 & 23.18 & 17.38 \\ \hline
[40] & ResNeXt-101 & 0.085 & 29.68 & 30.32 & 33.93 & 24.00 \\ \hline
[41] & ResNet-101-FPN & 0.085 & 28.83 & 27.68 & 30.19 & 24.03 \\ \hline
[42] & VGGNet-16 & 0.085 & 26.45 & 20.09 & 27.92 & 11.31 \\ \hline
**Proposed** & **CSP** & **0.0085** & **29.66** & **41.21** & **43.01** & **24.13** \\ \hline \hline \end{tabular}
\end{table}
Table 7: The mAP (%) comparison on DAWN dataset.
###### Abstract  
Concrete linings containing cold joint defects may crack or detach under the aerodynamic fatigue loading generated by high-speed train operation, which posing a serious threat to the normal operation of high-speed trains. However, there is currently no simulation method specifically for fatigue damage of concrete linings containing cold joints. Based on the Roe-Siegmund cycle cohesive force model, a cohesive force fatigue damage elements were developed. A large dataset was constructed through numerical simulation software to build a BP neural network for back-calculated parameter of cohesive force fatigue damage elements. By combining experimental data, fatigue damage parameters corresponding to different pouring interval cold joints were back-calculated. These back-calculated parameters were then incorporated into the numerical model to compare simulation results with experimental results to validate the applicability of cohesive force fatigue damage elements and back propagation neural networks (BP neural network). The research results show that the difference between the fatigue life and fracture process calculated by numerical simulation and experimental data is small, verifying the applicability of the method proposed in this paper. The pouring interval directly affects the initial strength of the cold joint interface and the starting conditions of fatigue damage. The possibility of fatigue damage and fracture of concrete components containing cold joints increases with the increase of pouring interval, while the variability of fatigue life decreases with the increase of pouring interval. Interface strength and thickness are the main factors affecting the possibility of fatigue damage occurrence and the variability of fatigue life. The research results can be used to analyze the damage and cracking status of concrete linings containing cold joints under aerodynamic fatigue loading.  
cold joint; fatigue damage; cohesive force element; BP neural network; simulation method +
Footnote †: journal: Applied Sciences
# A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction
## 2 Methods  
Our proposed method is based on knowledge graphs in which each drug node is connected to at least one protein. The method projects all single and multidrug combinations onto the same embedding space (Figure 1) and then utilizes the obtained embedding vectors for polypharmacy side effects prediction. This method consists of the following three steps. First, all nodes (of single drugs and proteins) in the graph are embedded into their corresponding n-dimensional vectors using node2 vec algorithm, producing initial embedding vectors for single drugs and proteins. Second, a multidrug combination is treated as a node in the graph, and the hypothetical nodes for multidrug combinations are added to the original node set. Then, the embedding vectors of all nodes included in the expanded node set are produced (or reproduced) using initial embedding vectors as inputs to a unified embedding module. Consequently, single drugs, proteins, and multidrug combinations are represented by n-dimensional embedding vectors that exist in the same latent space. Finally, the prediction module is trained with labeled data of single drugs and multidrug combinations. During this process, all parameters of the embedding and prediction modules are optimized to minimize the predefined loss function while retaining the properties of the node relationships shown in the knowledge graph as much as possible.
###### Abstract  
The European Food Safety Authority is re-evaluating styrene for assessing the safety of food contact materials (FCM) such as polystyrene (PS) and started a systematic review of the data on migration levels in food. A restriction for styrene is expected in the near future. The main food contact application of PS is dairy packaging, mainly at refrigerated storage. In this study, seventeen dairy products packed in PS taken from the Italian and German markets were investigated. Styrene concentrations in the refrigerated dairy products (yogurt, cream) ranged from 5 to 30 \(\mu\)g/kg at the best before date, while in single serving portions of coffee creamer, which were stored at room temperature until the best before date of approx. 190 days, 401 \(\mu\)g/kg were measured. Among several parameters, the ratio between the surface contact area of the package and the quantity of the food packed, the time/temperature conditions of production/filling and storage of the products were identified as the main factors influencing styrene migration into food under realistic conditions. Yogurt's fermented in the pots for approximately 8 h at 40-50 \({}^{\circ}\)C showed higher styrene levels than those fermented in an incubator and filled at 20 \({}^{\circ}\)C. The fat content might influence the styrene level but the effect, if any, was too small in relation to the variability of other parameters. Levels of styrene migrating into 50% ethanol food simulant under standardized condition (10 days/40 \({}^{\circ}\)C) were found to be much higher than levels in refrigerated foods. This raises the question as to whether compliance testing for PS plastics should be adapted taking into consideration the correlation between migration testing by laboratory simulations and migration into real food.  
