esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
### Scenarios Without Load Balancing vs. Scenarios with Load Balancing  
To fully evaluate the effectiveness of the proposed load balancing model, this section consolidates the results of the simulations conducted under two scenarios: without load balancing and with load balancing. This integrated approach highlights the performance differences and emphasizes the advantages of the proposed model in terms of latency reduction, load distribution, and overall system stability.
# Multilevel Bipolar Electroforming-Free Resistive Switching Memory Based on Silicon Oxynitride
## 1 Introduction
To meet the abovementioned characteristics that require comprehensive defect profile control, ion doping [24], thermal treatment [27], optimization of the film thickness [28] and modulation of the fabrication process [24; 29] have been investigated. In this work, we studied electroforming-free operation as well as multilevel operation of a SiO\({}_{x}\)N\({}_{y}\)-based RRAM device. Defect-rich SiO\({}_{x}\)N\({}_{y}\) is employed as the resistive switching layer that is sandwiched between a p\({}^{+}\) silicon substrate and an Au/Ni metal layer. With the combination of the Ni layer and the defect-rich SiO\({}_{x}\)N\({}_{y}\) layer, we successfully incorporate an intermediate resistance state (IRS) between the HRS and low-resistance state (LRS) and eliminate the electroforming process, which requires a high activation voltage [18]. The device exhibits bipolar electroforming-free SET/RESET operational characteristics as well as multilevel digital memory characteristics with an HRS/LRS resistance ratio over 10\({}^{3}\), which is better than that of the SiO\({}_{x}\)N\({}_{y}\)-based devices introduced in previous reports [17; 18]. In addition, the effect of current and silicon oxynitride thickness on the overall device performance is investigated. Finally, a feasible resistive switching model is proposed to describe the conduction mechanism of the device.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 3 System Model
### Mobility Model  
UAVs are deployed to particular locations to cover the area uniformly. They have small-scale mobility where these aerial systems are assumed to hover around these points in a limited space. Specifically, they are assumed to be forming a small-sized 8 shape by moving around two fixed points at the same altitude as the mobility model in [31].
## 5 Conclusions  
This study proposes a cascaded convolutional recurrent neural network based on multidimensional features for emotion recognition. To address the limitations of the previous literature, a 4D matrix is constructed to incorporate emotional features of the signal in the temporal, frequency, and spatial dimensions. Additionally, a hybrid deep learning model is proposed to better fit the extracted feature matrix. The convolutional encoder is mainly used to extract spatial-frequency features from 4D input data, and the residual network composed of DC and PC improves the real-time performance of the recognition system. FeaNet assigns more accurate weights to different feature channels at a negligible computational cost, allowing useful feature information to be further highlighted. Finally, to emphasize the temporal significance of the frame windows in the sample, the weighted sum of the hidden layer states of the Bi-LSTM at all frame moments is utilized as input to the softmax layer. The experimental results demonstrate that the proposed method in this paper performs well compared to the rest of the literature, with an average accuracy of 97.84% in the two classification experiments and 88.46% in the four-classification experiments. In future work, we will explore a more expressive feature extraction method  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Literature** & **Metrics** & **Dataset** & **Test Accuracy (\%)** & **Methods** \\ \hline Zubair [7] & H/L V-A & DEAP & 45.40 & mRMR \\ \hline \multirow{3}{*}{Guptal [8]} & H/L V-A & DEAP & 71.43 & \multirow{3}{*}{FAWT-SVM} \\  & Negative/neutral/positive & — & — & \\  & On beta gamma & SEED & 83.33 (avg) & \\ \hline \multirow{2}{*}{Agui\#aga [9]} & Valence/arousal & \multirow{2}{*}{DEAP} & 84.20 (avg) & \multirow{2}{*}{WP-NN-SVM} \\  & H/L V-A & & 80.90 & \\ \hline \multirow{4}{*}{[PERSON] [21]} & Valence & \multirow{4}{*}{DEAP} & 83.60 & \multirow{2}{*}{CNN} \\  & Arousal & & 83.0 & \\ \cline{1-1}  & H/L V-A & & 73.10 & \\ \hline \multirow{3}{*}{[PERSON] [39]} & Valence & \multirow{3}{*}{DEAP} & 84.16 & \multirow{3}{*}{PSO-BiLSTM} \\  & Arousal & & 85.21 & \\ \cline{1-1}  & H/L V-A & & 82.01 & \\ \hline \multirow{3}{*}{[PERSON] [40]} & Valence & \multirow{3}{*}{DEAP} & 85.53 & \multirow{3}{*}{Multiscale CNN} \\  & Arousal & & 76.77 & \\ \hline Ours & H/L V-A & DEAP & 88.46 & CNN-BiLSTM-FeaNet \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of different deep learning methods for four classifications.  
