esa-sceva/satcom-chunk-collection huggingface dataset:
Therefore, a combined model called GRU-LR-TCN is proposed to achieve more accurate PM\({}_{2.5}\) concentration prediction under complex time-varying conditions in cities in this paper. Based on the original TCN, an improved version of the TCN, known as LR-TCN, is introduced to strengthen the feature extraction ability of the TCN. The enhanced feature extraction of LR-TCN is then combined with the time series prediction capabilities of GRU. In contrast to other studies that aim to enhance accuracy by optimizing parameters or increasing model complexity, this model emphasizes leveraging the strengths of different models to reduce complexity, shortening training time, and boosting prediction accuracy. Compared with previous work on PM\({}_{2.5}\) concentration prediction, the method proposed in this paper has the following contributions. * In this paper, LR-TCN is built upon the foundation of the TCN, and LR-TCN can predict future PM\({}_{2.5}\) concentrations. * Based on LR-TCN proposed in this paper, a combined prediction model is established by combining GRU and LR-TCN, and the outputs of the GRU prediction model and the LR-TCN prediction model are weighted and fused according to the inverse root mean square error ratio to realize the short-term prediction of PM\({}_{2.5}\) concentration. * Comparison experiments with other models reveal that the GRU-LR-TCN prediction model demonstrates better prediction performance and generalization ability, helping to improve air quality and protect public health. The remainder of the paper is organized as follows: Section 2 cites some recent work using TCN-based prediction. A brief description of the TCN principle, the GRU principle, and the proposed method is given in Section 3. The dataset, hyperparameter settings, and experimental results are discussed in Section 4. Finally, the experiments and the proposed method are summarized with an outlook in Section 5.
## 2 Deep Neural Networks for Inverse Kinematics
### Supervised Learning
#### 2.1.3 Recurrent Neural Network  
Recurrent neural networks (RNN) are DNN models that are capable of managing sequential data [53]. This makes them well suited for processing time-series. In this case, inputs and outputs are not independent, but can affect the way new data is processed. RNNs possess a recurrent unit that acts as a form of memory, allowing the network to learn from past inputs. Three main types of RNN units can be selected: Gated Recurrent Units (GRU), Long-Short Term Memory (LSTM) and Bilateral Long-Short Term Memory (BiLSTM). As noted previously, multiple studies prove RNNs to be more suitable than other options for the IK problem [38; 50].  
In [54], a RNN is used to solve the IK problem for a 6-DOF manipulator. This network is then compared to the approach followed in [55], where a Backpropagation Neural Network (BPNN) with particle swarm optimization (PSO) is used. The RNN achieves remarkable results and is proven to outperform the BPNN, even when this one uses optimization. An LSTM is used in [56] to solve IK for a 5-DOF manipulator. This type of RNN has the ability to remember long-term information and avoid the problem of gradient vanishing or explosion. The results prove it has high efficiency and satisfies the demands of real-time computing.  
To reduce the complexity of the problem for high-DOF manipulators, ref. [57] builds several network architectures taking into account the dependencies between the different joints. The RNN model proves useful for this task, as it takes the coordinate of the target point as the initial state and outputs one joint space value based on the previous joint space positions at each time step. In fact, it achieves better results than an MLP and Hierarchical Neural Networks. More examples of RNNs for the IK problem can be seen in [58; 59; 60].
