esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiservice-Based Traffic Scheduling for 5G Access Traffic Steering, Switching and Splitting
## 4 Simulation Results and Analysis  
### Simulation Parameters  
In this study, the feasibility of using the proposed MSTT algorithm and the network capacity realized with it were experimentally verified through simulations. To this end, an NR network was deployed as a macro-BS, thereby representing 3 GPP scenarios. Likewise, a WLAN was deployed as a micro-BS, representing non-3 GPP scenarios. In the proposed simulation platform, all UEs supported ATSSS, and the signaling interaction between the 3 GPP and non-3 GPP networks followed ATSSS rules. In our system simulation, the network architecture, protocols, signaling, and simulation scenarios were constructed in full compliance with 3 GPP standards. During the simulation, all UEs were randomly distributed, moved at a speed of 30 km/h, and changed position every 100 TTIs. The maximum transmitted power of each UE was 21 dBm. The handover time for the UE in the candidate set was called the intra-network handover delay, which was set to 10 m. If the BS to which the UE was handed over to was not in the candidate set, the handover time was called the inter-network handover delay and was set to 50 m.  
Additionally, we simulated a configuration wherein no UE demonstrated multi-path operation. This configuration is referred to as single connectivity (SC). In the SC scenario, the UE \(u\) connected to only one path, with maximum \(M_{u,c}\). According to the 3 GPP and the IEEE 802.11 g standards, the proposed channel model complied with 3 GPP 38.901 The cellular network structure was represented by seven NR BSs, and three WLAN APs were evenly distributed under each NR BS. The statistical window of the average load of the BS was 200 m. The under-loaded and overloaded scenarios were simulated by distributing different numbers of UEs for each BS, and an unevenly loaded scenario comprised a random combination of the under-loaded and overloaded scenarios.  
Each user-service type employed an FTP model with a service data-packet arrival rate \(\alpha\). The total uplink bandwidths of the NR network and WLAN links were 10 M Hz and 22 M Hz, respectively. Additionally, \"normalized effective network capacity\" in the simulation results referred to the effective network capacity in Hz/s.  
The most important parameters used in the simulations and their corresponding values are listed in Table 1.
# Low-Cost Nanocarbon-Based Peroxidases from Graphite and Carbon Fibers
## 2 Results and Discussion
### Phenolic Compounds Removal Utilizing Carbon Nanomaterials  
The removal of phenolic compounds using different carbon nanomaterials as the catalyst in presence of H\({}_{2}\)O\({}_{2}\) was explored. In a typical procedure [31], 1 mg of carbon nanomaterials was added into 1 mL of NaAc buffer (0.1 M, pH 3.5) containing 6 mM of phenol and 30 mM of H\({}_{2}\)O\({}_{2}\), and then incubated at 30 \({}^{\circ}\)C for 24 h. After 24 h of reaction, the residual phenolic compounds present in the supernatant was measured with potassium ferricyanide and 4-aminoantipyrine, to determin the removal efficiency by measuring the absorbance of the phenol complex at 490 nm(Figure 7a). It was found that 12.03%, 55.7% and 71.7% of the phenol were removed by Carbon nanomaterials-1, Carbon  
Figure 6: (**a**) Reaction between hydroxyl radia \(\bullet\)OH and terephthalatic acid (TA). Fluorescence spectra of TA with/without H\({}_{2}\)O\({}_{2}\) treated 24 h with carbon nanomaterials; (**b**) Carbon nanomaterials-1; (**c**) Carbon nanomaterials-2; (**d**) Carbon nanomaterials-3. In the reaction system, the TA, H\({}_{2}\)O\({}_{2}\) and carbon nanomaterials were 0.5 mM, 1 mM, and 20 \(\upmu\)g\(\cdot\)mL\({}^{-1}\), respectively. The fluorescence spectra were acquired with a LS55 fluorescence spectrometer (PerkinElmer, Waltham, MA, USA).  
nanomaterials-2 and Carbon nanomaterials-3 (Figure 7b). Carbon nanomaterials-2 was further used to remove homologues of phenol under the same condition. Among tested phenolic compounds, the removal efficacy of 2,4-dichlorophenol was 23.64%, the removal effiency of 4-methoxyphenol was 56.35%, the removal effiency of resorcinol was 21.2%.
