esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### LTE Mode 4
#### 4.1.2 Performance Results  
OMNeT++ provides the results obtained in both vector (.vec) and scalar (.sca) files. Jupyter Notebook, which runs under the Anaconda package management system, is used as a data analysis and representation tool.  
Figure 7 shows PDR curves as a function of distance under certain packet transmission rates. The rest of the parameters configured in this simulation were described in Section 4.1. In this experiment, a road density of 0.06 vpm has been considered. That is, using openCV2X, about 130 vehicles are placed along the two kilometers of the highway. This case corresponds to the fast highway scenario mentioned above. The average speed at which vehicles travel on the road is 140 km/h. Figure 7 shows that the increase in V2V packet transmission rates decreases the distance under which an adequate PDR can be guaranteed. For example, for a packet transmission rate of 5 pps, the maximum distance  
Figure 6: Main aspects followed by openCV2X for LTE Mode 4.  
to guarantee a PDR of 80% must be below 250 m. Increasing the packet transmission rate causes coverage conditions to decrease significantly.  
In order to analyze the PDRs under congested freeway conditions, a scenario with a vehicle density on the freeway of 0.12 vpm with an average speed of 70 km/h is set up. Figure 8 shows the variation of PDR as a function of propagation distance under different packet transmission rates and vehicle density on the road. In this case, it is evident that if the number of vehicles on the road increases, the PDR decreases because the channel congestion increases. Thus, there is a higher probability that more than one vehicle reserves the same resources in time and frequency, so that the SCIs associated with each V2V message cannot be properly received.  
In Figure 9, we analyze the effect of transmit power on the PDR for different propagation distances. In this case, a fast highway scenario with a transmission rate of 1 pps is considered. It is verified that when the transmit power decreases from 23 dbm to 20 dBm, the PDR levels are similar for distances below 300 m. However, longer distances lead to higher packet losses.  
Figure 8: PDR as a function of propagation distance under different packet transmission rates and vehicle density.  
Figure 7: PDR variation as a function of distance for different V2V packet transmission rates.
## 3 Proposed System
### Multiview Features Extraction and Representation
#### 3.2.2 Hu-Moments Invariant Features  
The use of invariant moments for binary shape representation was proposed in [34]. The moments which are invariant with respect to rotation, scales, and translations, are known as Hu-moments invariants. We have computed seven moments for each radial bin of the silhouette. The moment (_p,q_) of an image _f_(_x,y_) of size \(M\times N\) is defined as:  
\[m_{p,q}=\sum_{x=0}^{M-1}\sum_{y=0}^{N-1}f(x,y)x^{p}y^{q} \tag{3}\]  
Here, \(p\) is order of \(x\) and \(q\) is order of \(y\). We can calculate the central moment in the same way as these moments, except the value of \(x\) and \(y\) is displaced by the mean values as follows:  
\[\mu_{p,q}=\sum_{x=0}^{M-1}\sum_{y=0}^{N-1}f(x,y)(x-x_{avg})^{p}(y-y_{avg})^{q} \tag{4}\]  
where  
\[x_{avg}=\frac{m_{10}}{m_{00}}\text{ and }y_{avg}=\frac{m_{01}}{m_{00}} \tag{5}\]  
By applying normalization, scale-invariant moments are obtained. Hence, normalized central moments are defined as follows [35].  
