esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Computational Experiments
### Ablation Experiments
#### 4.4.3 Effectiveness Verification of the Adaptive Learning Rate Strategy  
To validate the feasibility and effectiveness of the adaptive learning rate strategy, we compare the proposed DRLLA with the adaptive learning rate (DRLLA-a) to two compared algorithms: DRLLA with a fixed learning rate of 0.001 (DRLLA-f), and DRLLA with an exponential learning rate (DRLLA-e). In DRLLA-e, the initial learning rate is 0.01, and the decay rate is 0.001. The three algorithms are trained on the dataset \(\textit{Training\_R1}\) first and then tested on the dataset \(\textit{Testing\_R1}\). The comparison results of training and testing are presented as follows.  
Figure 10 depicts the training process of  
Table 11 lists the testing results of the three algorithms, where the best values are bolded. The standard deviation of profit ratios (STD) is set as an additional comparison indicator, and the lower STD value indicates the more densely distributed solutions and further demonstrates the better stability and generalization of the algorithm. DRLLA-a can reach the highest MPR value within the acceptable computational time in most testing instances, and its STD value, close to that of DRLLA-e, is significantly better than that of DRLLA-f. As for DRLLA-f, its MPR value is slightly lower than that of DRLLA-a in most testing instances and even exceeds that of DRLLA-a when the task number is 250, while it performs the worst in terms of STD. DRLLA-e has superior stability ability and takes the least computational time, but it fails to generate solutions with high profit ratios. The testing results demonstrate that the proposed neural network trained with the adaptive learning rate training algorithm has an excellent solving ability, and the comprehensive testing performance of DRLLA-a is superior to the compared algorithms.
## 1 Introduction
* In order to minimize the total energy consumption of the entire system, a dynamic application partitioning task assignment algorithm (DAPTS) is presented. This DAPTS determines how to perform the assignment of local tasks on the devices and remote tasks on the fog nodes. * DAPTS exploits dynamic voltage frequency scaling (DVFS) method to manage and reduce the entire energy consumption of the FCA after task assignment phase without degrading any system performance. The rest of the paper is organized as follows. Section 2 elaborates related work and Section 3 explains the proposed model and formalizes the problem under study. A heuristic is proposed for the considered problem in Section 4, which describes the proposed algorithm and sequences. Section 5 evaluates the simulation part, and Section 6 presents the conclusive remarks.
## 4 Results and Discussion  
All push-based protocols are designed to deliver video segments to the client proactively, which means that the server sends the segments without waiting for the client to request them. Those protocols minimize the delay between the time the segment is available on the server and the time it is available to the client. All push-based protocols require the server to have knowledge of the client's buffer state to avoid sending unnecessary segments.  
The reason behind the selection of the FASP protocol is that it always uses the maximum throughput to transfer the network packets and makes the network condition stable with a minimum packet loss rate. As a result, no degradation occurs in transferred data bitrates as displayed in Figures 7 and 8.  
One advantage of the cuckoo search algorithm is that it does not require any gradient information or assumptions about the distribution of the objective function. This makes it suitable for optimizing complex, non-linear functions in high-dimensional search space. According to test results using MCSA, the average delay time was used to select an optimal edge server among other servers, which is quickly available for users and provides a high-quality video playback streamed video.  
The delay time of push-based protocol is equal to 0.00015 s when the proposed algorithm is used for 10 servers while equal to 1.1 s for the conventional algorithm. Moreover, the average delay time for 100 servers is equal to 13 s and 0.296 s with and without MCSA, respectively, as illustrated in Figure 9.
# Status of Pure Electric Vehicle Power Train Technology and Future Prospects
## 5 Power Electronics  
System integration of power electronics is an effective to fulfill the cost and package volume requirements on pure electric vehicles. Power electronics plays the medium to transfer the information between the battery, a DC current source, and the drive motor.  
A power converter device is necessary to regulate the power between the motor and the battery systems. The battery delivers current at a particular voltage. Power flowing into the battery needs to ensure of delivery of the correct voltage. Similarly, the power delivered by the battery must ensure the capability of electric machine to propel the vehicle. Algorithms of the control system specific to each type of motor always ensures its operation at the highest efficiency, typically between 95% and 98% [76]. Control strategies like model-referencing adaptive control (MRAC) and self-tuning control (STC), have been successfully applied to EV propulsion. Motor drives have also employed variable structure control (VSC) [76; 83]. Fuzzy logic and neural networks have been employed to realize the concept of intelligent controllers with promising applications to EV propulsion. Progress has been made in magnetic components and capacitors for its use in high frequency power electronics. Still, many improvements need to be made; these are described in [122; 123].  
The power electronics components like diodes and switches should have resistance to both high temperature and high levels of vibrations. Improvements are required in capacitors and further investigation on the use of dielectric materials. Simplification of inverters is needed to integrate electromagnetic interference. Optimization of power electronics devices with higher heat resistance and the size optimization are required to make them more suitable for the EVs.
# Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances
## 2 Related Work
LSTM is popularly used in time series prediction problems. However, the use of other types of Neural Networks is no less popular, such as the Autoregressive Network method developed by [PERSON] et al. [14] The research from which we derived the most inspiration is that of [PERSON] et al. Their solution focuses on the feature engineering process, which seeks to capture inter-case dependency features, as inspired by processes that run concurrently and access the same resource. Instead of manually performing feature engineering, we can obtain the inter-case dependency by utilizing chart images to predict the remaining time of the ongoing process, which is one of the main focuses of our research. In addition to research focusing on optimization of the hyperparameters mentioned earlier, the same researchers, [PERSON] et al., developed and updated their solutions by using one of the famous evolutionary algorithms, the Genetic Algorithm, an algorithm that mimics the evolutionary process. In Table 1, for ease of comparison, we summarize some of the related work on PBPM research. Of all the related work that we show, we focus on the problem of remaining-time prediction. We also focus on the latest research, narrowing down the literature to only three studies: those of [PERSON] et al. [13], [PERSON] et al. [15], and [PERSON] et al. [9]. The method by [PERSON] et al. [13] has several problems, such as the amount of time required for the test process and the lack of consideration of data attributes. These issues were handled by [PERSON] et al. [PERSON] et al. [16; 17] introduced the concept of inter-case dependencies that can be obtained manually from the feature engineering process. This method inspired us to propose a new method that utilizes deep learning and chart images to capture inter-case dependencies automatically. Thus, we expected our method to outperform the existing deep-learning method, namely data-aware LSTM (DA-LSTM) by [PERSON] et al. [15], which does not consider inter-case dependencies. Besides that, DA-LSTM also requires a significantly long time to be trained. One of the reasons is that it employs one-hot encoding, with the consequence that the dimensions of the encoding result are enormous, thus rendering it slow to train. Our proposed method also handles these issues. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Author** & **Method** & **Objective** \\ \hline [PERSON], [PERSON] and [PERSON] (2008) [6] &
\begin{tabular}{c} Non-parametric \\ Regression technique \\ \end{tabular} & Predicting the remaining time \\ \hline [PERSON], [PERSON] and [PERSON] (2011) [3] & Annotated Transition Systems & Predicting the remaining time \\ \hline [PERSON], [PERSON] and [PERSON] (2012) [7] & Predictive Clustering Tree & Predicting the remaining time \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of research in PBPM.
## 3 Materials and Methods
### Feature Extraction  
The following paragraphs outline the procedure for processing one subject's data. The same procedure was applied uniformly to all the subjects. Once the data were imported into MATLAB, only the sEMG signals from the electrodes of the first Myo (the one placed on the forearm) and the gesture labels were used, the latter indicating when and which grasp was performed in the sEMG signal (Figure 2).  
Once the sEMG signals for the 9 selected grasping postures were obtained, feature extraction was performed to create the dataset used for SVM training. The system is designed to operate in real-time with minimal delay: for an actual prosthesis, the time required for acquiring and classifying each signal should be under 300 ms [23]. With this in mind, a 250 ms window was chosen for signal acquisition, leaving the remaining 50 ms for classification. As previously mentioned, the Myo armband records sEMG signals at 200 Hz, meaning that in 250 ms it collects 50 data points, on which various features are evaluated. With fewer data points, classifier performance would degrade significantly.  
In total, 8 features were extracted: 6 from the time domain and 2 from the frequency domain. The selected time domain features include:  
\begin{table}
\begin{tabular}{c c} \hline
**Grasp** & **\%use** & **Color** \\ \hline Cyl & 9.4\% \\ Pinch & 36.9\% \\ LatP & 6.6\% \\ Obl & 11.9\% \\ Lum & 10.9\% \\ Hook & 2.3\% \\ intPP & 5.8\% \\ SpP & 5.7\% \\ nonP & 7.6\% \\ Not analyzed & 3\% \\ \hline \end{tabular}
\end{table}
Table 3: Distribution of daily frequencies of use of each grasping posture [6].  
Figure 2: sEMG signals from the eight channels during the CYL grasp (**left**). sEMG signal from a single channel during the CYL grasp, with corresponding labels below indicating the timing and type of grasp performed (**right**).  
* MAV: Mean absolute value;
* SD: Standard deviation;
* VAR: Variance;
* WL: Waveform Length;
* RMS: Root Mean Square;
* IEMG: Integrated EMG.  
The selected frequency domain features were:
* MF: Median Frequency;
* MPF: Mean Power Frequency.  