###### Abstract  
The synthesis and characterization of a set of iron(III) complexes, viz. the mononuclear [Fe(L)\({}_{3}\)] **(1)** and [NHEt\({}_{3}\)][Fe(L)\({}_{2}\)(Cl)\({}_{2}\)] **(2)**, the dinuclear methoxido-bridged [Fe(L)\({}_{2}\)(\(\mu\)-OMe)]\({}_{2}\).DMF. 1.5 MeOH **(3)**, and the heteronuclear Fe(III)/Na(I) two-dimensional coordination polymer [Fe(N\({}_{3}\))\({}_{(}\)i-L)\({}_{2}\) (\(\mu\)-O)\({}_{1/2}\)(Na)(\(\mu\)-H\({}_{2}\)O)\({}_{1/2}\)]\({}_{2}\) In **(4)**, are reported. Reactions of 3-amino-2-pyrazinecarboxylic acid (HL) with iron(III) chloride under different reaction conditions were studied, and the obtained compounds were characterized by elemental analysis, Fourier Transform Infrared (FT-IR) spectroscopy, and X-ray single-crystal diffraction. Compound **1** is a neutral mononuclear complex, whereas **2** is mono-anionic with its charge being neutralized by triethylammonium cation. Compounds **3** and **4** display a di-methoxido-bridged dinuclear complex and a two-dimensional heterometallic Fe(III)/Na(I) polynuclear coordination polymer, respectively. Compounds **3** and **4** are the first examples of methoxido- and oxido-bridged iron(III) complexes, respectively, with 3-amino-2-pyrazinecarboxylate ligands. The electrochemical study of these compounds reveals a facile single-electron reversible Fe(III)-to-Fe(II) reduction at a positive potential of 0.08V vs. saturated calomel electrode (SCE), which is in line with their ability to act as efficient oxidants and heterogeneous catalysts for the solvent-free microwave-assisted peroxididative oxidation (with _tert_-butyl hydroperoxide) of cyclohexanol to cyclohexanone (almost quantitative yields after 1 h). Moreover, the catalysts are easily recovered and reused for five consecutive cycles, maintaining a high activity and selectivity.  
Iron(III) complex; Crystal structure; Electrochemistry; Catalysis; Oxidation reaction
## 3 Proposed Design
### Application Traffic Patterns Identification
#### 3.1.3 Electroencephalography (EEG)  
EEG signals represent the brain's electrical activity. Currently, ambulatory EEG (AEEG) recordings are valuable in the diagnosis of epilepsy and monitoring patient response to therapy. To obtain the EEG information in the normal routine wireless ECG sensor is very useful.  
Sampling rate is 250 Hz and sampling resolution is 16 bits; using Equations (1)-(3), following values are computed from the EEG data: SIT = 1/250 = 4 ms; MPDS = 250 \(\times\) 16 = 4 Kbps = 500 bytes/s; DDNS after 4 ms = (500 \(\times\) 4)/1000 = 2 bytes; DNS after 4 ms including minimum header = 2 + 9 + 6 = 17 bytes = 136 bits; Total data rate required by ECG packet = 250 \(\times\) 136 = 34 Kbps.
## 3 Results and Discussion
### Three-Point Bending Properties
This indicates that the fractal configuration of BS-6 is better and its internal structure is more complex, resulting in better material properties, especially in terms of bending strength. This demonstrates that the bending strength can be improved effectively by increasing the number of polygonal structures inside the thin tube. The bending strengths of BS-4, BS-6, and BS-7 are similar, whereas the bending strength of BS-4 is the highest, 4.2% and 5% higher than that of BS-6 and BS-7, respectively. This indicates that the bending strength of the two quadrangle structures in BS-4 is better than that of the two hexagon structures in BS-6 and the one hexagon structure in BS-7. Thus, BS-4 has better three-point bending characteristics. Table 4 presents a comparison of the bending data between experiment and simulation. The error between the simulated value of the maximum bending load of BS-7 and the actual value is within 6.6%; that is, the simulation results are in good agreement with the experimental results. BS-2 has the lowest maximum bending load, and LWN-B is 3684.3 N and 35.62 N/g, respectively, while BS-4 has the highest maximum bending load, and LWN-B is 5339.8 N and 51.63 N/g, respectively. The difference between the maximum bending load and LWN-B value of BS-3 and BS-5 is very small, 5.3% and 8.05% higher than that of BS-1, respectively. The internal structures of BS-3 and BS-5 are a single quadrilateral and a single pentagon, respectively, which indicates that the LWN-B value is improved by adding a polygon structure inside the thin tube. The LWN-B value of BS-4 is 33.8% higher than that of BS-3, and the internal structure of BS-4 is two quadrangles, while that of BS-3 is one quadrangle. The LWN-B value of BS-5 is 25.1% higher than that of BS-6, and the internal structure of BS-5 is two hexagons, while that of BS-3 is one hexagon. This illustrates that increasing the number  
Figure 7: Three-point bending finite element model of BS-7. of polygons inside the thin tube effectively improves the three-point bending performance. The LWN-B values of BS-4, BS-6, and BS-7 are relatively close, with the highest being BS-4 at 51.63 N/g, which is 4.2% and 5% higher than BS-6 and BS-7, respectively. Thus, the design of the two quadrangle structures in BS-4 outperforms that of the two hexagon structures in BS-6 and the one quadrangle and one hexagon structure in BS-7.
