esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
The first experiment involved applying ML models using the original imbalanced dataset. For the other experiments, the balanced data were used with under- and oversampling techniques. The calculated weighted average (which gives more importance to values appearing more often in the data) used the numbers of data points in the majority and minority class as weights. On the other hand, the results of the macro average show the arithmetic mean of the metric values for each class, treating each class equally, regardless of the class size. Both averages are presented in Tables 2-8. Table 2 reports the metric values obtained for all algorithms with the data in their state after initial preprocessing. We are interested in the highest values (numerical values represent percentages (%)). The obtained results show that the best performance was achieved by RF. The other classifiers, in general, are limited to predicting the minority class. The performance results of the ML models were obtained before applying sampling techniques. Tables 3 and 4 show the results obtained by the KNN algorithm with two neighbors after the application of under- and oversampling techniques. The highest sampling values \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Metric/Sampling** & **KNN2** & **KNN3** & **bLR** & **RF** \\ \hline Accuracy & 93.12 & 91.53 & 92.59 & 95.76 \\ Ba Accuracy & 53.57 & 52.71 & 50.00 & 71.42 \\ Precision (Mac;Wight) & 96.5493.59 & 58.98,87.93 & 46.29,85.73 & 97.81,95.95 \\ Recall (Mac;Wight) & 53.57,93.12 & 52.71,91.53 & 50.00;92.59 & 71.42,95.76 \\ MaiClass & 100 & 98.28 & 100 & 100 \\ MinClass & 7.14 & 7.14 & 0 & 42.85 \\ F1-Score (Mac;Wight) & 54.87,90.26 & 53.33,89.30 & 48.07,89.03 & 78.88,94.96 \\ ROC\_AUC & 0.61 & 0.63 & 0.71 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance results of ML models (before applying sampling techniques). for KNN were achieved by the Borderline SMOTE technique, but all methods generated much higher values than the models applied to the original dataset. ADASYN showed the least effectiveness overall, particularly in terms of accuracy and ROC_AUC score. These insights also suggest that SVM SMOTE is an effective oversampling technique for enhancing the performance of the KNN2 model. Among the undersampling techniques, NM1 and IHT predicted 78.57% and 85.71%, respectively. NCR stands out with a statistically significant ROC_AUC score of 0.80, which is notably higher than that of the rest. When excluding NCR, the remaining values do not show notable differences. The rest of the metric values were not higher than the original values. Tables 6 and 6 present the results achieved by bLR. The good results for the AUC curve of all SMOTE methods must be noted. The undersampling techniques were not spectacular. CNN, TL, All-KNN, RENN, ENN, and NCR maintained high accuracy but at the cost of ignoring the minority class, resulting in poor balanced accuracy and macro metrics. NM1 and IHT handled the minority class better, but this came at the expense of overall accuracy.
# A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks
## 4 Applications of RL in FANET
### Other Scenarios  
There are other usages and challenges of FANETs, such as charging UAVs, using UAVs as network relay, using UAVs to give protection against jamming, that were solved by some researchers using RL. The applications of RL in these scenarios are summarized in Table 4.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Type** & **Algorithm** & **Characteristics** & **Goal** \\ \hline Relaying & Deep \(Q\)-learning & Multiple UAVs are positioned in dynamic UAV swarming applications by using the replay-buffer-based DQN learning algorithm which can keep track of the network topology changes [63]. & Achieve the optimal communication among swarming nodes. \\ \hline Protection against jamming & Federated \(Q\)-learning & [PERSON] et al. developed a cognitive jamming detection technique using priority-based federated learning in [64]. Then, the authors developed an adaptive model-free jamming defense mechanism based on federated Q-learning with spatial retreat strategy in [65] for FANETs. & Jamming protection for other networks. \\ \hline Charging UAVs & Deep \(Q\)-learning & The mobile charging scheduling problem is interpreted as an auction problem where each UAV bids its own valuation and then the charging station schedules drones based on it in terms of revenue optimality. The charging auction enables efficient scheduling by learning the bids distribution using DQL [11]. & Scheduling UAVs for charging. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of other scenarios based on RL in FANET.