stype; migration; polystyrene; food contact materials; packaged foodstuffs; yogurt; cream; dairy products +
Footnote †: journal: _foods_  
Article
## 4 Experimental Results
### Analysis Results for False Detection Cases
#### 4.2.1 Annotation Scope Errors  
Incorrectly specifying the annotation range during data labeling causes the model to detect named entities incorrectly; however, it may appear to make false detections owing  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **Named Entity Tag** & **Precision** & **Recall** & **F1-Score** & **No.** & **Named Entity Tag** & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & PS NAME & 0.932 & 0.915 & 0.923 & 18 & QT MOBLE & 1.000 & 1.000 & 1.000 \\
2 & PS NICNAME & 0.792 & 0.840 & 0.815 & 19 & QT PHONE & 1.000 & 1.000 & 1.000 \\
3 & DT\_BIRTH & 0.946 & 1.000 & 0.972 & 20 & QT\_CARD NUMBER & 0.987 & 1.000 & 0.994 \\
4 & QT\_AGE & 0.974 & 0.962 & 0.968 & 21 & QT\_ACCOUNT NUMBER & 0.988 & 0.988 & 0.988 \\
5 & CV SEX & 0.968 & 0.984 & 0.976 & 22 & THI EMAIL & 1.000 & 1.000 & 1.000 \\
6 & QT\_LENGTH & 0.957 & 1.000 & 0.978 & 23 & QT\_PLATE NUMBER & 1.000 & 0.983 & 0.992 \\
7 & QT\_WEIGHT & 0.929 & 1.000 & 0.963 & 24 & CG\_WORPLACE & 0.895 & 0.925 & 0.910 \\
8 & TM\_BLODOD TYPE & 1.000 & 1.000 & 1.000 & 25 & OG\_DEPARTMENT & 0.908 & 0.989 & 0.943 \\
9 & OG\_RELIGION & 0.962 & 1.000 & 0.981 & 26 & CV\_POSITION & 0.868 & 0.881 & 0.874 \\
10 & LCP\_COUNT & 0.988 & 0.988 & 0.988 & 27 & OG\_EDUCATION & 0.930 & 0.964 & 0.946 \\
11 & OG\_CLUB & 0.861 & 0.920 & 0.890 & 28 & QT\_GRADE & 0.967 & 1.000 & 0.983 \\
12 & LC\_ADDRESS & 0.942 & 0.966 & 0.954 & 29 & PD\_MAIOR & 0.990 & 0.980 & 0.985 \\
13 & LC PLACE & 0.824 & 0.871 & 0.847 & 30 & PS ID & 0.974 & 1.000 & 0.987 \\
14 & QT\_RESISTENT\_NUMBER & 1.000 & 0.944 & 0.971 & 31 & TM\_SITE & 1.000 & 1.000 & 1.000 \\
15 & QT\_ALIES\_NUMBER & 0.947 & 1.000 & 0.973 & 32 & QT\_IP & 1.000 & 1.000 & 1.000 \\
16 & QT\_PASFORM\_NUMBER & 1.000 & 1.000 & 1.000 & 33 & CV\_MILITARY\_CAMP & 0.908 & 0.983 & 0.944 \\
17 & QT\_DRIVER\_NUMBER & 1.000 & 1.000 & 1.000 & 33 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 12: Experimental results for each KPF-BERT personal information item (F1-score: 0.943).  
to incorrect ground-truth labeling. Typically, names, nicknames, and job titles/positions including vocative particles such as '-ah' and '-ya' or suffixes such as '-nim' and '-ssi' are annotated incorrectly. Some examples are listed in Table 13.
# Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things
## 2 Requirements and Challenges
Critical applications (classes 1 and 2) also require redundancy, resistance to noise and robustness against failure as they must ensure timely and successful delivery. In addition, the failure of one or a few nodes must not compromise the operation of the network as whole. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Application** & **Range [m]** & **Cycle Time** \\ \hline Building automation & 10–200 & 100 ms—seconds \\ Monitoring and supervision & 100–1000 & seconds—days \\ Process control & 50–500 & 10–1000 ms \\ Factory automation & 10–50 & 0.5–100 ms \\ Automotive & 1–10 & 1–100 ms \\ Interlocking and control & 50–100 & 10–250 ms \\ Power-system protection & 100–10 k & 0.01 μs-50 ms \\ Event-based control & 10–100 & 1–100 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cycle time and communication range requirements broadly vary over industrial automation use-cases [12; 24; 25; 26]. Figure 3: Different classes of industrial systems have significantly different performance requirements. Many of the stated requirements are interconnected and there is no single technology that covers all of them simultaneously. The inevitable trade-offs, their causes and consequences are elaborated in the next section.
# Underwater Vehicle Path Planning Based on Bidirectional Path and Cached Random Tree Star Algorithm
## 4 Experiments
### Implementation and Testbeds  
During the experimental phase, a comparative study was conducted between the rudimentary RRT* algorithm and the improved approach proposed in this manuscript. To construct the simulated space with obstacles we refer to the work of [PERSON] et al. [38]. The evaluation was performed using Python 3.7 and Matplotlib on an Intel Core i5-6500 processor with a clock speed of 3.20 GHz, a memory capacity of 4 GB, and the Windows 7 operating system. For this purpose, the search step was set to a fixed value of 5 cm, while the loop was allowed to terminate after 4000 iterations. The testbed comprised a three-dimensional map with dimensions of \(100\times 100\times 100\), where the starting and ending points were (5,5,5) and (95,95,95), respectively. The display modules were responsible for generating both the random tree and path information. The red lines denoted the branches of the random tree, whereas the black lines represented the output path. To ensure consistency in results, the same map was deployed for algorithmic validation in this research work.
## 4 Processing and Modeling
This distinction is useful since, for the ship used here, the squat does only depend on the easy-to-observe speed when the water is deeper than 25 m. As a consequence, the quality of the calculated squat can be assumed to be better. In total, 323 segments were found, from which 127 were in deep water and 196 in shallow water regions. Shallow water segments were used only for validation in Section 5. The estimation of a SWH model as a function of \(\delta\) was carried out with data from deep water segments only. The SNR data were analyzed in the same way as for the first experiment, whereby the correction terms in Equation (3) were taken from the GNSS processing in the other project mentioned above. The usable segments had an average length of ca. 40 min twice a day. Due to this fact and, additionally, due to the necessary azimuth mask, the number of analyzed satellite arcs was only 633 (362 GPS/271 GLONASS). The required SWH values at the ship's position were spatially interpolated from the wave buoy data. Unlike the first experiment, no SWH correction had to be used. In Figure 5, reference SWH data for both the static experiment (Figure 5a), as well as the kinematic experiment (Figure 5b) are plotted against the damping coefficient derived from SNR analysis. In both cases, the SWH can be assumed to be a linear function of the damping coefficients \(\delta\). Hence, a first degree polynomial was fitted to both datasets. Both the buoy data and the estimated \(\delta\) values are stochastic variables and have to be treated as such in the adjustment. This was done using a robust total least squat adjustment. Initially, the stochastic model was derived from the estimated standard deviations of the damping coefficients and, due to the lack of accuracy information, an assumed uniform standard deviation of 0.05 m for the SWH values. After each solution convergence, the observation weights were adjusted due to the normalized residuals of the observations. This procedure was repeated until a termination criterion was reached. The resulting linear functions are plotted as red lines in Figure 5a,b. The shading of the data relates to the final weights from the robust adjustment. As expected, the geodetic antenna's damping was higher for the same SWH values. Hence, this antenna is less affected by multipath effects. The estimated parameters and the standard deviation of unit weight (\(s_{0}\)) are shown in Table 2. The \(s_{0}\) of the static experiment was slightly larger than that of the kinematic experiment. This might be a result of the SWH conditions near the coast and the therefore necessary correction term. Nevertheless, since the standard deviations lied in the range of the resolution of the reference in situ SWH observations, the method worked well in both cases.