For a mathematical description of these features, we refer the reader to Appendix A. These features were selected based on their proven effectiveness in sEMG classification using an SVM [23], as well as their widespread use in the field of pattern recognition for myoelectric control. The full dataset was then divided into two parts: 70% of the data was used to train the SVM, while the remaining 30% was reserved for testing the classifier.
## 4 Dynamic Characteristics Analysis
### The Three Types of Dynamic Motions  
As Shown in Figure 18, if the IRW runs at 15 m/s and with no yaw suspension, the gravitational restoring moment plays an important role in the IRW's dynamics motion, resulting in derailment. If just a yaw damper is installed on the IRW, the derailment speed becomes slower. If just a yaw spring is installed on the IRW with a proper stiffness  
Figure 17: Lateral dynamics simulation results (V10-S10-C10, 100 s). (**a**) Lateral displacement; (**b**) yaw angle; (**c**) differential wheel rotational speed; (**d**) \(y-\psi\) phase diagram.  
coefficient, the gravitational restoring moment can be conquered. Then, under the effects of the gyroscopic moment, the hunting motion is generated. However, the mechanism of the hunting motion on the IRW differs from that on the SW, because the gyroscopic moment is much smaller than the longitudinal creep moment on the SW. Then, the self-guidance ability of the IRW is much lighter than that of the SW, and it is constrained by the yaw spring to some extent. Therefore, although the dynamic process presents as hunting motion, the long-time average lateral displacement presents as offset running. If both a yaw spring and a yaw damper are installed, the hunting motion can be suppressed. Then, the IRW can be suppressed into the steady convergent process and only presents as offset running. If the simulation duration is long enough, the lateral displacement can eventually converge to zero or nearly zero.  
The blue curve in Figure 19 demonstrates that when the IRW runs at a high speed, the derailment tendency caused by the gravitational restoring moment can be conquered by the gyroscopic moment, and the IRW's motion presents as hunting. Furthermore, the long-time average lateral displacement is close to zero. This phenomenon proves that if the speed is high enough, the gyroscopic moment can recouple the two wheels of the IRW, and then the self-guidance ability is generated. If just a yaw damper is installed, the hunting motion is suppressed, and the IRW can run along the center of the straight rail track. If just a yaw spring is installed, the self-guidance ability of the IRW is constrained to some extent, and the IRW's motion presents as hunting with offset running. If both a yaw spring and a damper are installed, the hunting motion can be suppressed, and the IRW's motion enters the steady convergent process after damped oscillations.  
Figure 18: \(y-\psi\) phase diagram (\(v=15\) m/s) with different yaw suspension configurations.
## 2 Proposed Scheme
### On Content Owner Side
#### 2.1.1 Preprocessing  
Similar to the related schemes, only OFF format 3D mesh files are discussed in this paper. The 3D mesh model \(M\) contains vertices and faces in the OFF file. The vertex set and the face set are represented by:  
\[V=\left\{v^{i}_{(x,\ y,\ z)}\middle|0<i\leq n_{V}\right\}, \tag{1}\]  
\[F=\left\{f^{j}\middle|0<j\leq n_{F}\right\}, \tag{2}\]  
where \(i\) and \(j\) are the indexes of vertex and face, and \(n_{V}\) and \(n_{F}\) are the numbers of vertex and face. The \((x,\ y,\ z)\) indicates the three-dimensional coordinate of each vertex, and each coordinate has 6 valid bits, i.e., is represented by a 32-bit floating point number. Each face contains three vertices and is not modified in this paper. Standing on the shoulders of giants, we compress the 3D coordinate of each vertex by preserving \(\sigma\) valid bits and transform it into an integer by:  
\[\psi^{i}_{(x,\ y,\ z)}=\left\lfloor v^{i}_{(x,\ y,\ z)}\times 10^{\sigma}\right\rfloor. \tag{3}\]  
Then, the integer coordinate can be used for further processes. The floating coordinate can be recovered after the rest processing by:  
\[\vartheta^{i}_{(x,\ y,\ z)}=\frac{\psi^{i}_{(x,\ y,\ z)}}{10^{\sigma}}. \tag{4}\]
## 3 Reliability Modeling of PV Systems  
Developing a generation model of the overall system generation including the output power of the PV system and conventional generation is required. In this step, the failure rates of critical components in the PV system, such as capacitance and switching, are taken into consideration. Secondly, the developed generation model is then combined with an hourly load model to evaluate system adequacy as shown in Figure 1. In this work, the central, string and micro PV inverter system topologies and the output power of solar cell are taken into consideration in reliability modeling of overall PV system.  
Section three presents a clear framework for developing an overall reliability model of PV system. There are three types of system-level reliability models: part-count, combination and state-space models. Part-count is utilized in this project since this model can provide adequate reliability estimation. Three assumptions are taken into account to apply this model.  
Figure 3: Evaluation of effective load carrying capability (ELCC).  