## 4 Results and Discussion  
In order to confirm the accuracy of the applied model, the results of this study were compared with the experimental results of [11]. The results of this study were in reasonable agreement with the experimental results, thereby verifying the present numerical methodology. A close agreement exists between the present numerical calculations of velocity with the experimental results at lower mass flow rates, where the maximum percentage error was limited to 4.6% and 5.4% at a mass flow rate of 1.6 kg/s and 2 kg/s. However, at a high mass flow rate, the predicted values of velocity deviate from the experimental values considerably, where the maximum percentage error was 18.59% and 18.89% at a flow rate of 2.4 and 2.8 kg/s, respectively (Figure 2).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No of Mesh Elements** & **U (m/s)** \\ \hline (523,359) & 0.5165 \\ (134,191) & 0.5128 \\ (60,175) & 0.5110 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mesh independence test (_in_ 2.4 kg/s, d = 5.6 mm, slope = 1\({}^{\circ}\)) for velocity averaged on the entire reactor volume.  
The hydrodynamic performance and algae cell mixing features of a thin-layer cascade reactor have been evaluated by using computational fluid dynamics. The hydrodynamic performance based on the proposed numerical methodology was evaluated by computing the hydrodynamic properties, namely, the power consumption and velocity magnitude. In addition to the variation in mass flow rate, geometric parameters such as the channel depth, channel width, and slope angle were the main criteria for the calculation of these properties. Moreover, the algae cell mixing characteristics of the TLC reactor were also evaluated based on the residence time and mixing efficiency of the reactor with the variation in the above-mentioned geometric properties of the reactor.
# Digital Systems in Smart City and Infrastructure: Digital as a Service
## 8 Digital Management
### Building Management  
Building management system (BMS), controls and monitors the station, airports or building's mechanical and electrical equipment of the Smart Infrastructure including heating, ventilation, and air conditioning (HVAC), lighting, power, lifts, escalators and fire systems. Similar to the SCADA System, originally the BMS protocols were vendor specific such as C-Bus, Profibus; however, the migration and integration with IP has developed open standards such as DeviceNet, SOAP, XML, BACnet, LonWorks and Modbus. BMS is key to manage the energy consumption of the Smart Infrastructure.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 3 Possible Attack Types on Physical and MAC Layer
### Jamming Attack Types
Also, Frequency Ranges (FR) are a factor that have to be calculated in jammer design, since the hardware complexity of FR2/mmWave jammers is significantly greater than the jamming of the sub 6 GHz channels.
## 4 System Implementation and Simulation Results
### Efficiency Analysis
However, the scaling potential of discrete  
\[P_{DC-DC} =P_{Cond.}+P_{Sw.} \tag{9}\]2 Output power versus temperature (**top left**), output current versus temperature (**middle top**), total efficiency versus temperature (**top right**), gate driver losses versus temperature (**bottom left**), \(M_{2}\) losses versus temperature (**middle bottom**), \(M_{1}\) losses versus temperature (**bottom right**). Terms with the Adj indicator denote an adjustment in delay times. \[P_{DC-DC} =P_{Cond.}+P_{Sw.} \tag{9}\]3 Dead times across the entire temperature range. implementations is limited. Our design is distinguished by its combination of higher temperature operations (which can be increased up to 500 \({}^{\circ}\)C at the testing phase [54]) and high power density, which makes it a well-fitted candidate for applications where low volume and harsh-environment tolerance are required.
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 6 Conclusions  
This study introduces a novel GPP remote sensing estimation model, SA-LGBM, which incorporates considerations for spatial autocorrelation. We found that incorporating spatial factors led to varying degrees of improvement in model accuracy for different machine learning methods. Specifically, the SA-LGBM model exhibited an 8.5% increase in \(R^{2}\) and a 20.8% decrease in RMSE after incorporating spatial factors. Compared to LGBM, SA-LGBM reduces the spatial autocorrelation of prediction residuals. The residuals of the model are more random, with less spatial structure. EV can be considered as a variable capturing the spatial distribution characteristics of GPP, which has a positive effect on the model. Furthermore, the SA-LGBM model showed relatively favorable results compared to existing GPP products. Compared to MOD17 and GOSIF, SA-LGBM achieved an average increase in \(R^{2}\) of 0.21 and 0.17 across different time scales. SA-LGBM demonstrates good applicability across different vegetation types and can effectively capture the spatiotemporal variation characteristics of GPP. These findings align with our expectations, indicating that incorporating spatial information helps capture spatial autocorrelation and improves the accuracy of GPP estimation.  