# Modified Echo State Network Enabled Dynamic Duty Cycle for Optimal Opportunistic Routing in EH-WSNs
## 5 Results and Discussion
### Impact of Varying Average Energy Harvesting Rate
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{\begin{tabular}{c} **Throughput (packets/s) Comparison with Varying Average Energy** \\ **Harvesting Rates** \\ \end{tabular} } & \multicolumn{3}{c}{\begin{tabular}{c} **\% Improvement of OOR as Compared with Existing Schemes** \\ \end{tabular} } \\ \hline \begin{tabular}{c} **Energy Harvesting** \\ **Rate (mW)** \\ \end{tabular} & **AOR** & **Max-SNR** & **ESN** & **OOR** & \begin{tabular}{c} **\% Gain of OOR** \\ **Compared with ESN** \\ \end{tabular} & \
\begin{tabular}{c} **\% Gain of OOR** \\ **Compared to ESN** \\ **Max-SNR** \\ \end{tabular} &
\begin{tabular}{c} **\% Improvement of** \\ **with AOR** \\ \end{tabular} \\ \hline
20 & 5000 & 6000 & 8000 & 9500 & 15.79 & 36.84 & 47.37 \\
40 & 3220 & 4870 & 6520 & 8425 & 22.61 & 42.20 & 61.78 \\
60 & 2760 & 4000 & 5660 & 7620 & 25.72 & 47.51 & 63.78 \\
80 & 1990 & 3190 & 4000 & 6670 & 40.03 & 52.17 & 70.16 \\
100 & 1565 & 2000 & 3000 & 5725 & 47.60 & 65.07 & 72.66 \\ \hline \multicolumn{1}{c}{\begin{tabular}{c} **Average \% Improvement \(\rightarrow\)** \\ \end{tabular} } & \multicolumn{3}{c}{\begin{tabular}{c} **30.35** \\ \end{tabular} } & \multicolumn{3}{c}{\
\begin{tabular}{c} **48.76** \\ \end{tabular} } & \multicolumn{3}{c}{
\begin{tabular}{c} **63.15** \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Descriptive observations of OOR performance in terms of throughput (second scenario). Figure 14: Throughput comparison with varying average energy harvesting rates. In Figure 15, the five different columns in the first part of the figure show the outcomes of the throughput parameter in 100 scale with variation in average energy-harvesting rates for each scheme, respectively. For example, consider different columns of OOR scheme in first part of figure, the third column represents the value of throughput parameter as 76.2 (packets/s) at 60 mW average energy harvesting rate in 100 scale whereas the fifth column depicts the value 57.25 (packets/s) at 100 mW average energy harvesting rate in 100 scale. We use the 100 scale for better representation of columns in the figure. The values can be verified from above Table 6 In the second part of the figure, the five different columns show the percentage improvement, and the sixth column depicts the average percentage improvement.
# Space-Based Displacement Monitoring of Coastal Urban Areas: The Case of Limassol's Coastal Front
## 3 Results
### Final Land Subsidence Results of Limassol Coastal Front according to PSI and Leveling Techniques  
The final results of the study are presented in Table 8. Studying the distribution and the number of the PSs in every buffer zone can be characterized as crucial since it provides information about the sufficiency of the results. The average number of distribution was 182, ranging between 86 (Dream Tower) and 395 (ICON). Concerning the distribution of PSs in the buffer zone of 200 m around each building, this depended on the surrounding area and the construction phase in each case. A similar measurement unit in millimeters was indispensable for the compatibility correctness of the results. Ergo, multiplication by 4 (years of study: 2017-2021) of the value corresponds to PSI mean vertical deformation (mm) computed for the final land subsidence rates for PSI analysis from 2017 to 2021 (mm).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**PSI Analysis Results of Tall Buildings**} & \multicolumn{3}{c}{**Statistics (mm)**} \\ \cline{2-4}
**on Limassol Coastal Front** & **Mean/Average** & **Maximum** & **Minimum** \\ \hline \(-1.70\) & \(2.43\) & \(-4.17\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: PSI analysis results of tall buildings on Limassol coastal front.  