\[\mathfrak{n}_{p,q}=\frac{\mu_{p,q}}{\mu 000^{\gamma}},\ \gamma=\frac{p+q+2}{2},\ p+q=2,3,\ldots \tag{6}\]  
Based on these central moments, [34] introduced seven Hu-moments as linear combination of central moments defined as follows:  
\[h_{1} =\mathfrak{n}_{20}+\mathfrak{n}_{02}\] \[h_{2} =(\mathfrak{n}_{20}-\mathfrak{n}_{02})^{2}+4\mathfrak{n}^{2}{}_ {11}\] \[h_{3} =(\mathfrak{n}_{30}-3\mathfrak{n}_{12})^{2}+(3\mathfrak{n}_{21} -\mathfrak{n}_{03})^{2}\] \[h_{4} =(\mathfrak{n}_{30}+\mathfrak{n}_{12})^{2}+(\mathfrak{n}_{21}+ \mathfrak{n}_{03})^{2}\] \[h_{5} =(\mathfrak{n}_{30}-3\mathfrak{n}_{12})((\mathfrak{n}_{30}+3 \mathfrak{n}_{12})^{2}-3(\mathfrak{n}_{21}+\mathfrak{n}_{03})^{2})+\] \[(3\mathfrak{n}_{21}-\mathfrak{n}_{03})^{2}(\mathfrak{n}_{21}+ \mathfrak{n}_{03})(3(\mathfrak{n}_{30}+\mathfrak{n}_{12})^{2}-(\mathfrak{n}_ {21}+\mathfrak{n}_{03})^{2})\] \[h_{6} =(\mathfrak{n}_{20}-\mathfrak{n}_{02})((\mathfrak{n}_{30}+ \mathfrak{n}_{12})^{2}-(\mathfrak{n}_{21}+\mathfrak{n}_{03})^{2})+4\mathfrak{ n}_{11}(\mathfrak{n}_{30}+\mathfrak{n}_{12})(\mathfrak{n}_{21}+\mathfrak{n}_{03})\] \[h_{7} =(3\mathfrak{n}_{21}-\mathfrak{n}_{03})(\mathfrak{n}_{21}+ \mathfrak{n}_{03})(3(\mathfrak{n}_{30}-3\mathfrak{n}_{12})^{2}-(\mathfrak{n}_ {21}+\mathfrak{n}_{03})^{2})-\] \[(\mathfrak{n}_{30}-3\mathfrak{n}_{12})(\mathfrak{n}_{21}+ \mathfrak{n}_{03})(3(\mathfrak{n}_{30}+\mathfrak{n}_{12})^{2}-(\mathfrak{n}_ {21}+\mathfrak{n}_{03})^{2})\]  
Figure 4: Example of division of silhouette into radial bins.
# A Survey of Graphene-to-Phoneme Conversion Methods
## 5 Dataset and Evaluation Metrics
### Evaluation Metrics  
To evaluate the performance of a G2P converter, Phoneme Error Rate (\(PER\)) and Word Error Rate (\(WER\)) are the two most commonly used metrics. Both of these two metrics are used to measure the similarity between the predicted text and the reference text. \(WER\) is a word-level indicator which can be defined as the rate of word errors in a predicted text:  
\[WER=\frac{Substitutions+Deletions+Insertions}{Number\ of\ Words in\ Reference} \tag{5}\]  
\(PER\) is a phoneme-level indicator that can be calculated as the rate of phoneme errors in a predicted text:  
\[PER=\frac{Substitutions+Deletions+Insertions}{Number\ of\ Words\ in\ Reference} \tag{6}\]  
where the \(Substitutions,Deletions,Insertions\) are edit operations that change one text to another reference text.  
\(PER\) is more concerned with phoneme-level errors and is suitable for evaluating phoneme conversion systems' performance; \(WER\) is more concerned with word-level errors and is suitable for evaluating speech recognition systems' performance. In general, lower \(WER\) and \(PER\) indicate better conversion performance. \(WER\) is generally considered the primary metric for grapheme-to-phoneme conversion tasks because even a small conversion error can lead to a noticeable decline in the quality of downstream speech applications.
## 2 Materials and Methods  
The methodology implemented in this research is described in Figure 1. The initial phase of the research involved gathering and preprocessing the ground reference data in ArcGIS Pro 3.3.0. The ground reference data aided in training the models, finding the optimized architectures of DL models, and evaluating their accuracy. Having reduced the number of samples in the majority classes and made a relative balance among classes, ground reference pixels were partitioned into training, validation, and test subsets. Following the division of ground reference pixels into training, validation, and test subsets and ensuring relatively consistent class distributions across all subsets, we examined whether the dataset partitioning adhered to the assumption of independence and identical distribution (IID). Accordingly, the conformity to the IID assumption was evaluated by comparing the cumulative distribution functions (CDFs) of subsets pairwise and examining the corresponding \(p\)-values through the Kolmogorov-Smirnov (KS) statistical test. Subsequently, 10-day interval median composites of Sentinel-2 satellite imagery comprising 10 spectral bands (i.e., blue, green, red, red-edge1, red-edge2, red-edge3, NIR, red-edge4, SWIR1, SWIR2) were created through the Earth Engine Python API on the Google Earth Engine (GEE) cloud-based platform. This process entailed capturing the median pixel values of corresponding pixels in the images for each band within 10-day intervals to accentuate the dynamics of vegetation growth. Then, the values of the spectral bands were extracted for all training, validation, and test pixels from April to October 2021. Subsequently, the models were trained with the training pixels, the optimal architecture for each DL model was determined using the validation pixels, and the accuracy of the models was assessed with the test pixels. Finally, the DL-based model exhibiting the highest accuracy was selected for predicting land cover and crop type throughout the study area.
