esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition
## 5 Conclusions  
This paper proposes joint learning of the temporal model in an effort to improve the classification results for minority classes, as well as for the majority classes of human activity recognition tasks in smart homes. The data are preprocessed using FTWs to extract informative features. The proposed model is built upon LSTM and 1D CNN in parallel as one joint model. Extensive evaluations have been conducted in order to compare the proposed joint model with individual temporal models, i.e., LSTM, 1D CNN, and their hybridisations to show the superiority of the proposed model. The experimental results also confirm that our model has better performance for imbalanced data. The F-score results of the joint temporal model outperform 1D CNN, LSTM, and the hybrid learner by up to 4%.  
The proposed joint learning outperforms the state-of-the-arts by 4% to 10%, which can be considered a substantial margin, since building accurate HAR systems is challenging due to the large diversity of activities since different sensors record human movements and inherently imbalanced the frequency of activities. Besides that the proposed method is evaluated against state-of-the-art based on five standard benchmark datasets of activity recognition. An accepted threshold for a model to be considered to be successful is mainly based on the application scenario. Because activity recognition could be used to security perspective or elderly monitoring, the minimal 4% improvement is a substantial margin.  
Future work will investigate a newly proposed method in human activity recognition to handle imbalanced human activity problems by integrating a data level and an algorithm level. Handling imbalanced class problems from data level in addition to applying the proposed joint learning will further improve the recognition rate, particularly for extremely rare activities. One approach could be oversampling using SMOTE technique to only generate new samples from infrequent activities. Besides, weak supervision will be used to properly and correctly label the newly generated samples, since SMOTE is not sufficiently accurate in labeling new samples.  
Conceptualization, R.A.H.; methodology, R.A.H.; software, R.A.H.; validation, R.A.H., B.W., W.L.W. and L.Y.; formal analysis, R.A.H.; investigation, R.A.H.; resources, R.A.H.; data curation, public datasets; writing--original draft preparation, R.A.H.; writing--review and editing, R.A.H., B.W.; visualization, R.A.H.; supervision, B.W.; Project administration, B.W.; funding acquisition, B.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
The authors declare no conflict of interest.
## 2 Hourly Meteorological Data Analysis Based on Cubic Spline Function Method (FFT)
### The Principle and Implementation of Cubic Spline Function FFT Method
#### 2.2.1 The Principle of Cubic Spline Function
The above discussion shows that the cubic spline function has the unique solution under the boundary condition.
## 2 Methodology
### Experimental Setup  
Two distinct ROIs were considered for training the MOGP algorithms and evaluating the S1 and S2 synergy capacity to map VWC over an irrigated area of winter wheat crop. Table 1 shows the geographic boundaries, the dimension in pixels, and the area expressed in hectares for each selected ROI belonging to the winter wheat paddocks of the BVCR 2020 and 2021 crop campaigns. The S2 GP VWC maps were stacked along with S1 orbit 68 and S1 orbit 141 RVI products and the mean for the ROI was then calculated. An exhaustive detail of S1 & S2 acquisition dates for models validation corresponding to the winter wheat 2020 and 2021 crop campaigns is given in Tables 1 and 2 correspondingly. S1 & S2 datasets were used to initialize and train the MOGP models. Aiming to completely reconstruct the VWC cycles over winter wheat cropland, we created artificial S2 GP VWC data gaps (latentS2 GP VWC data). Seeking to explore the S1-derived RVI capability to reconstruct S2-based VWC maps at distinct acquisition geometries, the MOGP performance was subsequently assessed given models with two and three input channels. Three tests for each ROI were conducted analyzing the following MOGP model input configurations: (i) S2 & S1 orbit 68 (2-channel model), (ii) S2 & S1 orbit 141 (2-channel model), and (iii) (i) S2 & S1 orbits 68 and 141 (3-channel model). Several goodness-of-fit metrics were evaluated to select the best MOGP model: the mean absolute error (_MAE_, Equation (13)), the root mean square error (_RMSE_, Equation (15)), the mean absolute percentage error (_MAPE_, Equation (14)), the normalized root mean square error (_NRMSE_, Equation (16)), and the training time were recorded. Below we display the formulation used for _MAE_, _MAPE_, _RMSE_ and _NRMSE_ calculations:  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}|y_{i}-\hat{y_{i}}| \tag{13}\]  
\[MAPE=\frac{1}{N}\sum_{i=1}^{N}\frac{|y_{i}-\hat{y_{i}}|}{y_{i}} \tag{14}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y_{i}})^{2}} \tag{15}\]  
\[NRMSE=\frac{RMSE}{(y_{max}-y_{min})} \tag{16}\]  
where \(\{y_{i}\}_{i=1}^{N}\) are the S2 GP VWC latent observations used for model assessing, \(\{\hat{y}_{i}\}_{i=1}^{N}\) are the VWC retrieved values based on S1 RVI data, \((y_{max}-y_{min})\) is the S2 GP VWC data range and \(\overline{y}\) is the mean of the VWC S2-derived values. To investigate the correlation between each channel of the S1 & S2 dataset, we also plotted the cross-correlation matrix for each evaluated MOGP model. Finally, the best MOGP model was applied to winter wheat 2020 and 2021 sites (see Figure 1). A MOGP model was iteratively trained per pixel using the S1 & S2 three-channel dataset. Each S1 & S2 MOGP VWC reconstructed subset was compared against the latent S2 GP VWC by calculating the coefficient of determination (R\({}^{2}\)) and the linear regression.