Conceptualization, R.X. and Y.C.; methodology, Y.C. and G.H.; software, J.M.; validation, W.M. and R.X.; formal analysis, R.X.; investigation, W.M.; resources, Y.C.; data curation, R.X.; writing--original draft, R.X. and J.P.W.; writing--review and editing, Y.C., M.G. and G.H.; visualization, R.X.; supervision, Y.C. and J.P.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China [Project No. 2022 YFB3902300] and the Fundamental Research Funds for the Central Universities, China [Grant No. 2042022 dx0001].  
All datasets used in this paper are publicly available and the URLs are provided in the Data and Preprocessing Section.  
The authors declare no conflicts of interest.
## 2 Research Background
### Message Exchange Mechanism
It is obvious that some schemes in Table 2, like DNRL, UPS and LDB do not consume transmitting energy from ordinary sensor nodes. The silent positioning features of these methods can conserve bandwidth and improve network throughput since underwater sensors do not transmit any message for positioning purposes, but they have low average response times and they are only suitable for applications involving gathering of environmental data. On the other hand, active localization methods involve intensive messages and also the localization response times are not fast enough for surveillance and prevention applications. Hence, RLS is the novel localization method with least possible message exchange and fast reaction to the events. RLS significantly decreases the number of messages transmitted by underwater sensor nodes. Moreover, the small packet size of RLS deserves further emphasis. None of the existing underwater localization schemes consider reducing the transmitted packet size in order to preserve the energy consumption and increase the available bandwidth. RLS considers disaster prevention and coastline protection applications like estimating tsunami location occurrence in deep waters to inform early warning alarms.
# Nonlinear Finite Element Analysis Formulation for Shear in Reinforced Concrete Beams
###### Abstract  
This study suggests a novel beam-column element formulation that utilizes an equilibrium-driven shear stress function. The beam shear is obtained from the bi-axial states of micro-planes, through matrix condensation and zero vertical traction assumptions. This properly remedies the shear stiffening of a one-dimensional beam-column element, keeping its degrees of freedom to a minimum. For verification of the proposed method, a total of seven shear test results of reinforced concrete (RC) beams were collected from the literature, in which the key variables were the reinforcement ratio, the presence of shear reinforcement, and section shape. The advantages are clearly shown in the shear stresses distributions being accurately described and the global load-displacement relations being successfully obtained and matching well with various test results. The proposed model shows satisfactory descriptions of the monotonic load-displacement response of the RC beams failing in multiple modes that vary from diagonal-tension to flexural-compression. In addition, more accurate and reliable information of sectional responses including sectional shear deformation and stresses is collected, leading to better prediction of a potential shear failure mode. Finally, the advantages of the proposed model are demonstrated by comparing the analysis results of an RCT-beam by using the different shear assumptions that include the constant and parabolic shear strains, constant shear flow, and the proposed shear stress function.  
nonlinear analysis; beam-column element; beam shear; RC beam +
Footnote †: journal: Applied Mechanics and Physics
# Performance of Graph and Relational Databases in Complex Queries
## 6 Test Executions
### Test Results
#### 6.2.1 Query Performance in Task 1  
The performance results for Task 1 are illustrated in Figure 8. From the generated dataset, the query returned 10,000 rows/objects. With this query, Neo4j outperformed relational databases. The best run of Neo4j was about three times faster than the best run on relational databases. Between MySQL 8.0 and MariaDB, no essential difference  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MySQL 5** & **MySQL 8** & **MariaDB** & **Neo4J** & **Neo4J** & **CALL** \\ \hline Task 1 (Short Query) & & & & & \\ Avg & 576 & 464 & 486 & 162 & 149 \\ Avg, ind & 453 & 459 & 472 & 173 & 149 \\ Task 2 (Long Query) & & & & & \\ Avg & 6550 & 5337 & 5549 & 1868 & 1776 \\ Avg, ind & 5190 & 5257 & 5293 & 1968 & 1831 \\ Task 3 (Aggregate Query) & & & & & \\ Avg & 276,935 & 7674 & 7242 & 212,171 & 209,816 \\ Avg, ind & 251,138 & 7615 & 7117 & 215,053 & 205,198 \\ Task 4 (Aggregate Query with defined key) & & & & \\ Avg & 3,938,500 & 5212 & 59 & 33 & 57 \\ Avg, ind & 3,891,082 & 5227 & 57 & 26 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Query performance of the MySQL, MariaDB and Neo4J.  
appeared. Among versions of MySQL, the old version 5.1 was the fastest, when indices were used. In other databases, indexing played a minor role in efficiency. The inclusion of CALL brought minor benefits to Neo4j with this query. The numeric performance values are given in Table 3.