## 5 Comparative Analysis Results and Discussion
### Comparison of ClaimsBERT against Other Network-Based Classifier Models
#### 5.2.3 Multilayer Perceptron  
A Multilayer Perceptron (MLP) is a feedforward artificial neural network. For this research, each MLP block consists of two fully-connected NN layers, connected by a GELU nonlinearity, which matches the configuration of the MLP modules used in the MLP-Mixer paper [28].  
For this research we also explored the relative impact provided by the number of MLP layers and their hidden units, the dense layers and their neurons, and the dropout rates for each layer stacked on top of BERT. The LrFinder function [53] was used to determine the optimum LR for each architecture. Table 5 reports the highest accuracy and F1 for each architecture. The highest accuracy for the combined BERT + MLP model was achieved by stacking three MLP blocks with 256 and 128 neuron sizes with a GELU activation function and a 0.5 dropout rate. These MLP blocks then connected to a dense layer with 64 hidden units and a 0.5 dropout rate on top of BERT, linked to one dense layer with the softmax activation function for the classifier.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Architecture**} & \multicolumn{1}{c}{**LR**} & **Accuracy** & **F1 Score** \\ \hline \multirow{3}{*}{**BERT + 1 BiLSTM**} & 1 Dense & \(5\times 10^{-5}\) & 0.94 & 0.93 \\  & **2 Dense** & \(6\times 10^{-5}\) & **0.95** & **0.94** \\  & 3 Dense & \(4\times 10^{-5}\) & 0.94 & 0.93 \\  & 1 Dense & \(8\times 10^{-5}\) & 0.92 & 0.91 \\ BERT + 2 BiLSTM & 2 Dense & \(5\times 10^{-5}\) & 0.93 & 0.92 \\  & 3 Dense & \(4\times 10^{-5}\) & 0.91 & 0.91 \\  & 1 Dense & \(1\times 10^{-4}\) & 0.91 & 0.91 \\ BERT + 3 BiLSTM & 2 Dense & \(8\times 10^{-5}\) & 0.94 & 0.92 \\  & 3 Dense & \(4\times 10^{-5}\) & 0.91 & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparing BERT + BiLSTM Classifier results. (Bold text indicates the model parameters achieving the highest accuracy.)  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Architecture**} & \multicolumn{1}{c}{**LR**} & **Accuracy** & **F1 Score** \\ \hline \multirow{3}{*}{**BERT + 1 LSTM**} & 1 Dense & \(7\times 10^{-5}\) & 0.92 & 0.9 \\  & **2 Dense** & \(2\times 10^{-5}\) & **0.94** & **0.93** \\  & 3 Dense & \(4\times 10^{-5}\) & 0.93 & 0.91 \\  & 1 Dense & \(3\times 10^{-5}\) & 0.92 & 0.91 \\ BERT + 2 LSTM & 2 Dense & \(7\times 10^{-5}\) & 0.94 & 0.92 \\  & 3 Dense & \(4\times 10^{-5}\) & 0.93 & 0.91 \\  & 1 Dense & \(2\times 10^{-5}\) & 0.93 & 0.91 \\ BERT + 3 LSTM & 2 Dense & \(1\times 10^{-4}\) & 0.92 & 0.91 \\  & 3 Dense & \(7\times 10^{-5}\) & 0.91 & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparing BERT + LSTM Classifier results. (Bold text indicates the model parameters achieving the highest accuracy.)
# Pseudorange Measurement Method Based on AIS Signals
## 5 Optimal Estimation of Timestamps  
In theory, the zero-crossing detection method can make the pseudorange measurement accurate up to 10 m. However, due to the existence of channel interference, the result of a single measurement is much lower than the accuracy. In a time slot, the AIS system can transmit up to 256 bits of message, which means that the receiver will get about 200 timestamp detection results during a one frame message transmission process. This makes it possible to use the results of multi-point measurements to optimize the estimation result of transmission delay so that the pseudorange measurement accuracy is closer to the theoretical result.
## 4 Discussion
### Effect of Envelope Surface Roughness on CHTC Calculation  
The CHTC is influenced by many factors, such as wind speed, surface roughness, fluid characteristics, and so on. However, considering that the convective heat transfer coefficient is mainly affected by wind velocity, we investigated the correlation between CHTC and wind velocity only. It should be pointed out that other influencing factors are also included in the fitting formula according to the analogy theory of heat and mass transfer, because the measuring parameters, such as air temperature (\(T_{a}\)) and test specimen surface temperature (\(T_{s}\)), are influenced by surface roughness and fluid characteristics. Therefore, the surface roughness has been considered in the CHTC calculation result.