## 3 Jitter Modeling  
In this section, we first give the TIE jitter model, and then give the specific model of PJ and DDJ according to TIE. The periodic jitter model of rectangular, sine waves, DCD, and ISI, in time domain, is given.  
\begin{table}
\begin{tabular}{c c c c} \hline  & [21] & [19] & **This Paper** \\ \hline Jitter frequency & 1 Hz–100 MHz & 1 kHz–20 MHz & 1 Hz–20 MHz \\ \hline \multirow{2}{*}{Jitter frequency Resolution} & 1 Hz in 1 Hz to 1 MHz; & & 1 Hz in 1 Hz to 1 MHz; \\  & 1 kHz in 1 MHz to 100 MHz & / & 1 kHz in 1 MHz to 20 MHz \\ \hline Jitter frequency accuracy & \(\pm\)50 ppm & / & \(\pm\)30 ppm \\ \hline \multirow{2}{*}{Minimum jitter amplitude} & 0.6 UI & 0.2 UI & 10 ps \\  & (100 M jitter frequency) & (20 M jitter frequency) & (Not related to jitter frequency and data rate) \\ \hline Minimum jitter & 0.01 UI & / & 10 ps (digital control) \\ amplitude resolution & (100 M jitter frequency) & / & 2 ps (analog control) \\ \hline \end{tabular}
\end{table}
Table 1: Comparing jitter generation.  
Figure 1: Analog modulation for jitter generation.
## 2 Research and Development Trends
### From CPS to CPSS  
The Cyber-Physical System was proposed by the USA's national science foundation (NSF) in 2007, which is a complex system mainly consisting of engineering complexity demand. It is becoming a hot topic all over the world [8]. The international business machines corporation (IBM) puts forward that CPS is a strategic application concept practice of \"Smart Earth\". The NSF has funded more than 500 research projects in such areas as basic theory, method tools, and platform systems of CPS.  
However, the existing researches of complex systems mainly focus on engineering complexity. The social complexity elements like human acting as designers, builders, operators, and terminal users, usually play important roles in these complex systems. Therefore, to achieve secure reliability and efficient management of those systems, the engineering complexity elements and social complexity elements should be studied as an indivisible whole. To address these problems, [PERSON] firstly proposed CPSS in which those complex systems can integrate more even all elements with in-depth wisdom gradually, and then build up more and more complex systems [9]. CPSS is a kind of complex system composed of physical system, social system, and cyber system, in which a cyber system connects the physical system and social system [10; 11].  
Based on CPS, the social system is added in CPSS which contains humans and social factors. Moreover, the cyber system can be achieved through an artificial system to connect the social and physical system [12; 13]. Effective management strategies are generated through the interaction between artificial and real systems. It is an organic combination of human organization and physical entity system through intelligent human-machine interaction, which is the basis of intelligent management for complex systems [14].  
At present, with the rapid development of new information technologies like mobile Internet, cloud computing, big data, and AI, the study of CPSS becomes a common demand of the many national strategy industries, and also becomes the frontier of theory, technology, and application of \"system science and systems engineering discipline\".
# Hierarchical Synchronization Control Strategy of Active Rear Axle Independent Steering System
## 3 Hierarchical Synchronization Control Strategy Design
### Steering Angle Tracking Controller
The proportional-differential (PD) form is selected for the linear state error feedback control law, given by  
\[\left\{ \begin{array}{l}u_{01}=k_{p}(\delta_{r1}-z_{1})-k_{d}z_{2}\\ u_{1}=(u_{01}-z_{3})/b_{01}\\ F_{r1}=u_{1}/\cos z_{1}\end{array} \right. \tag{17}\]  
\[\left\{ \begin{array}{l}u_{02}=k_{p}(\delta_{r2}-z_{4})-k_{d}z_{5}\\ u_{2}=(u_{02}-z_{6})/b_{02}\\ F_{r2}=u_{2}/\cos z_{4}\end{array} \right. \tag{18}\]  
where \(\delta_{r1}\) and \(\delta_{r2}\) are the given reference signal; \(u_{01}\) and \(u_{02}\) are the ideal control output; and \(k_{p}\) and \(k_{d}\) are the gain coefficients of the feedback control law. According to [28], the parameters of \(k_{p}\) and \(k_{d}\) can be selected by  
\[k_{p}=\omega_{c}^{2},\ k_{d}{=}2\omega_{c} \tag{19}\]  
where \(\omega_{c}\) is the controller bandwidth. The control block diagram of linear active disturbance rejection control (LADRC) tracking controller for ARIS system is shown in Figure 5. The stability of LADRC was analyzed and proved in detail in [34]. The upper controller adopts the PD feedback control law in order to realize steering angle synchronization control. It can be seen from Equation (10) that when the synchronization error \(e_{s}\) converges to \(0\), the equivalent rear axle steering angle \(\delta_{r}^{*}\) will converge to the target angle of the rear axle wheel \(\delta_{r}\). It can be seen from Equations (17) and (18) that under the control of \(F_{r1}\) and \(F_{r2}\) of the lower controller, \(\delta_{r1}^{*}\) and \(\delta_{r2}^{*}\) converge to \(\delta_{r1}\) and \(\delta_{r2}\), respectively. Therefore, ARIS system can achieve stability. The proposed control strategy above has the following advantages. (1) The lower controller output signals, including the external disturbance of both side wheels, are fed back to the upper controller and interact on the dynamic model of VRSM together. It reduces the synchronization error caused by the unilateral disturbance. (2) The LESO in the lower controller can not only observe the external disturbance, but also reconstruct the steering wheel angle and angular velocity. (3) The feedback control law uses the estimated states as the input to avoid the output oscillation of the controller in case of directly using the measurement value of the sensor with noise, especially in a differential term.