and apply a more streamlined network to the recognition task, making emotion recognition more rapid in the HCI domain.  
Data curation, C.W.; investigation, Y.L.; resources, Y.L.; software, C.W. and C.L.; supervision, Y.L.; writing--original draft, C.W.; writing--review and editing, C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51775076).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Conclusions  
In this paper, a similarity measurement with entropy-based weighting is proposed for mixed datasets with numerical and categorical attributes. For categorical datasets, a similarity metric is designed by assigning different weights to each attribute based on information entropy theory. For numerical datasets, the original high-dimensional numerical data are transformed to categorical data by an automatic categorization technique, so the similarity metric for categorical datasets can be applied to numerical datasets. Then, a similarity measurement for mixed datasets was obtained. Extensive experimental results show that the iterative clustering algorithm based on the proposed similarity measurement can achieve higher clustering accuracy and is superior to the existing clustering algorithms on datasets from UCI. The results also validate the feasibility of handling different types of attributes and verify that various attributes contribute differently in similarity measurements when clustering.  
Conceptualization, X.Q. and S.J.; methodology, J.Y.; software, S.J.; writing--original draft preparation, J.Y. and S.J.; writing--review and editing, J.Y. and X.Q.; supervision, N.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported partially by the National Natural Science Foundation of China (No. 61502135), the Programme of Introducing Talents of Discipline to Universities (No. B14025), and the Anhui Provincial Key Technologies R&D Program (No. 1804 bb06020378). The funding bodies had no role in study design, data collection and analysis, or preparation of the manuscript.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Dataset** & **Correlation Coefficient** \\ \hline Waveform & 0.1200 \\ Wine & \(-\)0.1458 \\ Mass & 0.9245 \\ Seeds & 0.0944 \\ Iris & \(-\)0.6511 \\ Fertility & 0.5526 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The correlation coefficient between dependencies and weights in each numerical dataset.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset** & **k-Means** & **The Proposed Algorithm** \\ \hline Waveform & \(0.4764\pm 0.0002\) & \(0.4654\pm 0.0097\) \\ Wine & \(0.0378\pm 0.0031\) & \(0.0660\pm 0.0451\) \\ Mass & \(0.4631\pm 0.0000\) & \(0.1962\pm 0.0740\) \\ Seeds & \(0.3857\pm 0.0000\) & \(0.3813\pm 0.0310\) \\ Iris & \(0.1677\pm 0.0859\) & \(0.0563\pm 0.0651\) \\ Fertility & \(0.1200\pm 0.0000\) & \(0.1200\pm 0.0000\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the proposed algorithm with k-means on numerical datasets.
# Effect of Aging and Moisture Damage on Fatigue Cracking Properties in Asphalt Mixtures
## 2 Materials and Mix Design  
### Materials  
In this study, the main specifications of the used asphalt binder for manufacturing HMA and WMA has penetration levels of 70-80 and PG 64-22 produced by company A. Table 1 shows the basic properties of asphalt binders of HMA and WMA.  