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 4 Execution Environment Analysis
### Transaction Data Analysis  
A single transaction contains comprehensive information about a user action. The data required during the execution are encoded in the calldata, while the transaction object stores metadata for pre- and post-execution states. We focus on the following attributes that can provide clues for diagnosing errors:  
* [noitemsep]
* TO **Address**: The destination address is read directly from the transaction object. Because the smart contract may use the proxy pattern, this address can reference only a forwarding proxy and the implementation logic is located at a different address from the TO address. The proxy analysis in Section 4.2 handles such cases.
* **Function Selector**: The EVM interprets the first four bytes of calldata as the function selector, derived from the Keccak-256 hash of the function signature. For example, \"0 xa9059 ebb\" identifies \"transfer(address,uint256)\". Since a single transaction can call a single function, the function indicated by the function selector is regarded as the _entry point_ of this execution of the transaction.
* **Function Arguments**: The remaining bytes of calldata encode the ABI-formatted arguments.
* **Revert Reason**: If execution aborts, smart contracts provide a binary-encoded payload for error messages. It can also be obtained during off-chain simulation via eth_call or eth_estimateGas, and on-chain via eth_sendTransaction [18].
## 3 Methodology
### Data Pre-Processing
#### 3.3.2 Data Encoding  
Data encoding involves transforming the categorical data into numerical data while preserving the dataset's structure (i.e., the same column names and index). Encoding ensures that the categorical data is represented numerically and can be used as input for the algorithm. This can help improve the accuracy and performance of the algorithm. Encoding converts raw data into a format that machine learning algorithms may employ [44].  
Popular encoding methods include one-hot encoding since it is an easy process. In one-hot encoding, each object or feature is represented by a separate binary vector representing a category [45]. In contrast to embedding, which means each object or feature is a dense vector in a continuous space, label encoding gives each category a number label [46]. The appropriate encoding method must be used for the recommender system to be accurate and practical since it affects how the data is stored. In this study, we employed one-hot encoding.
# Dynamic Behavior Analysis of I-Shaped RC Beams under Combined Blast and Impact Loads
## 4 Numerical Results of I-Shaped RC Beams under Combined Loads
### Influences of Different Beam Spans  
In this section, the influence of different beam spans on the damage behavior of structures under combined blast and impact loads is discussed. When the proportional distance of the explosion is 0.32 m/kg\({}^{1/3}\) and the impact velocity is 6 m/s, the dynamic responses of the I-shaped RC beams with spans of 1.5 m, 2 m and 2.5 m are numerically calculated, respectively. Figure 9 shows the structural damage states of the beams with different spans under the two loading conditions. The results show that with the increase in the beam span, the damage area of the concrete increases obviously. This is due to the fact that the increased span results in more flexural moments in the mid-span of the beam. At the same time, we can also see that the damage changes of the structure are more obvious under the impact-blast load. As the beam span increases, the cumulative effect of flexural-shear stress from the impact loads and stress diffusion from blast loads increases. The longer the span of the beam, the more severe the concrete failure in the depth direction of the beam. Figure 10 shows the mid-span displacement time histories of beams at different spans. It can be seen that as the beam span increases, the maximum mid-span displacement also increases. When the impact-blast load is applied to the structure, the effect of the different beam span lengths on the maximum mid-span displacement is more sensitive than when the blast-impact load is applied to the structure.
## 5 Results of Health Monitoring
### Global Deformation
#### 5.1.3 Tower Top Deformations  
The measured tower top deformations are shown as Table 2. The tower settlement amounts were small after one year, and the transverse displacements of towers were more significant than the longitudinal ones. The southwest tower had offset 2.7 cm towards the road centerline during the system transformation due to the shearing center of girder cross-section is closer to road centerline as compared with the designed hanging point. The towers continued to offset towards the road centerline based on the measured data. The stress states of tower roots are worthy of attention during the long-time health monitoring. In addition, the towers offset longitudinally towards the mid-span slightly affected by concrete S&C. The towers should be biased towards the side-span in advance through adjusting the internal forces of the hangers close to towers during hanger tension process, which can reduce the adverse influences brought by concrete S&C.