# Degradation of Trace Organic Contaminants by a Membrane Distillation--Enzymatic Bioreactor
## 3 Results and Discussion
### Impact of Mediator Addition on TrOC Degradation
#### 3.3.1 Comparison of Redox-Mediators  
To date, the impact of redox-mediator type on the improvement of TrOC degradation has been assessed mainly in small scale and batch tests [34; 41; 42]. For instance, [PERSON] et al. [34] investigated the performance of seven different redox-mediators including SA, HBT and VA for the degradation of four resistant TroCs, namely atrazine, naproxen, oxybenzene and pentachlorophenol in 10 mL batch reactors. They achieved significant improvement (40-90%) at a concentration of 1 mM. [PERSON] et al. [18] achieved enhanced (10-90%) removal of TroCs in UF-EMBR using SA and HBT. However, this is the first study investigating the efficacy of SA, VA and HBT for enhanced degradation of a broad spectrum of TroCs by an MD-EMBR.  
All the tested redox-mediators enhanced the degradation of TroCs. However, the best overall performance was shown by VA (Figure 3). In line with the findings of [PERSON] et al. [37], degradation of the phenolic TroCs that were already highly degraded by lacase (Figure 2) remained almost the same after the addition of redox-mediators. For the remaining phenolic TroCs, VA (at 0.5 mM), compared to HBT and SA achieved better removal for two compounds, namely salicylic acid (80%) and pentachlorophenol (90%). Both VA and SA achieved above 95% degradation of enterolactone, which compares favorably with 45-70% degradation achieved in absence of mediators (Figure 3).  
Of the 17 non-phenolic compounds, degradation of four compounds viz metronidazoleazole, benzophenone, amitriptyline and octocrylene, was at least 90%, regardless of the mediator type (Figure 3). For the remaining compounds, VA (at 0.5 mM) achieved better degradation for 10 compounds compared to SA and HBT. SA (at 0.5 mM) performed the best for the degradation of two compounds, namely naproxen and primidone. It is well-known that the herbicide atrazine is resistant  
Figure 3: Enzymatic degradation of 30 TroCs in the presence of three mediators, namely HBT, VA and SA (separately at 0.5 mM) in the MD-EMBR. Error bars indicate the standard deviation of duplicate samples. Operating conditions of the MD-EMBR are given in the caption of Figure 2.  
to increase catalyzed degradation [18]. Compared to other redox-mediators, HBT was particularly efficient (>99%) for the degradation of atrazine. Although a superior ability of VA compared to other mediators for the degradation of non-phenolic TrOCs has been reported previously in a batch enzymatic bioreactor spiked with four TrOCs [34], the effectiveness of VA for the degradation of a broad spectrum of non-phenolic TrOCs is demonstrated for the first time in this study.
# On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections
## 3 Overview of MPTCP Congestion Control Algorithms
### Olia  
OLIA [33] was introduced as an alternative to LIA to provide both load balancing and responsiveness. The following congestion window increase function of OLIA is called when a subflow \(r\) receives an ACK.  