The WMA additive used in this study was prepared using the Fischer-Tropsch synthesis method based on a polyethylene-based wax. It is characterized by the effects of improving the fluidity of the asphalt binder and reducing the manufacturing temperature and compaction temperature of the asphalt mixture. The asphalt binder used in HMA and WMA mixtures consists of asphalt for road paving with a penetration standard of 70-80 and a PG grade of 64-22. Similarly, the aggregate consists of crushed granite aggregate satisfying the WC-3 (nominal maximum size 19 mm) particle size standard presented in the \"Asphalt Mixture Production and Construction Guidelines (2017)\" by the Ministry of Land, Infrastructure, and Transport [15]. In addition, the thick aggregate used was a single-particle first-class aggregate, and an aggregate with a knitting stone rate of less than 10% of the thick aggregates was used. Tables 2 and 3 show the results of the quality test of the aggregation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Contents** & **Test** & **HMA** & **WMA** \\ \hline \multirow{5}{*}{Original (Before short-term aging)} & Penetration (0.1 mm) & 73 & 69 \\  & Flash point (\({}^{\circ}\)C) & Over 300 & Over 300 \\  & Softening point (\({}^{\circ}\)C) & 44.2 & 46.3 \\  & Viscosity at 135 \({}^{\circ}\)C(\(\mathrm{c}\)F) & 482 & 416 \\  & G\({}^{\circ}\)sin\(\delta\)Fa) & 1.221 & 1.304 \\  & & (64 \({}^{\circ}\)C) & (64 \({}^{\circ}\)C) \\ RTFO (Short-term aging) & G\({}^{\circ}\)/sin\(\delta\)Fa) & 2.535 & 2.419 \\  & & (64 \({}^{\circ}\)C) & (64 \({}^{\circ}\)C) \\  & Bending beam & Stiffness (MPa) & 201.44 & 187.32 \\  & rheometer & (\(-12\)\({}^{\circ}\)C) & (\(-12\)\({}^{\circ}\)C) \\  & (BBR) & m-value & 0.31 & 0.32 \\  & Performance grade & (\(-12\)\({}^{\circ}\)C) & (\(-12\)\({}^{\circ}\)C) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test variables and designation of specimens.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Contents**} & \multirow{2}{*}{**Spec.**} & \multicolumn{3}{c}{**Test Result**} \\ \cline{3-5}  & & **Coarse Agg.** & **Fine Agg.** & **Filler** \\ \hline Specific gravity & \(\geq\)2.5 & 2.89 & 2.77 & 2.68 \\ Water absorption (\%) & \(\leq\)3.0 & 0.33 & 0.65 & - \\ LA abrasion coefficient (\%) & \(\leq\)35 & 12.2 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic properties of the used aggregates.
# Trajectory Planning and Optimization for a Par4 Parallel Robot Based on Energy Consumption
## 2 Part Parallel Robot
### Inverse Kinematics of the Par4 Parallel Robot  
According to the Par4 parallel robot structure, the inverse kinematic rotation angle of each big arm can be obtained as shown in Equation (1):  
\[q_{i}=2\arctan(\frac{-l_{i}-\sqrt{l_{i}^{2}-K_{i}^{2}+l_{i}^{2}}}{K_{i}-l_{i}}), \quad i=1,2,3,4. \tag{1}\]  
Here, \(l_{i}=L_{i}z\), \(K_{i}=({a_{i}}^{2}+{b_{i}}^{2}+{L_{i}^{2}}-l_{i}^{2}+{z^{2}})/2\), \(J_{i}=-L_{i}(a_{i}\cos\gamma_{i}+b_{i}\sin\gamma_{i})\), \(a_{i}=x-\varepsilon_{i}h\sin\theta+\xi_{i}d/2+\varsigma_{i}d_{i}-R\cos\gamma_{ i}\), \(b_{i}=y+\varepsilon_{i}h\cos\theta+\sigma_{i}h_{i}-R\sin\gamma_{i}\), \(\boldsymbol{\varepsilon}=[\varepsilon_{1},\varepsilon_{2},\varepsilon_{3}, \varepsilon_{4}]=[1,1,-1,-1]\), \(\boldsymbol{\xi}=[\xi_{1},\xi_{2},\xi_{3},\xi_{4}]=[1,-1,-1,1]\), \(\boldsymbol{\varsigma}=[\varepsilon_{1},\varepsilon_{2},\varepsilon_{3}, \varepsilon_{4}]=[1,-1,-1,1]\), and \(\boldsymbol{\sigma}=[\sigma_{1},\sigma_{2},\sigma_{3},\sigma_{4}]=[1,1,-1,-1]\).  