# Efficient Detection of Pre-Proinsulin by Double Antibody Sandwich ELISA
###### Abstract  
To detect residual pre-proinsulin (PPI) in recombinant human insulin production, an analytical method based on double-antibody sandwich ELISA was developed in this study. The BALB/c mice were immunized with PPI, and the hybridomas secreting anti-PPI monoclonal antibodies were obtained using the conventional cell fusion technique and ELISA screening. We purified the antibody using a Protein G gel column and identified its purity by SDS-PAGE. The sandwich ELISA was used to explore the pairing effect, and the specificity of the paired antibody was determined. We selected a paired antibody with relatively good specificity to establish sandwich ELISA, constructed a quantitative curve, and evaluated the accuracy and sensitivity of the method. Six anti-PPI monoclonal antibodies were obtained, named P1, P2, P3, P4, P5 and P6, of which P5 had the highest titer value. The sandwich ELISA method was established with P5 for plating and P2 as detection antibodies. The linear range of the quantitative curve of PPI by sandwich ELISA was 0.645 to 82.5 pg/mL, the recovery was 95% and the detection limit was 3.06 pg/mL. In this study, we prepared six anti-PPI monoclonal antibodies and established the sandwich ELISA method to detect PPI in process control and product release control for recombinant human insulin production.  
recombinant human insulin; pre-proinsulin; monoclonal antibody; sandwich ELISA Article  
Efficient Detection of Pre-Proinsulin  
ELISA. April. 2022, 12, 9868. [[https://doi.org/10.3390/app1299868](https://doi.org/10.3390/app1299868)]([https://doi.org/10.3390/app1299868](https://doi.org/10.3390/app1299868))
## 3 Results and Discussion
### Thermal Conductivity of the Hybrid Nanofluids
#### 3.2.3 Effect of Temperature  
Thermal conductivity of the hybrid nanofluid increased with temperature, as displayed in Figure 15, which was consistent with the trend of thermal conductivity of deionized water with temperature. Moreover, the thermal conductivity enhancement of the hybrid nanofluid decreased with increasing temperature. The thermal conductivity enhancement at 0 \({}^{\circ}\)C was 28.5 times that at 70 \({}^{\circ}\)C for 0.025 wt %, while the thermal conductivity enhancement at 0 \({}^{\circ}\)C was 4 times that at 70 \({}^{\circ}\)C for 0.2 wt %. This means that the influence of temperature on the thermal conductivity enhancement was more significant at a lower concentration, even though a higher concentration led to a higher thermal conductivity enhancement. This can be attributed to the fact that Brownian motion and thermal diffusion of nanoparticles increased as the temperature increased.  
Figure 13: Comparison of experimental and reference values of the thermal conductivity of deionized water.