# Mathematical Description of Changes of Dried Apple Characteristics during Their Rehydration
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Rehydration Index** & \multicolumn{4}{c}{**Weights and Biases between Input and Hidden Layer**} & \multicolumn{4}{c}{**Weights and Biases between Hidden and Output Layer**} \\ \hline  & **No.** & \multicolumn{4}{c}{**Weights**} & **Bias** & **Weights** & **Bias** \\ \hline  & \(i\) & \(D_{1i}\) & \(D_{2i}\) & \(D_{3i}\) & \(D_{4i}\) & \(D_{i}\) & \(W_{i}\) & \(B_{i}\) \\ \cline{2-8}  & \(1\) & \(0.76302\) & \(-4.30465\) & \(6.1141\) & \(4.1011\) & \(3.2308\) & \(3.8703\) & \\ RI\({}_{1}\) & \(2\) & \(-0.18017\) & \(5.2373\) & \(-2.251\) & \(-9.0609\) & \(-0.41154\) & \(0.46843\) & \\  & \(3\) & \(-5.4656\) & \(2.1371\) & \(-5.5557\) & \(4.6212\) & \(-1.931\) & \(4.4227\) & \\  & \(4\) & \(-8.7999\) & \(2.4317\) & \(-0.35888\) & \(3.979\) & \(0.66115\) & \(-6.7914\) & \\ \hline  & \(1\) & \(-1.8885\) & \(2.0685\) & \(1.1286\) & \(-0.37259\) & \(-2.3498\) & \(3.0742\) & \\ RI\({}_{2}\) & \(2\) & \(0.50806\) & \(0.55895\) & \(-7.7709\) & \(-0.84271\) & \(0.62434\) & \(-2.1788\) & \\  & \(3\) & \(-3.4158\) & \(-0.11008\) & \(-10.5825\) & \(-1.202\) & \(0.85564\) & \(8.3004\) & \\  & \(4\) & \(0.32057\) & \(-4.9892\) & \(2.4163\) & \(-5.1056\) & \(1.52\) & \(3.3142\) & \\ \hline  & \(1\) & \(-0.32603\) & \(-0.53705\) & \(-0.88337\) & \(2.8526\) & \(-2.0969\) & \(-1.0055\) & \\ WAC & \(2\) & \(9.2834\) & \(-46.5133\) & \(-60.2563\) & \(122.4035\) & \(0.81527\) & \(7.7133\) & \\  & \(3\) & \(-22.1376\) & \(-52.8683\) & \(0.59559\) & \(19.7618\) & \(0.54159\) & \(-62.5777\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Weights and biases between input and hidden layer and between hidden and output layers for rehydration indices. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rehydration Index** & **R** & **RMSE** & \(\chi^{2}\) \\ \hline RI\({}_{1}\) & 0.9424 & 0.0756 & 0.0065 \\ RI\({}_{2}\) & 0.9376 & 0.0787 & 0.0070 \\ WAC & 0.9270 & 0.0802 & 0.0070 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of statistical analyses on the modelling of rehydration indices. pretreatment, drying, and rehydration. The value of the rehydration indices being discussed is predominantly influenced by the mass of the rehydrated apple. This mass depends on the one hand on the amount of water absorbed, and on the other hand, on the amount of dry matter lost. Both of the masses depend on the degradation magnitude of changes during drying but also on the rehydration conditions. Increasing the \(T_{r}\) causes deterioration in the texture, which is connected by the damage caused during drying and promotes a significant loss in mechanical resistance and then causes the alterations of the mass transfer ability of the system [22].