\[\left(\frac{\frac{Wnd_{r}}{RTT_{r}^{2}}}{(\sum\limits_{r=1}^{n}\frac{Wnd_{r}} {RTT_{r}^{2}})^{2}}+\frac{\beta_{r}}{Wnd_{r}}\right)\ B\ MSS_{r} \tag{4}\]  
The congestion window is increased by summing up two terms. The first term is expected to guarantee optimal load balancing, whereas the second term is anticipated to preserve the responsiveness characteristic. The \(\beta_{r}\) parameter in the second term is specific to each subflow \(r\), and it is computed based on the quality of paths according to  
\[\beta_{r}>0,\text{ If }r\in Collected_{paths}\] \[\beta_{r}<0,\text{ If }r\in Max_{win_{paths}}\] \[\beta_{r}=0,\,\forall\,r,\text{ If all paths}\in Best_{paths}\text{ have a maximum congestion window}\]  
where \(Best_{paths}\) is a set of paths that are best for MPTCP connection (have maximum \(l_{r}^{2}/RTT_{r}\)); \(l_{r}\) is computed based on the number of bytes transmitted between last two losses), \(Max\_{win_{paths}}\) is a set of paths with a maximum congestion window, and \(Collected_{paths}\) is a set of paths in \(Best_{paths}\) but not in \(Max\_{win_{paths}}\).
## 2 RDFTA (Repairable Dynamic Fault Tree Analysis)  
This section discusses the reliability analysis of a repairable system using FTA, deduces new qualitative and quantitative RDFTA formulae on the basis of previous equations, and provides the specific process of the RDFTA reliability analysis. [PERSON] et al. [41] proposed the concept of a \"repair box\" on the basis of a Dugan dynamic fault tree for system reliability analysis. The authors of [42; 43] introduced the repair rate into the repair box and used it for the reliability analysis of mechanical and software systems, but the repair box only established a delayed FTA mechanism for the repair and maintenance time of a single component within the system and did not consider the equivalent repair rate. The RDFTA proposed in this section considers the failure rate and maintenance rate (following the index distribution) of a repairable system at the same time, which is consistent with the life cycle of a repairable system in practice.
## 5 Results and Discussion  
In this section, the proposed Attn-MAPPO algorithm was evaluated at the unsignalized intersection on an open source platform, called highway-env [32]. A total of 30 episodes of the evaluation for each contrast were executed using 30 various seeds. Two metrics were used for the performance of the algorithm, namely the collision rate and the average speed. The collision rate was defined as the ratio of the number of episodes where the collision occurred to the total number of the test episodes. The average speed was defined as the average speed of all the vehicles in all the episodes. In the scenario, a straight lane had a distance of \(L_{s}\) = 200 m, the right turn radius was 9 m, and the left turn radius was 13 m. The vehicle's policy frequency was set to 5 Hz, i.e., the CAVs took an action every 0.2 s. The speed range of receiving a reward was [8,10] m/s. For the actions, the normal acceleration and deceleration were to add or subtract 1.5 m/s from the current speed, as the desired speed and the hard were 3 m/s. The PID algorithm was used as the low-level controller to change the current speed to the expected speed. To avoid the excessive speed, the maximum speed of CAVs was set to 10 m/s. The HDVs' desired speed was set to 10 m/s. The communication range \(L_{c}\) was set to 120 m. The setting of the training process was presented as follows. The model was evaluated every 20 episodes during the training. ADAM was used as the optimizer and the learning rate was set to 8 \(\times\) 10\({}^{-5}\). The soft update weighting factor \(\tau\) was set to 0.001. The trade-off coefficient of the GAE \(\lambda\) was set to 0.95 and the discounting factor was set to 0.99.
## 4 Simulation and Analysis  
According to the previous discussion, a novel carrier loop algorithm based on MLE and KF is designed. In order to further illustrate the feasibility and performance of the proposed algorithm, simulations and real data tests are performed in this section. Monte Carlo simulations are adopted to compare the proposed algorithm with the current algorithms to make a comprehensive evaluation of the novel carrier loop algorithm. In addition, the TC-OFDM signals are broadcasted by the modified base stations, a positioning receiver and other related equipment is also utilized to achieve the proposed algorithm. Finally, we choose several points in a test building to test the positioning accuracy for the static receiver.
# Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
## 4 Simulation Results
### System Performance versus Pilot Density  
The impact of pilot density is illustrated in Figure 15 to evaluate the robustness of deep learning estimators. As the pilot density decreased, the performance of the three deep learning estimators remained unchanged with different values of SNR. Thus, we can conclude that the deep learning estimation models are robust to different pilot densities.
