esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Hyperparameter Optimization  
Hyperparameter optimization is the process of determining the optimal collection of values for hyperparameters for ML and DL models. ML algorithms are improved by grid search and cross-validation, while a Bayesian optimizer is used to optimize the architecture and the hyperparameters of DL models. We use the standard KerasTuner implementation for the Bayesian optimizer.  
* Grid Search: The hyperparameters' ideal values are obtained via a tuning procedure. When the Model had several hyperparameters, it became required to search in a multidimensional space for the best combination of values for the hyperparameters [34; 35]. Grid search is a hyperparameter tuning method that divides the hyperparameter domain into distinct grids and obtains the optimal combination of hyperparameter values.
* An evaluation method for learning algorithms known as cross-validation separates data into two parts: one for training models while the other is for model verification [36]. Cross-validation includes a single parameter, k, that indicates how many groups a given data sample should be divided into, which is why it is also known as k-fold cross-validation. Cross-validation is seen to be a strong preventive technique against overfitting because the first fold is utilized for the validation set, while the other k-1 folds are provided to the learning system to ensure that the model is estimated using data that were not seen during training [37].
* The KerasTuner hyperparameter optimization system includes the hyperband, Bayesian optimization, and random search algorithms. The optimal hyperparameter values for the models are found by employing one of the search algorithms after the search space is set up using a define-by-run syntax [38]. Hyperparameters are variables that govern the model's topology and training process and remain constant throughout the training phase, affecting the model's performance. There are two sorts of hyperparameters: process hyperparameters, which impact the quality and speed of the learning algorithm, and model hyperparameters, which control the number and breadth of hidden layers in the model [39]. For sophisticated models, the number of hyperparameters can be considerably expanded, making manual tuning difficult, and underscoring the need for the techniques. In our work, we optimized some of the values of parameters set for DeepCNN, CNN-LSTM, and CNN-GRU, as shown in Table 1.
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
## 4 Experimental Results
Moreover, it shows the residual energy is more stable than competitive protocols. Results show that the ELR-W consumes energy 14% and 45% less than iM-SIMPLE and M-ATTEMPT correspondingly. ELR-W achieved this because of using the effective criteria for the selection of next-hop in the network. The selection criteria are based on path cost expressed in Equation (4). The proposed path cost function supports the load balancing in the network which increases throughput along with a lower number of packet drops. As a result, there are fewer packet retransmission attempts in ELR-W protocol which reduces the overall energy consumption in the network. Tables 3 and 4 show the performance of ELR-W in a nutshell as compared to the benchmark protocols. Figure 5: Analysis of network lifetime. Figure 6: Analysis of network throughput. Figure 7: Analysis of packet drops. It is noteworthy as shown in Table 4 that the ELR-W protocol outperforms 19% and 102% in terms of increased throughput, 30% and 34% in increased network lifetime and, 14% and 45% in reduced energy consumption as compared to the benchmark protocols iM-SIMPLE and M-ATTEMPT respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Protocols**} & \multicolumn{3}{c}{**Performance of ELR-W against Benchmark Protocols**} \\ \cline{2-4}  & **Throughput** & **Energy Consumption** & **Network Lifetime** \\ \hline iM-SIMPLE & 19\% \(\uparrow\) & 14\% \(\downarrow\) & 30\% \(\uparrow\) \\ M-ATTEMPT & 102\% \(\uparrow\) & 45\% \(\downarrow\) & 34\% \(\uparrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of ELR-W against competitive protocols with increase\(\uparrow\)or decrease\(\downarrow\)trend. Figure 8: Analysis of energy consumption.
# MIMO Signal Detection Based on IM-LSTMNet Model
## 2 Methods  
This section explains the framework for detecting signals from an FrFT-based MIMO-OFDM system. Figure 2 describes the flow diagram for offline training and online signal detection of the FrFT-based MIMO-OFDM system by our proposed IM-LSTMNet model.  
Figure 2 clearly illustrates that we construct a MIMO-OFDM system and replace the Fourier transform in OFDM modulation with FrFT to increase the flexibility of the system. Then, the IM-LSTMNet network model is trained using MIMO-OFDM signals based on FrFT. The IM-LSTMNet model combines the traditional CNN with the LSTM neural network, and then adds the Squeeze-and-Excitation Network to the attention mechanism module. Finally, at the online testing, the proposed IM-LSTMNet model is used to detect the signal of the FrFT-based MIMO-OFDM system. The superiority of the proposed model in the process of signal detection is verified.