## 4 Discussion
### Limitations and Strengths  
The strengths of this scoping review were the following: (i) the inclusion of three generic databases (Scopus, PubMed, Web of Science), increasing precision and reducing possible biases in the results obtained; (ii) the methodological processes followed by the PRISMA ScR, TESTEX, RoB2 and GRADE scales; and (iii) the inclusion of the analysis of the acute effects of muscle strength training in amateur boxers. On the other hand, the main limitations of this review were (i) not having experimental studies that analyzed the effects of muscle strength training in boxers, (ii) the fact that most studies obtained a moderately low rating in TESTEX, (iii) the diversity of instruments and variables observed, such as the different ages of the groups analyzed and (iv) having only one study with female  
Figure 6: Acute effects of strength interventions on punches’ performance. PAPE: post-activation performance enhancement. RM: repetition maximum. MVC: maximum voluntary contraction.  
boxers. It is necessary that future studies carry out research with experimental designs on muscle strength training in boxers, since it helps to improve the quality of the studies, helping readers to understand more clearly how the data were obtained and analyzed, improving the interpretation of the results. Additionally, we encourage researchers to perform interventions in female boxers to improve the understanding of their physical adaptations and optimize sports training processes.
## 1 Introduction
We then proposed a subcarrier transmission power assignment (STPA) [18; 19], wherein the transmitter provides two levels of power density for each subcarrier: high and low levels. The receiver then applies CMA to high-level subcarriers and PI to low-level subcarriers so that these algorithms precisely work to suppress interference. It is regarded that the proposed STPA-BAA exploits power spectral densities as a priori information that can be implicitly shared between the transmitter and the receiver. The secondary receiver can successfully suppress the inter-system interference from the primary transmitter when SIR is equal to about 0 dB. Furthermore, STPA can improve an operational SIR for the primary system since low-level subcarriers of the secondary transmitter decreases the interference power to the primary receiver. Additionally, forward error correction (FEC) functions of the primary receiver also can compensate the strong interference influenced by high-level subcarriers. The fundamental effectiveness of the proposal was clarified through computer simulations under the scenario where two orthogonal frequency division multiplexing (OFDM) systems share the superposed spectrum [19]. Our approach can realize spectral efficiency improvement without any a priori information or sensing mechanisms. Its comprehensive comparison is summarized in Table 1. However, the practical performances of STPA-BAA appeared to be limited due to the hardware specifications, e.g., quantization or dynamic range of the transceiver. To elucidate this concern, the effectiveness of the STPA-BAA proposal was reported by fabricating and testing a prototype based on filter bank multicarrier transmission systems [20]. A major contribution of this paper is to expand its significance in terms of the detailed hardware implementation and results. The rest of this paper is organized as follows. Section 2 describes the system model and our previously proposed STPA-BAA scheme. Sections 3 and 4 present prototype specification and experiment results, respectively. Finally, Section 5 concludes the paper.
## 4 Complexity Analysis  
In this section, we analyze the complexity of the proposed system and compare it with that of the conventional OFDM system. In the proposed system, there are three main blocks where the computational burden is significant. On the transmitter side, these are the BCH encoder, the CPM mapper, and the FFT block. On the receiver side, the main computational burden is due to the BCH decoder, the FFT block, and the Viterbi decoder used to decode the CPM-mapped symbols. The complexity of FFT will be the same for the proposed system and the traditional OFDM system, which is known to be \(O(Nlog\,N)\) when \(N\) is in powers of two [60].  