# The Experiments and Stability Analysis of Hypersonic Boundary Layer Transition on a Flat Plate
###### Abstract
[22] explored boundary layer transition on a cone and plate at Mach number 3.5 using the \(e^{N}\) method. Their results indicate that when \(N=10\), the theoretical results are in excellent agreement with the transition data obtained by the experiments. [PERSON] et al. [23] investigated the influence of the noise environment on Hifire-5 elliptic cone transition in the wind tunnel and found that \(N=3.5\) in the case of noise, and \(N=8\) in the quiet freestream condition. [PERSON] et al. [24] improved the \(e^{N}\) method while considering the modal transformation; the results show that by neglecting the disturbance decay between the two instability modes, the proposed strategy provides more accurate results than existing strategies. LST method has also been applied to other studies on boundary layer stability. [PERSON] et al. [25] used wind tunnel experiment and stability analysis to study the nonlinear interaction between high-frequency and low-frequency modes on porous surfaces. It was found that the suppression of near wall disturbances by porous surfaces changed the spatial distribution characteristics of fundamental resonance disturbances, broke the phase-locked relationship, and suppressed the instability of fundamental oblique waves. At the same time, the study also found that porous surfaces greatly suppressed aerodynamic heating and delayed the transition position. [PERSON] et al. [26] developed a linear stability analysis method under thermochemical nonequilibrium conditions, which extends the scope of application of linear stability. In a recent study, [PERSON] et al. [27] employed the direct simulation Monte Carlo (DSMC) method to computationally analyze the steady laminar flow over a semi-infinite flat plate. The obtained results demonstrated a favorable agreement with corresponding solutions derived from boundary layer considerations. Following this, the researchers conducted a comparative examination of the linear stability characteristics between the DSMC-generated basic flow and those derived from classical Navier-Stokes-based profiles. The findings revealed a slight increase in stability for flows obtained through the DSMC method when compared to their Navier-Stokes counterparts. In this study, experimental investigations were conducted to examine hypersonic boundary layer transition on a flat plate, employing infrared (IR) thermography and PCB technology. Additionally, linear stability analysis was employed to calculate the growth rate of the second-mode instability in the boundary layer across various Reynolds numbers. The primary focus of this paper centers on characterizing the transition position observed in the experiments, along with analyzing the growth rate and amplitude of disturbances within the hypersonic boundary layer. Furthermore, the determination of the \(N\)-factor at the transition location of the flat plate in the NHW hypersonic wind tunnel was performed using the \(e^{N}\) method.
## 3 Results and Discussion
### Comparative LCA of Hydrogen Production: Biogas Reforming vs. Agricultural Residue  
Table 5 presents a comparative LCA of hydrogen production processes, demonstrating that agricultural residue gasification outperforms biogas reforming in nearly all environmental impact categories.  
Figure 6: Environmental impacts for 1 kg of H\({}_{2}\) production by the gasification process.
# Chemical, Nutritional and Antioxidant Characteristics of Different Food Seeds
## 3 Results and Discussion
### FAs Composition
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Fathy Acid** & **Sunflower** & **Reference** & **Poppy Seed** & **Reference** & **Flassed** & **Reference** & **Hempseed** & **Reference** & **Sesame** & **Reference** \\  & **Seed Oil** & **[**6**]** & **Oil** & **[**28**]** & **Oil** & **[**14**]** & **Oil** & **[**12**]** & **Seed Oil** & **[**30**]** \\ \hline C8.0 & 0.21 \(\pm\) 0.01 & nd & nd & nd & nd & nd & nd & nd & nd & nd \\ \hline C10.0 & nd & nd & nd & nd & nd & nd & nd & nd & nd \\ \hline C12.0 & nd & 0.02 & nd & nd & nd & 0.0035 & 0.001 \(\pm\) 0.01 & nd & nd & nd \\ \hline C14.0 & 0.05 \(\pm\) 0.02 & 0.09 & 0.04 \(\pm\) 0.02 & nd & 0.02 \(\pm\) 0.01 & 0.0332 & 0.017 \(\pm\) 0.01 & nd & 0.01 \(\pm\) 0.01 & 0.04 \\ \hline C14.1 & nd & nd & nd & nd & nd & nd & nd & nd & nd \\ \hline C16.0 & 3.94 \(\pm\) 0.02 & 6.2 & 7.79 \(\pm\) 0.07 & 8.99 & 2.51 \(\pm\) 0.07 & 6.12 & 3.17 \(\pm\) 0.05 & 5.62 & 6.57 \(\pm\) 0.04 & 14.2 \\ \hline C16.1(n-7) & 0.90 \(\pm\) 0.01 & 0.12 & 1.47 \(\pm\) 0.02 & nd & 0.41 \(\pm\) 0.01 & 0.03 & 0.55 \(\pm\) 0.02 & 0.31 & 0.91 \(\pm\) 0.01 & 0.31 \\ \hline C18.0 & 2.17 \(\pm\) 0.01 & 2.8 & 1.59 \(\pm\) 0.08 & 2.15 & 1.93 \(\pm\) 0.06 & 4.11 & 1.49 \(\pm\) 0.03 & 2.68 & 3.89 \(\pm\) 0.02 & 7.35 \\ \hline C18.1 (cis+trans) (n-9) & 26.0 \(\pm\) 0.50 & 28.0 & 8.34 \(\pm\) 0.09 & 13.7 & 5.28 \(\pm\) 0.09 & 15.4 & 5.75 \(\pm\) 0.08 & 11.90 & 21.5 \(\pm\) 0.60 & 34.4 \\ \hline C18.2(cis+trans) (n-6) & 61.0 \(\pm\) 0.70 & 62.2 & 67.2 \(\pm\) 0.80 & 74.1 & 11.6 \(\pm\) 0.20 & 13.6 & 55.9 \(\pm\) 0.30 & 55.1 & 60.2 \(\pm\) 0.50 & 40.8 \\ \hline C18.3 (n-6) & 0.20 \(\pm\) 0.02 & nd & 0.04 \(\pm\) 0.01 & nd & 0.005 \(\pm\) 0.01 & 0.18 & 0.58 \(\pm\) 0.02 & nd & 0.02 \(\pm\) 0.01 & nd \\ \hline C18.3 (n-3) & 0.21 \(\pm\) 0.02 & 0.16 & 0.77 \(\pm\) 0.02 & 1.0 & 73.2 \(\pm\) 0.40 & 59.4 & 26.5 \(\pm\) 0.30 & 16.7 & 0.91 \(\pm\) 0.02 & nd \\ \hline C20.0 & 0.29 \(\pm\) 0.03 & 0.21 & 0.17 \(\pm\) 0.05 & nd & 0.11 \(\pm\) 0.01 & 0.14 & 0.63 \(\pm\) 0.03 & 2.50 & 0.71 \(\pm\) 0.06 & 1.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fatty acids (FAs), saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs) and FUFAs/SFAs ratio in the analyzed oilseed samples.
# Classification of Malicious URLs by CNN Model Based on Genetic Algorithm
## 2 Materials and Methods
### Feature Extraction
#### 2.2.3 Fully Connected Layer  
In CNN, after the structure of several convolutional layers and pooling layers, one or more fully connected layers are connected. Each neuron in the fully connected layer is fully connected to all neurons in the previous layer. The output can be expressed by Formula (13).  
\[f(x)=W\times x+b \tag{13}\]  
In Formula (13), \(x\) is the input of the fully connected layer, \(W\) is the weighting factor, and \(b\) is the bias. The activation function of each neuron in the fully connected layer generally uses the ReLU function. The output value of the last fully connected layer is passed to the output layer, and the softmax function can be used for classification. In order to avoid training overfitting, dropout technology is usually used in the fully connected layer. Through this technology, some hidden layer nodes fail. These nodes do not participate in the propagation process of CNN, which reduces the complexity of mutual adaptation between neurons and enables neuron learning to obtain more robust features.
# Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
## 5 Measured and Predicted Results
### Comparison of Antennas  
The properties of the dual-band antenna (Ant#2) and the wideband antenna (Ant#3) proposed in this paper are summarized, together with the LSA antenna (Ant#1), in Table 3. The efficiency of Ant#3 is higher than that of Ant#2 in both the mid and high bands. The gain variations of Ant#3 are 1.3 and 1.4 dB in the mid and high bands, respectively, whereas the variations are 2 and 1.9 dB, respectively, for Ant#2. The radiation patterns in the mid band are nearly omnidirectional for both antennas (Ant#2 and Ant#3). However, in the high band, Ant#2 had nearly omnidirectional radiation patterns with two nulls in the x-y plane radiation patterns. On the other hand, Ant#3 had a nearly omnidirectional pattern in the high band without any nulls.  
Figure 19: Measured and predicted normalized radiation patterns of the wideband antenna (Ant#3) at 1.8, 2.6, and 3.6 GHz.  
A comparison of the fabricated antenna (Ant#3) and the reference antennas is provided in Table 4 in terms of performance and the area occupied on the substrate to verify the suitability of the proposed antennas for hand-held devices. The comparison in Table 4 shows that Ant#3 has high efficiency and a large bandwidth than most of the reference antennas to cover the mid and high bands while occupying a smaller area.