Given the spatial trajectory of the robot end-effector, the angular displacement of each driving mechanism can be obtained through the inverse kinematic equations, as in Equation (1). Accordingly, if the robot is driven with this obtained angular displacement law, the expected end-effector spatial trajectory will be achieved [35].
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 2 Innovative Positioning and Navigation Technologies
### Wi-Fi(tm) Ranging  
Besides providing connectivity, Wi-Fi(tm) signals are being also used to provide with detection/positioning using the Received Signal Strength (RSS) and a propagation model to obtain an estimate of the range between the rover and router. Unfortunately, the received power is very susceptible to the environment (walls, glass, etc.) [30, 31]. In 2016, the new Wi-Fi(tm) protocol (802.11 mc, [32]) was issued to allow the possibility to measure the travel time (Fine Timing Measurements (FTM) or Round Travel Time (RTT)), between the rover and router. This direct measure of range (without the need to rely on a propagation model) offers a more robust means to navigate indoors as the range-based navigation is less susceptible to environment. One of the potential limitations of Wi-Fi(tm) ranging is multipath, which may limit the accuracy of the position indoors. However several works have already demonstrated meter-level navigation [33] as well as potential means to improve accuracy by using frequency diversity (see for instance [34]).  
Moreover, range-based navigation based with Wi-Fi(tm) can be achieved with mobile technology. Recent versions of the Android(tm) operating systems ([35]) give developers the possibility to record and process Wi-Fi(tm) FTM readings.
The methodology used to evaluate the proposal is based on a simulation that uses a benchmark with real devices. We compare SLedge with a cloud architecture to highlight the benefits of the approach and evaluate the system behavior by measuring latency and utilization of each component of the system in a changing environment. The remainder of the article is organized as follows: Section 2 introduces the main background concepts and related work. Section 3 details SLedge, the system we propose to schedule and load balancing SPS's tasks over a set of available mobile devices. Experimental evaluation is presented in Section 4 with concluding remarks in Section 5.
# Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand
## 5 Computational Experiments
### Actual Case Study
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number** & **Latitude** & **Longitude** & **Demand** & **Time Window** & **Latitude** & **Longitude** & **Demand** & **Time Window** \\ \hline
0 & 41.81139 & 123.4902 & 0 & 8:00–11:00 & 11 & 41.79927 & 123.3922 & 13.9 & 9:00–10:00 \\
1 & 41.79223 & 123.3694 & 28.4 & 8:00–9:00 & 12 & 41.73469 & 123.4037 & 18.5 & 9:00–10:00 \\
2 & 41.83274 & 123.5763 & 0.8 & 10:00–11:00 & 13 & 41.86065 & 123.4044 & 1.3 & 10:00–11:00 \\
3 & 41.81372 & 123.5455 & 16.4 & 8:00–9:00 & 14 & 41.81685 & 123.4101 & 16.8 & 9:00–10:00 \\
4 & 41.8289 & 123.5302 & 23.5 & 8:00–9:00 & 15 & 41.76482 & 123.5461 & 17.2 & 10:00–11:00 \\
5 & 41.81375 & 123.5581 & 1.5 & 9:00–10:00 & 16 & 41.86355 & 123.4206 & 21.1 & 9:00–10:00 \\
6 & 41.8369 & 123.5497 & 1.5 & 9:00–10:00 & 17 & 41.76901 & 123.5652 & 17.5 & 10:00–11:00 \\
7 & 41.83439 & 123.3972 & 1.1 & 9:00–10:00 & 18 & 41.75193 & 123.5579 & 3.5 & 10:00–11:00 \\
8 & 41.80242 & 123.3434 & 13.5 & 10:00–11:00 & 19 & 41.69756 & 123.4184 & 2.6 & 9:00–10:00 \\
9 & 41.7789 & 123.4403 & 15.5 & 8:00–9:00 & 20 & 41.75746 & 123.5306 & 23.5 & 9:00–10:00 \\
10 & 41.87804 & 123.4808 & 0.5 & 9:00–10:00 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Information for static customers. route of the original customers as far as possible to avoid reducing the level of service offered to customers.