## 3 Results
### Mitigation of Odor and Targeted Gases as a Function of UV Wavelength
#### 3.3.3 Volatile Organic Compounds--Effect of PM Size and Concentration  
As the suspended PM concentration increased, the mitigation of VOCs decreased (Table 11). The three VOCs showed a statistically significant reduction under the lowest suspended PM concentration (0.004 mg\(\cdot\)m\({}^{-3}\); with MERV 8 and 15 filtration). However, no statistically significant mitigation was observed under the worst-case scenario (no filtration, total PM concentration: 0.22 mg\(\cdot\)m\({}^{-3}\)). The odorous VOC mitigation was very similar to the result of the reduction of odor, i.e., underscoring PM's importance as a carrier of sorbed odorous VOCs [22].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Total PM** & **PM Filtration** & **UV Dose** & **Control** & **UV** & **\% Reduction** \\
**(mg\(\cdot\)m\({}^{-3}\))** & **Status** & **(m)\(\cdot\)cm\({}^{-2}\))** & **(OUE\({}_{\text{E}}\)\(\cdot\)m\({}^{-3}\))** & **Treatment** & **(\(p\)-Value)** \\ \hline \multirow{3}{*}{0.22} & Unfiltered & & & & \\  & swine barn & 5.3 & 685 \(\pm\) 52 & 623 \(\pm\) 83 & 8.5 (0.44) \\ \cline{1-1}  & exhaust & & & & \\
0.06 & MERV 8 & 5.3 & 923 \(\pm\) 77.1 & 737 \(\pm\) 73.4 & 20.2 (0.19) \\ \cline{1-1}
0.004 & MERV 8 and 15 & 5.3 & 412 \(\pm\) 47 & 251 \(\pm\) 4.58 & **39.0 (0.04)** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance of UV-A photocatalysis in mitigating odor under different PM conditions. UV-A dose: 5.3 mJ\(\cdot\)cm\({}^{-2}\), airflow = 0.28 m\({}^{3}\)\(\cdot\)s\({}^{-1}\). Values in the table are mean \(\pm\) S.D. Bold signifies statistical significance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Total PM** & **PM Filtration** & **UV Dose** & **Control** & **UV** & **\% Reduction** \\
**(mg\(\cdot\)m\({}^{-3}\))** & **Status** & **(m)\(\cdot\)cm\({}^{-2}\))** & **(ppb)** & **(ppb)** & **(\(p\)-Value)** \\ \hline \multirow{3}{*}{0.22} & Unfiltered & & & & \\  & swine barn & 5.3 & 0.273 \(\pm\) 0.008 & 0.244 \(\pm\) 0.007 & **10.6 (0.02)** \\ \cline{1-1}  & exhaust & & & & \\
0.06 & MERV 8 & 5.3 & 0.267 \(\pm\) 0.002 & 0.244 \(\pm\) 0.002 & **8.5 (0.01)** \\ \cline{1-1}
0.004 & MERV 8 and 15 & 5.3 & 0.311 \(\pm\) 0.004 & 0.282 \(\pm\) 0.006 & **9.3 (0.01)** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance of UV-A photocatalysis in mitigating N\({}_{2}\)O concentrations under different PM conditions. UV-A dose: 5.3 mJ\(\cdot\)cm\({}^{-2}\), airflow = 0.28 m\({}^{3}\)\(\cdot\)s\({}^{-1}\). Values in the table are mean \(\pm\) S.D. Bold signifies statistical significance.
## 3 Mathematical Formulation/Performance Metrics
### Mathematical Formulation Model  
Application mapping for 2D NoC design, including preliminary knowledge and a mathematical model, is presented in this subsection. The following elements constitute the inputs for formulating the application mapping problem.  