The validation of PSI results was accomplished by leveling measurements. The leveling procedure had a completed accuracy of 7 mm in an almost 7 km length line. For the application of the comparison and validation of two techniques, the nearest benchmark to the corresponding building was selected. This led to a new assumption; the rate of the vertical displacement of leveling benchmark was accordingly compared with the mean vertical displacement of the PSs in their buffer zone. Particularly, the benchmarks with ID LVD041, LVD030, and LVD042 could be compared to the ICON, Trilogy, ONE, and Olympic Residence buildings, respectively. In the buffer zone of the Oval building, the 43 benchmark was included, while the MARR Tower used the LVD033 benchmark. Moreover, the DTA Tower was compared with the nearest benchmark, the LVD045. Opposite the Limassol Del Mar construction the LVD047 benchmark was measured, while the LVD048 benchmark was used for iHome and the Dream Tower.  
As it is shown in Table 8, only three cases exceed 2 mm; the One, Trilogy, and Olympic Residence, with vertical land deformation values of \(-2.393\) mm, \(-5.008\) mm, and \(-4.906\) mm, respectively. As seen in the case study described in Table 2, those structures were built next to each other at a distance of one kilometer, with two of them being skyscrapers and one a tall building. Buildings such as the Dream Tower, Sky Tower, iHome, DTA Tower, and MARR Tower have an average value of vertical deformation below 0.5 mm, with the minimum value corresponding to MARR Tower.
# On Combining Feature Selection and Over-Sampling Techniques for Breast Cancer Prediction
## 3 Research Methodology
### Experimental Setup
#### 3.2.2 The Feature Selection and Over-Sampling Methods  
In this paper, the information gain (IG) as the filter method and the genetic algorithm (GA) as the wrapper method are used for feature selection. Particularly, these two methods have been used in many research problems, including text classification [20], gene expression microarray analysis [21], intrusion detection [22], financial distress prediction [23], software defect prediction [24], etc.  
IG evaluates the gain of each variable in the context of the target variable, which is based on calculating the reduction in entropy. That is, the feature ranking stage focuses on ranking the subsets of features by high information gain entropy in decreasing order. In GA, an initial set of candidate solutions (i.e., individuals) are created and their corresponding fitness values are calculated for the later cross-over and mutation steps. Specifically, the individuals are subsets of predictors, and the fitness values are measures of the model performance.  
Analyses were performed using the WEKA data mining software package. Most related parameters are based on its default values, except for the genetic algorithm, where the population size, crossover rate, and mutation rate were set as 50, 0.8, and 0.01, respectively [25].  
On the other hand, the over-sampling method is based on SMOTE. It has been widely used as a baseline over-sampling method for breast cancer datasets [14; 15; 16; 17]. The percentage of synthetic instances was set to make the two datasets become balanced datasets where the malignant and benign classes contain the same numbers of data samples. Other related parameters were based on the default values of WEKA.
# An Enhanced Distributed Data Aggregation Method in the Internet of Things
## 3 Proposed Method (LA-RPL)  
In accordance with the demonstration of the previously proposed approaches in Section 2, the proposed method of this paper comprises two objective functions for both network graph creation phase and aggregation-based data transmission and exchange phase. In our proposed method, learning automata-RPL (LA-RPL), according to the first objective function (named OF 1), each parent node is limited to \(k\) child nodes, where value \(k\) is determined depending on the network application type. Through the second objective function, namely OF 2, each network node is equipped with a learning automata. According to the status and congestion of the received packets, such that learning automata grants either data aggregation permission or instantly direct transmission permission to the parent node. Subsequent subsections are as follows. The formation of the network graph, based on the objective function OF 1, is illustrated in Section 3.1. The proposed learning automata in the form of objective function OF 2 is presented in Section 3.2.