esa-sceva/satcom-chunk-collection huggingface dataset:
# Coordination of Congestion and Awareness Control in Vehicular Networks
###### Abstract  
Vehicular networks need to guarantee the communication reliability levels necessary to satisfy the application requirements, while ensuring a stable network operation even under dense deployments. To this aim, congestion and awareness control protocols dynamically adapt the same communication parameters based on context conditions. If the two protocols operate independently, negative interactions or conflicts can arise. This situation can occur if for example congestion control requires decreasing the transmission power to reduce the channel load, but this reduction negatively influences the vehicles' awareness range. To address these interactions or conflicts, this paper proposes and evaluates a methodology to coordinate congestion and awareness control protocols. A key advantage of the proposed methodology is that it does not require the integration of the interacting protocols, nor does it require changing their original design. The obtained results demonstrate the effectiveness of the proposed coordination methodology. In addition, the proposed methodology can be extended to the coordination of multiple protocols operating over the same communication parameters. This is here demonstrated considering the coordination of congestion, awareness and topology control protocols.  
e +
Footnote †: journal: Accepted: 15 November 2018; Published: 20 November 2018  
_Article_  
Coordination of Congestion and Awareness Control  
in Vehicular Networks
# Variable Rate Point Cloud Attribute Compression with Non-Local Attention Optimization
###### Abstract  
Point clouds are widely used as representations of 3D objects and scenes in a number of applications, including virtual and mixed reality, autonomous driving, antiques reconstruction. To reduce the cost for transmitting and storing such data, this paper proposes an end-to-end learning-based point cloud attribute compression (PCAC) approach. The proposed network adopts a sparse convolution-based variational autoencoder (VAE) structure to compress the color attribute of point clouds. Considering the difficulty of stacked convolution operations in capturing long range dependencies, the attention mechanism is incorporated in which a non-local attention module is developed to capture the local and global correlations in both spatial and channel dimensions. Towards the practical application, an additional modulation network is offered to achieve the variable rate compression purpose in a single network, avoiding the memory cost of storing multiple networks for multiple bitrates. Our proposed method achieves state-of-the-art compression performance compared to other existing learning-based methods and further reduces the gap with the latest MPEG G-PCC reference software TMC13 version 14.  
point cloud; compression; non-local attention mechanism; variable rate model; sparse convolution +
Footnote †: journal: Applied  
13 August 2022  
16 August 2022  
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**Ccepted:** 2022 by the authors. Liconsee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction
## 3 Experiments
### Model Comparison  
In this paper, we use some state-of-the-art models to test the superiority of the proposed model (TL-DSTP-DANN). In these comparison experiments, the hyperparameters of the compared models are set as following principle: For the models that provided the hyperparameters, we use the original hyperparameters directly. Otherwise, we adjust the hyperparameters to achieve the best performance. The compared models are introduced as follows.  
TL-LSTM: LSTM is used to learn from the long-term dependence of PM2.5, and transfer learning is applied to transfer the features of the source domain to the target domain.  
TL-BiLSTM [25]: the TL-BiLSTM model is proposed to predict the air quality of new stations lacking data. This approach uses data from existing sites to pre-train the Stacked BiLSTM model. Then, freeze the first few hidden layers of the basic model, and fine-tune the remaining hidden layers using the data of the newly-built sites.  
TL-DSTP [24]: This method uses an improved DSTP model to transfer source domain site data to assist in predicting PM2.5 concentrations at target domain sites.  
The results of the proposed model compared to the baselines are shown in Table 2. As can be seen from the results in Table 2, the combined results of TL-BiLSTM outperform TL-LSTM, indicating that the accuracy of transfer learning experiments using the Stacked BiLSTM model is better than that of LSTM. Meanwhile, the combined results of the TL-DSTP model outperform the TL-BiLSTM, indicating that transfer learning using the improved DSTP model is better than the BiLSTM. Compared with the model TL-DSTP, the proposed TL-DSTP-DANN model reduces 13.09%, 8.90%, and 13.04% in MAE, RMSE, and MAPE, respectively. The results show that the proposed model have good prediction performance for the PM2.5 concentrations at target domain sites with less data.  