**Definition 1**.: _A Communication Trace Graph (CTG) is defined as an undirected communication trace graph \(G=(V,E,M)\) that is weighted and consists of a set of vertices or cores, with \(V\), \(E\) representing the directed edge set and \(M\) referring to the volume of data and the connectivity between nodes in MS/s._  
\[V=\{c_{1},c_{2},c_{3},\ldots,c_{n}\} \tag{12}\]  
_where \(|V|=finite(n)\)_  
\[E=\{e_{ij}=(c_{i},c_{j})\in C\times C|(c_{i},c_{j})\in C,i\
eq j\} \tag{13}\]  
_and_  
\[M:E\to M(c_{i},c_{j})=M_{ij} \tag{14}\]  
_where \(|V|=finite(n)\)._  
**Definition 2**.: _In the Topology Graph (TG) for a network, TG = (T, L, M) constitutes the tile collection T placed in the network topology, the collection of links for tiled pairs in T is referred to as \(L\), and M specifies the data volume and link between the tiles in megabytes per second._  
\[T=\{t_{1},t_{2},t_{3},\ldots,t_{n}\} \tag{15}\]  
\[L=\{l_{i,j}=(t_{i},t_{j})\in Tx|(t_{i},t_{j})\in T,i\
eq j\} \tag{16}\]  
\[M:L\to M(t_{i},t_{j})=M_{ij} \tag{17}\]  
**Definition 3**.: \(NAG=(R,C):\) _The NoC Architecture Graph corresponds to the routing path \((c_{i,j})\in C)\) between any router pairs \((r_{i},r_{j})\) in the network. The intermediate links traversing the path \((r_{i},r_{j})\) are termed hop counts (Hops) from the router \(r_{i}\) to \(r_{j}\). Data are transferred or received to and from the cores through the connected routers, and the channel \(c_{i,j}\) acts as the physical link for data transfer between the cores. The routing channel has limited bandwidth \((B_{i,j})\) between the nodes._
# An Improved PSO Algorithm and Its Application in GNSS Ambiguity Resolution
## 3 Experiments and Result Analysis
### Performance Analysis of IPSO-AR
#### 3.2.2 Scheme #2 Experiments  
In Scheme #2, the selected DD ambiguity float resolutions are obtained from the smoothed results of observation data with five epochs. The corresponding ADOP value varies from 0.1 to 0.5. Compared with that obtained under Scheme #1, the precision obtained under Scheme #2 is relatively low. Similar to that in Scheme #1, we conducted the IPSO/SPSO-AR experiment and obtained the average efficiency and correct rate of the algorithms. The results are presented in Figures 7 and 8.  
Figure 6: Correct rates of IPSO/SPSO-AR under various DD ambiguity dimensions in Scheme #1.  
Figure 7: Average number of iterations and search time of IPSO/SPSO-AR under various DD ambiguity dimensions in Scheme #2.  
From Figures 7 and 8, we infer conclusions that are similar to those inferred for Scheme #1. The findings will not be described here to avoid repetition. Comparing Figure 7 with Figure 5, and Figure 8 with Figure 6 shows that under the same parameter setting and ambiguity dimensions, the average efficiency of IPSO/SPSO-AR slightly decreases in Scheme #2 relative to that in Scheme #1. The decrement in efficiency under these conditions, however, is not as drastic as that caused by the increase in dimensions. Under any parameter setting, the correct rate of SPSO-AR in Scheme #2 declines dramatically as the DD ambiguity dimension increases. When the number of dimensions is 10, SPSO-AR completely loses the ability to search for the correct integer ambiguity. Under the P1 parameter setting and high-dimensional conditions, the correct rate of IPSO-AR decreases in Scheme #1 relative to that in Scheme #2. Fortunately, under the appropriate parameter setting, the correct rate of IPSO-AR in Scheme #2 remains high and is similar to that in Scheme #1 (more than 99.9%).
# Robustness of Deep Learning Models for Vision Tasks
## 3 Adversarial Attacks and Defenses
### Adversarial Attacks
#### 3.1.3 DeepFool Attack  
Regarding the robustness of neural network models for image classification depends on the boundaries of the interclasses formed by the model. If the output for a given input of a neural network model is near the decision boundaries in a multiclassifier, an output with a small disturbance leads to misclassification. A DeepFool attack [121] calculates the distance of a sample \(x\) as an input to the closest decision boundary in the hyperplane and considers the direction of adversarial perturbation. By extending the distance between a point and a line in a 2D plane to the distance between the decision boundaries and classes on the hyperplane, as shown in Figure 19, a DeepFool attack produces an adversarial example \(x^{\prime}\) by iteratively perturbing the input \(x\) along the linearized decision boundaries between classes until a target is achieved. In the DeepFool algorithm, the input \(x\) in class \(j\) moves iteratively to the estimated closest decision boundary to generate an adversarial example \(x^{\prime}\) for misclassification.  