# Automatic Roadway Features Detection with Oriented Object Detection
## 3 System Overview
### Model Training  
The model training is conducted using a backpropagation learning algorithm [54], and Adam (short for adaptive moment estimation) optimizer [10] is used to optimize the model. Cross-validation is applied to ensure a reasonable model training stopping criterion. To evaluate the model performance, the mean average precision (mAP) is calculated for each epoch. For mAP calculation, Related IoU (ArIoU) estimation [43] is used instead of the exact IoU calculation, which is considerably slower in the case of oriented objects. In addition, ArIoU is found to be a good estimate of the exact IoU. However, unlike in [43], during inference time the exact IoU calculation is used to produce more accurate object detections through the non-maximum suppression stage. The validation data mAP progression over training epochs in addition to the training data loss curve are used to define the training process stopping criterion. Once the validation data mAP trend decreases while the model is improving training data loss, training is stopped to avoid over overfitting. Binary cross-entropy loss is used for the class predictions, in addition to sum of squared error loss for all the other predicted descriptors.
# Forecasting of Power Demands Using Deep Learning
## 3 Results and Discussions
SVM was chosen as a benchmark model because previous researchers have proven that SVM can produce satisfactory performance across various power demands forecasting [40; 41; 42]. For a fair comparison, we also performed a hyperparameter search for the penalty factor and gamma using grid search and found that the best performing penalty factor and gamma are 1 and 0.001 for the facility capacity feature, 0.001 and 1 for the supply capacity feature, and 100 and 0.001 for the power consumption feature. ANN models have been used by many researchers and showed good performance such as [10; 14; 15]. We have also performed a grid search for hyperparameter optimization. We designed a two-layer ANN model where the first layer has 32 nodes followed by ReLU as a non-linear activation function. Then a dropout layer with a drop rate of 0.5 was added. The second layer has one node for prediction. Table 3 shows the comparison results between the proposed model and SVM and ANN models. It can be seen that the CNN model outperforms ANN and SVM in facility capacity and supply capacity features. However, in power consumption, SVM performs slightly better. It is known that deep learning models require big datasets for training them therefore, our future work will include collecting large datasets for more accurate forecasting models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **CNN** & **ANN** & **SVM** \\ \hline Facility Capacity (MW) & 0.025 & 0.502 & 1.285 \\ Supply Capacity (MW) & 0.214 & 0.262 & 3.218 \\ Power Consumption (MW) & 0.314 & 0.398 & 0.308 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison between the proposed model and support vector machine (SVM) and artificial neural networks (ANN) in terms of mean absolute error (MAE). Figure 8: An example of the supply capacity prediction performance of the CNN model. Figure 9: An example of the power consumption prediction performance of the CNN model.
## 3 Results
### Characterization of PS/OMMT Nanocomposite
#### 3.2.4 DLS Analysis  
Figure 8 shows the size distributions of the pure PS and PS/OMMT nanocomposite samples, and the average particle size data and PDI values are summarized in Table 2. The particle size distributions of all samples showed only one narrow peak, and the average diameters of the polymer particles were in the range of 241.3-378.6 nm. These results were consistent with the results of the TEM analysis. The average particle size of the pure PS was 378.6 nm, while the average particle sizes of the PS/OMMT nanocomposites were 290.4 nm, 338.8 nm, and 241.3 nm for the PS/P-MMT, PS-P-SLS-MMT, and PS/P-KH570-MMT, respectively. The decrease in the average particle size of the nanocomposites was owing to the addition of OMMT would more likely to facilitate the nucleation to form smaller and more uniform particles. On the other hand, the PDI values, which were indicative of the monodispersity of polymer particles, were observed to decrease for the PS/OMMT nanocomposites compared with the pure PS. This observed decrease was owing to the self-arrangement of the modified MMT layers within the PS particles might affect the surface tension and cause fine and stable particles. Moreover, the hydrophobic MMT layers could act as stabilizers to improve the monodispersity of the PS particles [38]. The organic modification of MMT improved its compatibility with the monomer phase and promoted the exfoliation of the MMT, which was beneficial to the encapsulation of clay platelets by the polymer particles. The smallest particle size and PDI value were obtained for the PS/P-KH570-MMT sample, which was due to the ability of the vinyl group of the KH570 molecule to participate in the polymerization reaction.