To verify the performance of the proposed method, the true and predicted values of PM2.5 concentrations at the Tianjin site predicted by the four models from 23 April 2015 to 30 April 2015 are shown in Figure 4. As can be seen from the figure, compared with the TL-DSTP and TL-DSTP-DANN models, the predicted values of TL-LSTM and TL-BiLSTM models are quite different from the true values. At the same time, the error between the predicted value and the true value of the TL-DSTP-DANN model is smaller than that of TL-DSTP.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **MAE** & **RMSE** & **MAPE** \\ \hline TL-LSTM & 21.80 & 26.26 & 0.43 \\ TL-BiLSTM & 17.40 & 20.94 & 0.33 \\ TL-DSTP & 13.52 & 17.53 & 0.23 \\ TL-DSTP-DANN & 11.75 & 15.97 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of comparison between the proposed method and baselines.
## 4 Experiment
### Analysis of Actual Flow and Theoretical Flow Error  
A laboratory experiment was used to observe the real-time flow of the system conveniently. The flow information measured by the Hall flow sensor was displayed through the LCD screen in real time. The target flow rates under different duty cycles can be obtained by using the relationship between the duty cycle of the PWM square wave signal and nozzle flow rate. Table 2 is a comparison of the actual flow and the target flow of a single nozzle under different duty cycles.  
The actual flow measurement data measured by the Hall flow sensor were fed back to the controller chip. The PID algorithm adjusts the system flow according to the error between the target flow and the actual flow. It can be seen from Table 2 that the system flow is regulated by the duty cycle of PWM square wave signal. The actual flow changes with the change of duty cycle and is stable near the target flow value. Additionally, the average deviation of flow regulation is 2.16%, which indicates that the system can adjust the flow well.
# Gait Phase Identification and Damping Control for Knee Orthosis Using Time Series Forest Classifier
## 4 Machine Learning Algorithm for Gait Phase Identification: Training, Evaluation, and Results  
For this application, four different machine learning algorithms are tested with the system specifications given in Table 6. The time series forest classifier offers interpretability through its decision-tree-based approach. ROCKET provides computational efficiency by leveraging optimized matrix operations and random convolutional kernels. LSTM-FCN  
Figure 11: Mean left knee moment associated with gait phases.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Gait Subphase** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline
**Mean Right Knee** & 0.425 & 0.656 & 0.108 & 0.130 & 0.570 & 0.596 & 0.477 \\ \hline
**Median Right Knee** & 0.423 & 0.683 & 0.106 & 0.082 & 0.587 & 0.599 & 0.477 \\ \hline
**Mean Left Knee** & 0.112 & 0.546 & 0.733 & 0.392 & 0.731 & 0.672 & 0.082 \\ \hline
**Median Left Knee** & 0.090 & 0.553 & 0.400 & 0.364 & 0.732 & 0.706 & 0.081 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean and median activation for gait phases.  
Figure 10: Mean right knee moment associated with gait phases.  
combines LSTM and fully convolutional networks to capture long-term dependencies in time series. K-nearest neighbors is valued for its simplicity, flexibility, lack of a training phase, resilience to noise, and adaptability to various time series classification tasks. Evaluating and comparing these classifiers on a case-by-case basis is important, considering dataset characteristics and problem specifics, to determine the most suitable choice for a given time series classification scenario [16; 17].
## 4 Experimental Verification
### Experimental Results and Analysis
These experimental findings validate our simulation results presented in Section 3.2.3 and are further supported by the mechanical efficiency and volumetric efficiency comparisons shown in Figures 12 and 13. The close agreement between simulated and experimental values for both mechanical efficiency (deviation < 1.6%) and volumetric efficiency (deviation < 1%) at high speeds confirms the reliability of our thermal power characteristics model for internal gear pumps across various operating conditions.