The DeepFool attack was applied to various neural network models, including LeNet, NIN (Network-IN-Network), and GoogLeNet, in order to evaluate its performance and compare it to leading attack methods. The experimental results indicated that DeepFool generated adversarial examples that were approximately five times smaller than the results of FGSM for the MNIST and CIFAR-10 datasets and approximately 10 times smaller for the ImageNet dataset. Furthermore, compared to the L-BFGS attack, DeepFool generated a smaller perturbation while increasing the computation speed.
# A 12 dBm B1 dB N-Path Notch Filter for Transmitter Leakage Suppression in Wideband Receiver
## 5 Measurement Results
### S-Parameters  
The measured S21 of the proposed notch filter across the RF tuning range is shown in Figure 14a. To minimize the loss of receiver channel bandwidth, the 3 dB passband bandwidth of the proposed notch filter ranges from 3.5 to 4 MHz across the RF tuning range. The details of the measured and simulation results at \(f_{\mathrm{LO}}\) = 450 MHz are provided in Figure 14b. For a QAM64 signal with a baseband bandwidth of 800 kHz, the filters achieve a rejection greater than 20 dB over the entire frequency range and zoom in through the details, with a maximum rejection exceeding 25 dB.  
The frequency corresponding to the quarter-wavelength transmission line characteristic impedance is about 450 MHz to equalize the performance over the entire frequency band. The passband differential loss at the edge of the band increases by 2 dB due to the variation in the transmission line wavelength, which a more complex LC structure can reduce. However, it also requires the use of more off-chip inductance. The insertion loss shortfall reduces from 3.8 dB to 2 dB across 300 MHz to 600 MHz via adjustable C\({}_{\mathrm{T1}}\) and C\({}_{\mathrm{T2}}\). The filter insertion loss is 3.6 to 5.4 dB at \(f_{LO}\)= 450 MHz with a 1.8 dB insertion loss shortfall. This insertion loss is acceptable for multiple SAW filters in a switched array configuration, as shown in Figure 14b.
# Longitudinal Wind Tunnel Tests of the PROSIB 19-Pax Airplane
## 2 Wind Tunnel Setup
### Data Acquisition and Control System  
The instrumentation for data acquisition and control included:  
* A National Instruments USB-6341 device to acquire analog voltage signals and move the stepper motor controlling the angle of attack;
* A desktop PC with Windows 10 connected to the aforementioned device via USB;  
\begin{table}
\begin{tabular}{l l l} \hline
**Force/Moment** & **Max. Error** & **Full-Scale Value** \\ \hline Lift & 0.03 kgf (0.06\%) & 80 kgf \\ Drag & 0.02 kgf (0.10\%) & 20 kgf \\ Pitching moment & 0.02 kgf m (0.10\%) & 20 kgf m \\ \hline \end{tabular}
\end{table}
Table 2: Strain gauge balances margin of error.  
Figure 2: Subsonic wind tunnel layout. Units in meters.  
* A LabVIEW virtual instrument, developed by the authors to perform data acquisition, data processing, and control the stepper motor.  
The virtual instrument was developed with LabVIEW for deployment in the longitudinal wind tunnel test campaign. The application established communication channels with the NI USB-6341 device to acquire the analog signals from the sensors sensible to longitudinal forces and moment, angle of attack, temperature, and dynamic pressure.  
To initialize the data acquisition process, a calibration matrix was loaded, along with parameters tailored to the aircraft model and wind tunnel characteristics. All the signals were acquired at a rate of 1000 Hz and averaged on 1000 samples. The strain gauge and Venturi signals were zeroed after the averaging since their voltage at the wind tunnel off was not zero. Thus, their measurements were taken with respect to a reference voltage acquired at the start of the operations. The tilt and temperature sensors had their internal calibration with their own reference voltage and did not need to be zeroed.  
After that, the weight components were subtracted from the measured forces and moment at different angles of attack to clear the balance measurements from the effect of the weight and obtain the net aerodynamic forces. Such weight components were preliminarily recorded by changing the pitch of the model at wind tunnel off. This operation is named tare and it was conducted for every investigated configuration.  
Standard aerodynamic coefficients have been used to normalize all aerodynamic forces. These coefficients were calculated based on reference parameters: the test section dynamic pressure, the wing mean aerodynamic chord (m.a.c.), and wing planform area as reference parameters. The Reynolds number was determined with respect to the m.a.c. yielding about 430,000. Wind tunnel corrections have been incorporated in the LabVIEW virtual instrument under the procedure outlined in Appendix A.