## 5 Simulations
### Impact of the Algorithm Design Choices on the Structure of the DG Trees and their Performance  
The simulation results vindicate the impact of the design choices behind the algorithms for the construction of the two categories of data gathering trees (bottleneck node-weight _vs._ bottleneck link-weight based trees) on their structure as well as the performance. The smaller diameter of the bottleneck node-weight based data gathering trees is primarily attributed to limiting the _Tree-join-weight_ of a downstream node from not becoming more than the _Tree-join-weight_ of an upstream node (in the case of MaxBNW-DG trees) or from not becoming less than the _Tree-join-weight_ of an upstream node (in the case of MinBNW-DG trees). As a result, a node that is not yet included in a bottleneck node-weight based DG tree simply prefers to join the tree through an upstream node that has the largest _Tree-join-weight_ (for MaxBNW-DG trees) or the smallest _Tree-join-weight_ (for MinBNW-DG trees). Such upstream nodes are more likely to be closer to the root/leader node of the data gathering tree, as the _Tree-join-weight_ for any node cannot be larger than that of the leader node (for MaxBNW-DG trees) or smaller than that of the leader node (for MinBNW-DG trees). In the case of bottleneck link-weight based DG trees, there is no such restriction as seen in the case of bottleneck node-weight based DG trees. A node that is not yet included in a bottleneck link-weight based DG tree could simply join the tree via any upstream node through a link of the largest weight (in case of MaxBLW-DG trees) or the smallest weight (in case of MinBLW-DG trees). Such upstream nodes need not be always closer to the leader node, and this contributes to a relatively larger diameter (and a lower number of child nodes per intermediate node) for the bottleneck link-weight based DG trees vis-a-vis the bottleneck node-weight based DG trees. Thus, the simulation results observed here are only influenced by the structure of the data gathering trees and their construction procedure, and are not influenced by the configuration of the simulation. In other words, the simulation results merely confirm the performance expected of the two categories of DG trees owing to their design choices.
# The Survey of Lava Tube Distribution in Jeju Island by Multi-Source Data Fusion
## 4 Results
### Machine Learning for Point Classification
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \begin{tabular}{c} **Selection Standard** \\ **(Velocity (mm/Year) and** \\ **Standard Deviation** \\ **(Std.dev.))** \\ \end{tabular} & **Constraints** \\ \hline V-migrations & 50 & \begin{tabular}{c} Velocity \(<-1.0\) \\ Std.dev. \(>0.5\) \\ \end{tabular} & \begin{tabular}{c} Soumgol and Manjang \\ cave around road \\ crossing \\ \end{tabular} \\ H-migration & 17 & \begin{tabular}{c} Velocity \(>-0.75\) \\ Std.dev. \(>0.5\) \\ \end{tabular} & \begin{tabular}{c} Soumgol and Manjang \\ cave around road \\ crossing \\ \end{tabular} \\ Regional & 483 & \begin{tabular}{c} Velocity \(<-2\) \\ Std.dev. \(>1.0\) \\ \end{tabular} & \begin{tabular}{c} Seogwipo sediment unit \\ cave around road \\ crossing \\ \end{tabular} \\ Instability points & 433 & \
\begin{tabular}{c} Velocity \(<4.5\) \\ Std.dev. \(>1.0\) \\ \end{tabular} &
\begin{tabular}{c} Jeju City area \\ \end{tabular} \\ by artificial structures & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The number and standard of training data. Figure 6: Training point locations of V-migration, H-migration, regional subsidence, and instability points by artificial structures for machine learning. The training vectors for regional subsidence were chosen in Seogwipo City, where Jeju Island’s only sediment unit exists. Training vectors for instability caused by artificial structures were built in the Jeju City area, where the largest buildings are concentrated. Once the training vectors were established, point classification was conducted. We employed two algorithms--random forest (RF) [63; 64] and gradient boosting (GB) [65; 66] methods. RF uses multiple decision tree construction in the training stage and bootstrap aggregation of their outputs as an ensemble method. Thus, it is effective to prevent overfitting and upgrading modeling accuracy. Since [67] presented its applications for classification, it has been widely used, and the capability for manipulating spatial data sets has been demonstrated. GB is an algorithm to exploit residual fitting of the model as a boosting approach; it is known as a classifier with high precision. We applied both algorithms on PS points screened by the mask through the spatial analyses. As a result, it was observed that the RF outputs have significant problems detecting H-migrations, as only 1% of instability points were classified as H-migrations. Therefore, we excluded RF results for further analyses. The detected H/V-migrations in potential LTDPs using the GB algorithm are presented and discussed in Section 5. Figure 7: Deformations and standard deviations for training vectors on (**a**) collapse (PGS)-road crossing points, (**b**) Manjang cave-road crossing points, (**c**) deformation points in Seogwipo sediments, (**d**) deformation points in Jeju City urban area, and (**e**) deformations points on H-migrations. A five-window-sized moving average filter was applied to the original data set to observe the trend in the time domain.