## 3 Design of Control Strategy
### Lower Control Strategy  
The final input to the vehicle due to the controller for tire force observation is the brake cylinder pressure, it is therefore necessary to convert the longitudinal force of the target tire into the target braking pressure. The tire force controller adopts feedforward and feedback control strategies as shown in Equations (37) and (38).  
\[P_{f}=\frac{\Big{(}F_{xb,ref}+F_{t}\Big{)}r}{C_{b}} \tag{37}\]  
\[P_{b}=K_{p}\Big{(}F_{xb,ref}-F_{xb}\Big{)} \tag{38}\]  
where \(P_{f}\) is the feed-forward brake-pressure; \(F_{xb,ref}\) is the target brake pressure; \(F_{t}\) is the driving force; \(r\) is the tire rolling radius; \(C_{b}\) is the brake efficiency factor; \(P_{b}\) is the feedback brake pressure; \(K_{p}\) is the feedback gain; \(F_{xb}\) is the actual brake-pressure.  
Feedforward is the corresponding relationship between braking force and braking pressure under completely ideal conditions, which can improve the response speed of the system; feedback is used to reduce errors and improve control accuracy.
# Feature-Selection-Based DDoS Attack Detection Using AI Algorithms
## 2 Related Works and Contribution
### Performance of ML/DL in DDoS Attack Detection  
In their dataset, [PERSON] et al. [9] only include TCP traffic from actual networks and include the number of connected devices per second and peak/off-peaktime indicators. In terms of accuracy, precision, and recall, the results of the comparison demonstrate that NN has the best accuracy and precision. A decision tree (DT) technique is also suggested by [PERSON] et al. [10] to be used to identify DDoS attacks in the cloud network. They divide traffic into four groups and use self-generated traffic to validate the suggested approach. Entropy and logarithm values are calculated by [PERSON] et al. [11] to identify TCP-SYN flood and ICMP flood assaults in SDN, respectively. In order to identify DDoS attacks, [PERSON] et al. [12] offer an enhanced SVM model that uses kernel principal component analysis (KPCA) and genetic algorithms (GA). In order to determine the additional costs associated with using ML for DDoS attack detection in SDN, [PERSON] et al. [13] compared the initialization times and accuracy of seven classifiers. The effectiveness of four ML approaches in the detection of DDoS attacks with and without feature selection is also compared by [PERSON] et al. [14]. The most accurate method is KNN employing wrapper-based selection; the authors trained with 6 and 12 key features.
## 2 Materials and Methods
### Characterization of Phenolic Compounds from Burdock Extracts by HPLC\(-\)Dad\(-\)It\(-\)Ms  
Polyphenol characterization was conducted using an Agilent HPLC Series 1100 (Agilent Technologies, Santa Clara, CA, USA) equipped with an autosampler and a diode array detector (DAD), coupled to an Esquire 3000 Ion Trap Mass Spectrometer (Bruker, Bremen, Germany) with an Electrospray Ionization (ESI) source.  
Separation was optimized for this study and was achieved using a ZORBAX Eclipse XDB-C18 column (150 \(\times\) 4.6 mm, 5 \(\upmu\)m particle size; Phenomenex, Torrance, CA, USA). The separation process of the phenolic compounds was optimized by HPLC with various gradient programs, column temperatures (50, 40, 30, and 20 \({}^{\circ}\)C), and injection volumes (5 and 10 \(\upmu\)L). Finally, the mobile phase consisted of 0.1% (\(v/v\)) formic acid in water (A) and 0.1% (\(v/v\)) formic acid in methanol (B) with the following gradient elution: 5% B isocratic (0-1 min), 5-25% B (1-4 min), 25% B isocratic (4-12 min), 25-31% B (12-13 min), 31-43% B (13-25 min), 43% B isocratic (25-28 min), and 43-50% B (28-32 min). The flow rate was set at 1.2 mL/min, the injection volume was 10 \(\upmu\)L, and the column temperature was maintained at 20 \({}^{\circ}\)C. Detection was performed at 280 nm for phenolic acids, 320 nm for flavonoids, and 520 nm for anthocyanins.  
Mass spectrometric analysis was carried out according to [PERSON] et al. [33] in both negative and positive ionization modes, with a mass range from \(m/z\) 50 to 1000. MS parameters included a capillary voltage of 3500 V, drying gas flow rate of 10 L/min, nebulizer pressure at 45 psi, and drying temperature set to 350 \({}^{\circ}\)C.  