# A Cross-Platform Personalized Recommender System for Connecting E-Commerce and Social Network
## 2 System Model
### System Model of the Cross-Platform Recommender System (CPRec)  
We concentrate on an idealized cross-platform process in which an e-commerce company or a retailer \(R\) wants to recommend suitable commodities to a social network user \(u\). The first thing that the retailer \(R\) should do is to understand what the user \(u\) prefers. To give an example, if \(R\) recommends a basketball to a user who enjoys soccer, this user will not be satisfied with the recommendation. With our recommender system, there exists an opportunity for retailers to learn about a social network user's interests, supposing that our system has the necessary permissions to get the social network user's information. \(R\) could maintain a user's profiles with his/her existing information from social networks though our profiling model. The next question is how to make recommendations. Retailers naturally understand that users are more likely to purchase those commodities that most closely match their interests. Figure 2 depicts an overview of our CPRec. The first part is what we call the original data collection period, in which data for \"potential consumer profiling\" and \"commodity profiling\" would be collected. The original data used in user profiling include three types: historical microblogs, a user's tags and information about the user's followees (the people whom that user follows). Information about a commodity (the commodity name, commodity description and buyers' information) would be collected analogously. The second part refers to the profiling process whereby the data on the user and commodity will be analyzed and used to obtain profiles of both. From the original data, we aim to build both a stable interest profile and temporal interest profile for each user, due to the fact that a user's preferences may evolve over time, but his or her general preferences may also be relatively stable. In the third part, namely, the recommendation process, we offer three subdivisions for the CPRec, and the different subdivisions realize different functions.
# A Quantitative Study of Axial Performance of Rockbolts with an Elastic-Debonding Model
## 1 Introduction
Figure 2: Steel ribs along the rockbolt surface. strength, the shear force at the b/g face remains constant. Previous research indicated that after this tri-linear equation was induced, the non-linear axial performance of rockbolts can be successfully simulated [22]. However, for this tri-linear equation, a shortcoming is that the calculation process is quite complicated. A reason is that when the tri-linear equation is merged into the bolting system, the b/g face will encounter the elastic-softening-debonding phase [23]. In this phase, the full b/g face comprises three segments: the elastic segment, the softening segment, and the debonding segment. Therefore, in this phase, for the b/g face length, at least two variables, including the softening segment length and the debonding segment length, vary simultaneously. This makes the calculation process pretty complicated. To simulate the axial performance of rockbolts more conveniently, the authors recently proposed an elastic-debonding (ED) model [24]. The novelty of using this ED model is that the relative movement of the b/g face is depicted with two equations. One is the linear increasing equation. The other one is a constant-value equation. Based on the ED model, the loading process of rockbolts only comprises three phases: the elastic phase, the ED phase, and the debonding phase. Compared with the traditional tri-linear model, the difference of the ED model is that it removed the softening behaviour of the b/g face. Therefore, an advantage of the ED model is that the calculation process is much simpler. And it is more convenient to use this ED model to predict the axial performance of rockbolts. Although this ED model can depict the axial performance of rockbolts, the sensitivity of this ED model was not clear. Moreover, little research has been conducted to quantitatively study the axial performance of rockbolts based on this ED model. Therefore, this article continued previous research work and conducted a quantitative study on the axial performance of rockbolts. First, the basic calculation process of this ED model was explained. Then, the validation process of this ED model was elaborated. After that, a quantitative study was conducted based on this ED model to reveal the axial performance of rockbolts. This research provided an analytical approach for researchers to further understand the axial performance of rockbolts.
# Point Cloud Upsampling Algorithm: A Systematic Review
## 5 Algorithm Comparison and Analysis
Considering the computation, PU-Net uses the most parameters, but does not perform well in terms of various evaluation metrics. MPU achieves better performance by introducing dynamic graph convolution and multi-step upsampling, and greatly reduces parameter usage. PU-GAN introduces adversarial loss to improve performance, but also uses more parameters. PU-GCN uses a feature extraction block, Inception DenseGCN, and an upsampling module, NodeShuffle, based on graph convolution; this achieves excellent performance with the fewest parameters and the best results on CD and HD. This demonstrates the superiority of graph convolution in point cloud upsampling tasks. Although it is not as good as the models mentioned above in experimental results, SPU-Net, which is an unsupervised learning algorithm, still has merits. SPU-Net is not constrained by supervised information, does not need to obtain the label information of the dataset, and can directly obtain the characteristics of the data from the data itself and then complete the upsampling task.