## 1 Introduction  
Several literature studies showed that volcanic activity may induce electromagnetic signals (e.g., [1; 2; 3; 4; 5]). Among them, a few works were performed using data provided by the Detection of Electro-Magnetic Emissions Transmitted from Earthquake Regions (DEMETER) microsatellite. [PERSON] et al. [4] investigated the possible occurrence of ionospheric anomalies before volcanic eruptions. The authors analyzed data acquired 30 days before and 15 days after 74 eruptions from 50 volcanoes, recognizing 48 electromagnetic anomalies. In total, they found that ionospheric perturbations, at the satellite altitude, accompanied about 40% of eruptive events. Later on, [PERSON] et al. [5] extended their analyses with the complete set of DEMETER observations. During the entire mission, 73 volcanoes entered into eruption; for 58 of them, 269 anomalies related to 89 eruptions were detected. Five different types of ionospheric anomalies (density and temperature variations, electrostatic turbulence, and various electromagnetic emissions) were identified. They were similar to those observed above areas of impeding earthquakes. In addition, the study showed the occurrence of volcanic lightning strokes, which were observed by satellite above 12 volcanoes. [PERSON] et al. [6] reported observations of VLF (Very Low Frequency) waves from a Japanese ground-based transmitter, named JJI, during the January 2011 eruption of Mt. Kirishima (Southern Japan); this volcano was very close to the VLF transmitter. Perturbations of the nighttime subionospheric VLF signal were observed at the time of eruptions (receivers were located in the north of Japan). The authors attributed ionospheric perturbations to the penetration of gravity waves induced by the volcanic activity. Recently, another study investigated physical and chemical climatological anomalies related to volcanic activity, showing their occurrence before some major explosive eruptions [7].  
Some studies revealed that aerosols might alter the atmospheric electric field (e.g., [8; 9; 10]). Other authors demonstrated the formation of positive anomalies in the ionosphere over Europe, which were correlated with the position of ash clouds from the Eyjafjallajokull (Iceland) volcano [11]. Also, they reported the occurrence of stronger anomalies over Sahara sand storms passing from West Africa through the Atlantic Ocean, proposing the physical mechanism of the formation of ionospheric anomalies, due to modification of troposphere conductivity, within the framework of the global electric circuit (GEC) conception.  
In this work, we assessed the impact of Mt. Etna (Sicily, Italy) eruptions and massive Saharan dust outbreaks on the ionosphere. To perform this study, we analyzed a set of heterogeneous observations, spanning from DEMETER data to medium infrared/thermal infrared (MIR/TIR) satellite radiances and Global Positioning System (GPS) measurements. The paper is organized as follows: Section 2 summarizes the main studies of interest for this research; Sections 3 and 4 describe the data and methods used; Section 5 focuses on the achieved results; and Section 6 reports the final remarks of the work.
## 3 Results and Discussions
### Effects from Blockage on Radiation Heat Flux in Practical Battery Packs
With gradually increased spacing, the radiation heat flux drops on the closest cells and increases on the corner cells. This provides direct guidance on battery arrangement in arrays in order to suppress thermal runaway.
## 3 Results
### Evaluation  
To evaluate the developed application, we tested both the PCE creation as well as the Template generation process in the context of already existing postcoordinated expressions. By doing so, we intended to verify that our proposed approach is capable of creating correct Templates and PCEs while preventing the creation of incorrect expressions. Furthermore, we conducted a usability study to gain insight into the users' perspective. In this evaluation step, we primarily wanted to investigate if the implemented application adequately _simplifies_ PCE creation. Therefore, special attention was laid on the comprehensibility, functionality and usability ratings of the study participants.
# Design of Swarm Intelligence Control Based on Double-Layer Deep Reinforcement Learning
###### Abstract
Therefore, a key challenge remains in designing an adaptive optimization framework for swarm intelligence control systems. This framework should dynamically coordinate individual and global optimization while balancing task completion and energy efficiency under multi-objective conflicts. This paper focuses on dynamic adaptive adjustment and the collaborative learning optimization of agents and control strategies within the swarm, proposing a swarm intelligence control method based on double-layer deep reinforcement learning. This method effectively addresses the challenge of optimizing both energy efficiency and task completion in complex dynamic environments. It overcomes the limitations caused by the insufficient separation between individual and global optimization, as well as inadequate dynamic adaptability. Consequently, the overall system performance is significantly enhanced. In comparison to the existing research, the main innovations of this paper are as follows: (1) A swarm intelligence control framework based on double-layer deep reinforcement learning is proposed. Through dynamic interactions between the inner and outer layers, along with multi-level collaborative optimization, this method achieves an effective separation between individual and global optimization. This separation facilitates the seamless coordination between individual behavior optimization and global resource allocation. As a result, the system's adaptability and overall operational performance are significantly improved. (2) A learning algorithm designed for complex dynamic environments is developed. This algorithm uses deep reinforcement learning to enhance agents' self-adjustment capabilities, enabling them to adapt dynamically to both environmental changes and the behaviors of other agents. Thus, the system achieves a dynamic equilibrium between multi-agents and their environment. (3) Through simulation validation, this method demonstrates significant advantages in multi-objective collaborative optimization, effectively balancing energy efficiency and task completion, further enhancing the swarm's comprehensive task effectiveness.