The phenolic compounds were exhaustively identified by comparing their fragmentation patterns with authentic standards. Quantification was performed using calibration curves for each compound. The standards used for identification and quantification included chlorogenic acid, cynarine, 4-O-caffeoylquinic acid, 3,5-dicaffeoylquinic acid, and 1,5-dicaffeoylquinic acid. In addition, some compounds were tentatively identified using database spectra (FOODB) and literature data.
## 3 Results and Discussions
### Optimization Analysis of Cooling Channel Dimension
The selection principle is that the front bearing temperature P4, which is approximately 308 K, is the preferred design dimension reference frame. Thus, the blue region in the diagram is employed as the selection region, the channel width P1 is 7.0 mm\(\sim\)8.0 mm, and the channel height is 3.5-4.5 mm. The convenience of machining and the cost must be considered in the selection of the final channel width P1 and channel width P2. Three preferred dimension parameter combinations can be obtained by the optimization simulation, as shown in Table 6 and Figure 13. The cooling channel can be evaluated based on the actual design constraints. The lowest front bearing temperature can be obtained with the three groups of parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Combination** & **P1—Width (mm)** & **P2—Height (mm)** & **P3—Flow Rate(kg/s)** & **P4—Temperature(K)** \\ \hline
1 & 7.0099 & 3.8629 & 0.2 & 308.04 \\
2 & 7.3755 & 3.99 & 0.19854 & 308.04 \\
3 & 7.8615 & 3.8186 & 0.19827 & 308.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Optimal combination of cooling channel dimensions. Figure 12: P1–P3 response surface diagram. (**a**) the effect of channel width P1 and cooling oil flow rate P3; (**b**) the effect of channel height P2 and cooling oil flow rate P3; (**c**) the effect of channel width P1 and channel height P2. Figure 13: Optimal dimension parameter combination. This optimal design is admitted into the initial stage of design; the time and cost of trial and error can be substantially reduced, and the relative relationships among variables can be obtained from the simulation results. The design is based on a systematic method. The optimal dimensions of the front bearing cooling channel are shown in Table 7. Considering the design convenience, the first group of parameters is selected as the optimal dimension design, and the cooling channel wall temperature is lower than the original design channel (Figure 3a) by 25.9 \({}^{\circ}\)C. Use of the optimal design can reduce the wall temperature by 7.1 \({}^{\circ}\)C compared with sole use of the cooling channel simulation.
## 6 Scheduling Algorithm Performance Test Using TLS-VaD
### Hypotheses on the Performance of Scheduling Algorithms  
This section explains some hypotheses related to the performance of each scheduling algorithm, as follows:  
1. In producing minimum \(\lambda\), IRByTSA will outperform FTSA and FLSA. This is estimated because IRByTSA prioritizes transmission at higher-rank nodes. By prioritizing higher-rank nodes, data packets will arrive at the master node faster, which decreases the number of timeslots needed to send data along the passing of cycles. The decreased need for timeslots in each cycle will produce the minimum \(\lambda\) value.
2. In producing the minimum number of cycles, FLSA will outperform IRByTSA and FTSA. This can occur because FLSA gives priority to transmission at leaf nodes that will give more links to the DCFL(s) than the algorithm that prioritizes transmission at higher nodes. With more links on DCFL(s), network data will reach the master node faster. Faster arrival of data at the master node on a network that uses FLSA will be reflected in fewer cycles than a network that uses IRByTSA or FTSA.
3. In producing a minimum \(\lambda\), FTSA will not perform as well as IRByTSA, because in FTSA, the opportunity to transmit from the child node to its parent is not done in rotation, resulting in a buildup of data at certain nodes, leading to increased \(\lambda\) value generated by the algorithm.
4. In producing a minimum number of cycles, FTSA will not perform as well as FLSA because of two factors: (i) transmission priority is given to higher nodes. Algorithms that give priority to transmission at higher nodes will not be able to provide more DCFLs than algorithms that give priority to transmission at leaf nodes. Thus, what happens to IRByTSA will also happen to FTSA. (ii) The process of transmitting from a child node to its parent always starts from the first child node, not based on turns. As long as the child nodes have data, even if only one data packet, the node will be given the opportunity to transmit. This will result in data reaching the master node slowly, so the number of cycles needed to collect all data at the master node will be greater than the number of cycles in IRByTSA and FLSA.