Traditional OFDM systems incorporate channel estimation and equalization. As will be shown in the section on simulation results, certain configurations of our proposed system give error-free performance without any equalization. Hence, the computational burden due to equalization will only be for the traditional OFDM systems, which is \(O\big{(}N^{2}\big{)}\) whether a zero-forcing (ZF) equalizer is used or a minimum mean square error (MMSE) equalizer is used [61]. It is evident that this is too high a computation burden for even moderate values of \(N\). When compared with the additional computation burden due to BCH and CPM, it can be concluded that the conventional OFDM that uses equalizers is comparable to the proposed scheme, if not worse, in terms of computational complexity. Table 1 summarizes the complexity involved with these blocks, while a detailed analysis of the complexity of BCH and CPM is given below.
## 3 Results
### \(\beta\)-Lactoglobulin Concentration Analysis after Pasteurization  
We quantified the \(\beta\)-lactoglobulin content in samples subjected to various pasteurization techniques, with the results displayed in Figure 3. No significant differences in \(\beta\)-lactoglobulin content were observed between PEF treatment and traditional pasteurization methods. PEF-treated milk samples exhibited a \(\beta\)-lactoglobulin concentration of 2.97 mg/mL, identical to that found in raw milk control samples. Milk samples treated with MF displayed a \(\beta\)-lactoglobulin content of 2.81 mg/mL, while LTLT-treated samples had a concentration of 2.84 mg/mL. HTST-treated samples revealed a \(\beta\)-lactoglobulin content of 2.89 mg/mL. However, we were unable to replicate the ultrahigh-temperature (UHT; 140 \({}^{\circ}\)C for 4 s) pasteurization technique, employed for extended shelf-life milk storage, in our laboratory setting. The effects of UHT on milk samples were described in our previous article [27].  
Figure 2: Amino acid content in milk samples treated with different pasteurization techniques: control (raw milk); PEF—pulsed electric field; MF—microfiltration; LTLT—low temperature, long time; HTST—high temperature, short time.  
a-Casein, \(\beta\)-Casein, and \(\kappa\)-Casein Concentrations Analysis after Pasteurization  
Figure 4 illustrates the quantification of \(\alpha\)-casein, \(\beta\)-casein, and \(\kappa\)-casein concentrations in milk samples subjected to various thermal pasteurization treatments: MF, LTLT and HTST processes. The casein levels in these samples were compared to those in control and PEF-treated samples. The \(\alpha\)-casein concentrations in control samples averaged 1197 mg/100 mL, comparable to the PEF treated samples at 1144 mg/100 mL. Thermal pasteurization treatments resulted in \(\alpha\)-casein content of 1386 mg/100 mL in MF-treated samples, 1563 mg/100 mL in LTLT-treated samples, and 1410 mg/100 mL in HTST-treated samples. \(\beta\)-casein levels in control samples exhibited an average of 774 mg/100 mL, akin to PEF-treated samples at 747 mg/100 mL. The thermal pasteurization techniques led to increased \(\beta\)-casein concentrations: 936 mg/100 mL in MF-treated samples, 1030 mg/100 mL in LTLT-treated samples, and 911 mg/100 mL in HTST-treated samples. The \(\kappa\)-casein concentrations in control samples were observed at an average of 401 mg/100 mL, with PEF-treated samples displaying similar values at 402 mg/100 mL. The application of thermal pasteurization methods resulted in a rise in \(\kappa\)-casein content: 456 mg/100 mL in MF-treated samples, 552 mg/100 mL in LTLT-treated samples, and 444 mg/100 mL in HTST-treated samples.
# Fingering Instability of a Gravity-Driven Thin Film Flowing Down a Vertical Tube with Wall Slippage
## 5 Conclusions  
In the present paper, we investigated the fingering instability of a coating flow down a vertical tube with dynamic contact line, and the influence of the directional slippery substrate on the flow instability was analyzed. Based on the thin film theory, the governing equation for the thin film flow was derived, then the time responses for the axisymmetry model for different parameters were simulated, including the precursor thicknesses, the axial slippery length and the radius of the tube. Linear stability analysis was carried out based on the traveling wave solutions. Finally, three dimensional numerical simulations were given by the finite element method with the help of the open-source software Freefem++. Numerical simulations showed good agreement with linear stability analysis.  