# \(HM^{3}a1D\): Polymorphic Malware Detection Using Program Behavior-Aware Hidden Markov Model
## 5 Experimental Evaluation
### Experimental Setup  
Detection in \(HM^{3}aID\) was performed at runtime, so recording the system call sequence of a running program must be started when the program begins to run. \(HM^{3}aID\) uses Cuckoo sandbox tool which provides an isolated and safe environment to run programs. All the benign and malware programs are executed under Cuckoo sandbox tool [32] on a host, in a virtual environment. A machine with Intel Core i7-4790K processor and 16 GB RAM was used to execute all experiments. We installed the sandbox tool under Ubuntu-13.03 on this machine. The guest OS in this experiment was Windows XP 32 bit. For each program, Cuckoo sandbox restores the guest OS to a safe state, and then, after executing the program, it returns the corresponding system call sequence in JSON format comprehensively. To extract the action set, we developed a set of basic tools in C++ and they are executed in the sandbox environment. We implemented Algorithm 1 as a python script to drive the action sequence of a program from its corresponding system call sequence. We clustered benign (malware) action sequence sets using MATLAB toolbox. We used GHMM library with python wrapper [37] to implement HMM algorithms.  
To evaluate the performance of \(HM^{3}aID\), we used cross-validation strategy. The malware program set was randomly partitioned into two parts, 70% for learning malware HMMs \(\lambda_{c_{i}}\) and computing the corresponding malware decision thresholds, and 30% for detection phase. Similarly, the benign program set was randomly partitioned into two parts, i.e. 30% for computing the decision thresholds and 70% for detection phase. To train good HMMs, \(\lambda_{c_{i}}\), we randomly repeated the cross-validation strategy 10 times. Note that, in the learning process, Algorithm 2 (Baum-Welch algorithm) was repeated 30 times.
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## 5 Conclusions  
In SAR observations, high resolution and wide swath contradict each other. This paper proposes a novel SAR imaging system based on tandem SAR satellites, where Sat-1 works in the traditional scanning mode to obtain coarse observations of a wide swath, and Sat-2 adopts a new scanning method to obtain the downsampled echo from the same swath. Then, a minimum-energy-based algorithm is proposed to associate the two SARS data and achieve high resolution. Since the accurate phase synchronization between two satellites is unnecessary, the system cost can be significantly reduced. The simulations and processed results of real airborne SAR data have proven that the proposed imaging system can expand the range swath of the strip-map mode by up to six times and achieve the high-resolution imaging of observed scenes.  
Figure 15: Illustration of \(SNR_{ME}\) curves for different subswath numbers, where \(N\) is the number of subswaths.  
**Author Contributions:** Conceptualization, L.S. and C.L.; methodology, L.S. and C.L.; software, L.S.; validation, L.S.; formal analysis, L.S.; investigation, L.S.; resources, L.S.; data curation, L.S.; writing--original draft preparation, L.S.; writing--review and editing, L.S. and C.L.; visualization, L.S.; supervision, C.L.; project administration, C.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### The Effect of TNF-\(\alpha\) on Caco-2 Epithelial Barrier Integrity Monitored by TEER Method  
The monolayer characteristic of Caco-2 cells was observed by transepithelial electrical resistance (TEER) using the Millicell-ERS 2 (Electrical Resistance System) Voltohm meter (Merck Millipore, Burlington, MA, USA). Caco-2 cells (2 \(\times\) 10\({}^{4}\) cells/well) were seeded onto microporous filters in cell culture chamber inserts (Corning(r), 6.5 mm Transwell(r) with 0.4 \(\mu\)m Pore Polycarbonate Membrane Insert, product number 3413, Corning, NY, USA) for 21 days. The polycarbonate membrane is semipermeable, which allows the exchange of ions and nutrients. The medium was changed every 48 h with complete medium supplemented with 20% FBS in the apical and basolateral compartments in the first 7 days of seeding, followed by complete 20% FBS medium in the basolateral compartment only and 1% FBS in the apical compartment for the next 2 weeks [19]. Once the Caco-2 cells differentiated and a monolayer was established, a pair of electrodes was inserted in each well, and the electrical resistance was measured until similar values were recorded for three consecutive measurements [12]. The resistance was measured in Ohms (\(\Omega\)). Different doses of TNF-\(\alpha\) (ThermoFisher Scientific, Waltham, MA, USA) were added in the apical compartments, followed by the measurement of resistance in \(\Omega\) at different time intervals: 0, 4, 8, 12, 24, and 48 h [20].