## 3 Ultrasonic Measurement of Air Void Distribution in Early-Age Concrete  
### Sample Preparation and Early-Age Property Measurement  
Three different types of concrete samples were prepared based on the ASTM C192/C192M-16a [34] during this study. To focus on the influence of the air entrainment agent on the air void structure, the samples were prepared with the same mix design but different air entrainment agent dosages. MasterAir AE 200 type air entrainment agent (BASF Co., Florham Park, NJ, USA) was chosen in this study. The detailed mix design for three types of concrete are shown in Table 1. The volume percentage of size three-quarters (19 mm), one-half (12.5 mm), three-eighths (9.5 mm), No. 4 (4.75 mm), No. 8 (2.36 mm) and No. 16 (1.18 mm) coarse aggregate are 5%, 25%, 25%, 20%, 16% and 9% respectively. Particularly, the fine aggregates belong to the No. 8 (2.36 mm) sieve size. After mixing, the specific gravity of each sample was measured and used for the air void volume fraction calculation based on the ASTM C138 [35]. Then, the fresh concrete was placed into cylinder models (Humboldt Mfg. Co., Elgin, IL, USA) with 10.16 cm (4 in) diameter and 20.32 cm (8 in) height. After demolding at 24 h, the concrete samples were submerged into water and cured at room temperature for 28 days. Before the tests at each curing age, the samples were first air dried for 12 h to remove the extra water. Besides the ultrasonic scattering measurement, the compressive strength and electrical resistivity tests were also conducted at Day 3, 7 and 28. The compressive strength test was conducted based on the ASTM C39/C39M-16 [36] standard. The bulk electrical resistivity of the concrete sample was measured with the Rcon2 [37] equipment from Giatec Scientific Co. (Ottawa, ON, Canada). Considering the transmission ability of the ultrasonic wave, the concrete cylinder was cut with a diamond saw to obtain slices of around 2.54 cm (1 in) in height for measurement. The selection of thickness partly depended on the penetration ability of the ultrasonic source; the first two signals will not overlap with each other at the thickness of 2.54 cm.
# The Interaction of Fitness and Fatigue on Performance in Football
## 3 Data Analysis
### Evaluation of Match Physical and Tactical Outcomes  
Match physical outcomes were assessed using the external load metrics previously described. Given the individual variability related to playing position and playing style, these metrics were normalized using z-scores: z-TD, z-D14.4, z-19.8, z-25.2, z-MW. The team tactical data of the matches were extracted by a video tracking system (OPTA client system, SportVU 2.0). The OPTA system generates match statistics from every action through a combination of human annotation, computer vision, and artificial intelligence modeling. The system exhibited a strong degree of inter-operator reliability and low standardized typical errors [22]. Expert analysts selected specific tactical parameters related to offensive and defensive behaviors to describe the team's tactical approach, which were subsequently analyzed:  
* Percentage of field tilt (%FT): Field tilt is used to show the territorial dominance of the team. It measures the share of possession a team has in the game, considering only touches or passes in the attacking third. Higher values reflect greater attacking ability.
* Passes per defensive actions (PPDA): The metric is calculated by dividing the number of passes performed by the attacking team by the total number of defensive actions performed by the defending team (e.g., tackles and interceptions). Therefore, this metric counts how many passes a team allows the opponents to make before attempting to win the ball back with a defensive action. Lower PPDA values suggest higher defensive intensity.
* Percentage of territorial domination (%TDO): This metric represents the percentage of time spent in possession within the opponent's half.
* Expected Threat (xT): The value quantifies the change in goal-scoring probability before and after an action, allowing us to provide a value to the actions that lead the team toward more dangerous situations.  
Figure 1: Summary of the steps performed to calculate the \(PS_{individual}\) and \(\text{PS}_{\text{team}}\) scores.  
The external load data were normalized per playing minutes to enable easier comparisons. Only players who played at least 70 min were included to avoid the confounding influence of substitutes, who might display greater physical output due to different pacing strategies [23].