From the numerical simulations and the LSA, some conclusions were obtained: The thickness of the precursor layer induced a smaller capillary bump and causes the contact line to travel faster, while the radius of the tube promoted the surface instability. The slippery effect in the axial direction and the azimuthal direction acted differently thin film flows with dynamic contact lines. The axial slippery effect always played a stabilizing role while the azimuthal effect played two roles. When the wavenumber was smaller than a transfer wavenumber \(q_{t}\), the azimuthal effect slightly enhanced the flow instability, and when the wavenumber exceeded the critical value \(q_{t}\), the azimuthal effect impeded the flow instability.  
Conceptualization, C.M. and B.L.; methodology, C.M.; software, S.H.; validation, G.D.; formal analysis, C.M. and S.H.; data curation, G.D.; writing--original draft preparation,C.M.; writing--review and editing, C.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded the National Natural Science Foundation of China (Grant Nos. 11702162, 11972375 and 51975341) and Key Technology R & D Program of Shandong Province (Grant Nos. 2019 GHZ001 and 2019 RKB01041).  
[PERSON] wants to acknowledge the help from [PERSON] from Cambridge University.  
The authors declare that they have no conflict of interest.  
Figure 14: Time evolution of a single-mode perturbed fluid film down a vertical tube for different \(R\) (\(\beta_{\theta}=\beta_{z}=0.05\)).
## 4 Radiation Metrics
### Steady-State Temperature (ssT)  
The ssT is an important measure, especially in scenarios involving medium- to high-power radiation exposures at mmWave frequencies [16]. In such cases, ssT is considered to be preferable over SAR and power density, particularly when evaluating devices positioned close to the human body, such as sensors at mmWave frequencies. ssT offers valuable insights into the potential thermal effects on biological tissues, making it a relevant metric for assessing the safety and health implications of mmWave radiation exposure.
## 4 Experiments
### Experimental Metrics  
The experimental metrics include Accuracy, AUC and F1. The CTR task is a binary classification task, which represents the proportion of the total number of correct model predictions, and the accuracy is calculated as:  
\[Accuracy=\frac{TP+TN}{N} \tag{9}\]  
\(TP,TN\) denotes the number of positive samples predicted correctly and the number of negative samples predicted correctly, respectively, and \(N\) represents the total number of samples.  
AUC is a measure of the ranking performance of a recommender system. The ROC curve is a curve with FPR (False Positive Rate) as the horizontal axis and TPR (True Positive Rate) as the vertical axis, and AUC is the area under the ROC curve. The AUC is calculated as follows:  
\[AUC=\frac{\sum I(p_{positive},p_{negative})}{P\times N} \tag{10}\]  
\[I(p_{positive},p_{negative})=\begin{cases}1,&p_{positive}>p_{negative}\\ 0.5,&p_{positive}=p_{negative}\\ 0,&p_{positive}<p_{negative}\end{cases} \tag{11}\]  
\(P\) is the number of positive samples, \(N\) is the number of negative samples, \(p_{positive}\) is the positive sample prediction score, and \(p_{negative}\) is the negative sample prediction score. The F1 score is a composite of accuracy and recall and can be calculated by the following formula:  
\[F1=2*\frac{Precision\times Recall}{Precision+Recall} \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Users** & **Items** & \begin{tabular}{c} **Average** \\ **Click Sequence** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Click Categories** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Click Time Span** \\ \end{tabular} \\ \hline Amazon & 19,240 & 63,001 & 8.780 & 8.780 & 1.050 \\ Taobao & 104,693 & 1,592,919 & 102.170 & 24.385 & 0.009 \\ Douban & 52,539 & 140,502 & 6.321 & 2.726 & 0.418 \\ Yelp & 1,542,656 & 209,393 & 3.956 & 3.025 & 0.324 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of users and items in the four datasets. Average click sequence indicates the average number of items clicked by users; average click categories indicates the average number of items clicked by users; average click time span indicates the average time span clicked by users.  
Precision and recall denote accuracy and recall, respectively. The F1 score is a combination of precision and recall, while the AUC score is a combination of TPR and FPR.