# Biosynthetic Pathways of Hormones in Plants
## 9 Cytokines
DMAPP as donors in vitro, while HMBDP is predominantly utilized in vivo, leading to the production of \(tZ\), which is a highly active CK, in plastids of host plants [150]. IPTs are transported into different organelles, such as mitochondria, plastids, and the cytosol. Therefore, isoforms of IPT, such as AtIPT1, AtIPT3, AtIPT5, and AtIPT8, are present in the plastids and involved in the production of CKs from DMAPP that originate from the MEP pathway [151]. On the other hand, AtIPT4 is localized in cytosol, whereas AtIPT7 can be found in mitochondria. These observations suggest that DMAPP involved in CK biosynthesis is also derived via the MVA pathway [147]. Studies suggest that tRNA isopentyltransferase (tRNA IPT) is responsible for the transportation of DMAPP via MVA pathway to adenine in tRNA, which results in the creation of \(cZ\)[143]. Studies of the synthesis of the isopentenyl side chain of the CK present in the tRNA molecule in vitro demonstrated that the isopentenyl group was derived from CKs MVA, and the turnover of CK-containing tRNA may serve as a minor source of free \(cZ\) in plant cells. It has been proposed that the target adenine is released from the tRNA structure in a similar way to that modified by other nucleic acid-editing enzymes. This type of IPT has been identified in almost all living organisms, including bacteria, yeast, animals, and plants. However, tRNA IPT was not found in Archaea. The isopentenyl of adenine present in tRNA has an effect on translational efficiency and precision. It improves proofreading during the translation process by decreasing misreading at the first position of the codon [146; 152]. The different origins of large amounts of DMAPP utilized in biosynthesis of CKs, such as \(tZ\) and \(cZ\), suggest that plants can precisely regulate the homeostasis of these CKs [147].
# A Prototype of a Portable Gas Analyzer for Exhaled Acetone Detection
## 2 Development of the Portable Gas Analyzer
### 3D Printable Housing Design  
The housing of the device was designed in a way to accommodate the gas chamber described in Section 2.2 as well as the motherboard PCB described in Section 2.4. As the developed analyzer is a portable device, its size and ergonomics are of great importance. Therefore, a 3D printing technology was employed offering a high degree of design freedom. The design was an iterative process. The first iteration was evaluated in terms of ergonomics of use, in other words, the legibility of the display, the  
Figure 1: Drawing of the designed gas chamber as a dedicated compartment in the device housing (in salmon). View of the mouthpiece (in light gray) and ventilation grate (in beige) (**a**). The SHT21 is soldered to a raised daughter board (in dark gray) allowing for indirect airflow as well as thermal separation from the gas sensor array (in violet) printed circuit boards (PCB) (**b**).  
positioning of the buttons with regard to the possibility of one-hand operation, and the grip in the hand. When the design met the above requirements, the second iteration was carried out to position I/O ports (including openings in the housing), ensuring the convenience of use and avoiding interference when the analyzer is held. The final design is shown in Figure 2. The housing is comprised of two parts: front and back pieces. The front piece has the user interface panel and the back piece has two separated compartments: the gas chamber and the electronics compartment. To close the housing, a set of latches on the back panels snap into indents in the front piece allowing for easy and tool-free access. In the electronics compartment, a shallow indentation the size of the display was added for ease of alignment and mounting along with properly spaced mounting posts for the motherboard PCB. The housing size was designed so that there is sufficient space for the electronics and sensors while providing a compact body of the device. The overall external dimensions of the device are 95.2 mm \(\times\) 23.7 mm \(\times\) 51.6 mm.
# A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals
## 3 Conventional Two-Stage Carrier Tracking Strategy
### Coarse Tracking Design
#### 3.1.1 Frequency Pulling Algorithm  
The inputs of the frequency pulling module are the prompt branch of the orthonormal correlator outputs, \(I(k)\) and \(Q(k)\), as expressed in (2) and (3). The block diagram of the frequency pulling model is shown in Figure 2.  
Figure 1: Block diagram of the conventional two-stage carrier tracking control strategy.  
The formula for the four-quadrant arctangent discriminator is given by [1]:  
\[\left\{\begin{array}{c}cross=I_{p}(k-1)Q_{p}(k)-Q_{p}(k-1)I_{p}(k)\\ dot=I_{p}(k-1)I_{p}(k)+Q_{p}(k-1)Q_{p}(k)\end{array}\right. \tag{4}\]  
where \(I_{p}(k)\) and \(Q_{p}(k)\) are the correlation values of the prompt branch at time \(k\). The phase error and frequency error are expressed by [1]:  
\[\delta\varphi=\text{ATAN2}(cross,dot)=2\pi T_{s}\delta f(k) \tag{5}\]  
\[\delta f_{i}=\delta f(k)=\frac{\delta\varphi}{2\pi T_{s}} \tag{6}\]  
where ATAN2\((x,y)\) is the four-quadrant arctangent function, which returns the result in radians. The unit of \(\delta f_{i}\) is hertz.  
Due to the randomness of the receiver dynamic, the estimated value, \(\delta f_{i}\), can be regarded as a random variable. The output of the frequency pulling model, \(\delta f_{out}\), is computed by the maximum likelihood estimator of all observation measurements, \(\delta f_{i}\). The outlier elimination method is adopted as the maximum likelihood estimator. The calculation method is as follows:  
\[\delta f_{out}=\frac{1}{N-2}\left[\left(\sum_{i=1}^{N}\delta f_{i}\right)- \delta f_{\text{max}}-\delta f_{\text{min}}\right] \tag{7}\]  
where N is the total number of observations during the frequency pulling algorithm, and \(\delta f_{\text{max}}\) and \(\delta f_{\text{min}}\) are the maximum and minimum, respectively, of the \(N\) observation values.
## 3 Results and Discussion
### PCA of Volatile Components by GC-MS  
PCA was used to gain a comprehensive understanding of the differences in volatile compounds among the ISP suspensions with/without CD (Figure 1). Alkanes, alkenes, alcohols, ketones, aldehydes, and furan were absorbed by the SPME fiber from the samples. Figure 1 shows that the two most important principal components (PC1 and PC2) extracted by PCA were responsible for 53.71% and 36.87% of the total variance, respectively. +CD-0 and -CD-0 located in the positive area of both PC1 and PC2 and were related to higher contents of alcohols, ketones, and aldehydes, which contained compounds of heavy flavor, such as 1-octen-3-ol, benzaldehyde, hexanal, and 2-heptanones. Furthermore, +CD 1-4 were positioned in the higher left quadrant, whereas -CD 1-4 were positioned in the lower left quadrant. The positions of the +CD 1-4 samples were related to alkanes, alkenes and furans originating from CD. In particular, 2-pentylfuran was associated with the characteristic beamy flavor. Therefore, PC2 illustrates the differences between the presence (+) and absence (-) of flavor-entrapped CD. The absence (-) of flavor compounds in the -CD group can be attributed to the removal of flavor-entrapped CD.