1. The overall system will fail if any component or subsystem fails.
2. The failure rate of each component remains constant during lifetime.
3. The overall system is modelled as a series reliability block network, as shown in Figure 4.  
The probability of an up (\(P_{up}\)) and down (\(P_{Down}\)) state system model can be evaluated using Equations (6) and (7), respectively. This section is divided into three subsections.  
\[P_{up}=\frac{\mu_{1}}{\mu_{1}+\lambda_{1}}*\frac{\mu_{2}}{\mu_{2}+\lambda_{2} }*\frac{\mu_{3}}{\mu_{3}+\lambda_{3}}\times........\times\frac{\mu_{n}}{\mu_{n} +\lambda_{n}} \tag{6}\]  
\[P_{Down}=1-P_{up} \tag{7}\]
## 3 System Model and Symbol Definitions
### mMTC Architecture  
The 3 rd Generation Partnership Project (3 GPP) proposes a system architecture for MTC over the LTE network [38]. The MTC applications are hosted by application servers (MTC ASs) for task management and connected to the core network (CN) through the service capability server (SCS). Applications come with a set of required tasks to be executed by devices before the time expires. The actions of MTC devices are triggered by a message from MTC AS. After authorization checking, the MTC interworking function (MTC-IWF) relays triggering messages from SCS to MTC devices. When the cellular IoT (CIoT) device receives the triggering request message, it checks the ability to complete the requested tasks. If an MTC device has the ability, a packet data network (PDN) connection with the CN is created to perform the desired transmissions, and the tasks are executed by the MTC device during the scheduled period. Each device triggered by an MTC server is followed by several signaling messages exchanged on the network.  
The MTC applications are hosted by application servers (MTC ASs) for task management and scheduling and are connected to the core network (CN) through the service capability server (SCS). After authorization checking, the MTC interworking function (MTC-IWF) relays trigger requests from SCS to MTC devices. If an MTC device can execute the requested tasks, a packet data network (PDN) connection with the CN is created to perform the desired transmissions. Each application comes with a set of requirements, and the corresponding tasks have to be executed before the time expires [38].  
Therefore, the main high-level MTC operation players are applications, tasks, and devices. For massive deployment, recurring MTC applications are one of the main types. In addition, energy efficiency is one of the problems in the massive access scheme. Reducing signaling and saving energy are the two main topics of this paper. We adopt the grouping mechanism, sharing the same ID in a group, to reduce the signaling overhead and use the power-saving mode (PSM) to reduce power consumption.
arc systems along the southern limb, which is characterized by the occurrences of Early or Middle to Late Ordovician arc-related rocks in the Chinese West Tianshan [4; 5; 6]. The Late Paleozoic arc systems were built on the margin of the Kazakhstan microcontinent, and mainly contain the Balkhash-Yili arc (BY) and Devonian Volcanic Belt (DVB). The BY is mainly composed of Late Devonian to Carboniferous volcanic rocks and its southeastern part extends to the Yili Block (YB) [1]. The DVB, a Japanese-type volcanic arc, is mainly composed of Early to Middle Devonian plutonic and volcanic rocks [7; 8] and the eastward extension of its southeastern part is still unclear. Additionally, the geodynamic mechanism for the formation of the Late Paleozoic arc magmatism is unclear, especially whether it has a continuous subduction of the Junggar-Balkhash Ocean with trench retreating [9] or not [10; 11]. Bodies of Middle Ordovician to Carboniferous magmatites outcrop in the north margin of the YB, which was attributed to the southward subduction of the Junggar (or North Tianshan) oceanic crust beneath the YB [4; 9; 11; 12; 13; 14; 15; 16]. The Middle to Late Ordovician [4; 5; 17; 18] and the Middle to Late Devonian [11; 13; 19; 20] arc-type magmatism were both reported in the northern margin of YB. However, during the Caledonian and Hercynian orogenic periods, whether a magmatic gap occurred in the Late Silurian-Early Devonian or not is little known and their origin and tectonic implication are unclear in the northern margin of YB. The petrologic features of the sedimentary sequence shows that the northern margin of the YB was an active continental margin during roughly the whole Paleozoic, although the tectonic regime changed in the Paleozoic [21], while other authors consider that there was a collision orogenic events in the Silurian or early Devonian periods [22]. Furthermore, this tectonic setting, i.e., an active continental margin, probably still existed in the Late Silurian, evidenced by coeval sedimentary rocks deposited in the back-arc basin showing low-maturity source area with weak weathering degree and near-source rapid accumulation [23]. However, the status in the Early Devonian is puzzled due to the absence of Early Devonian strata in the northern margin of YB. Thus, the contemporaneous magmatic of the northern margin of YB is key to understanding the tectonic setting during that time.  
Here, we report newly identified Early Devonian rhythmic rocks in the Haerdaban area, northern margin of the YB. We obtained whole-rock major and trace element and Sr-Nd isotopic compositions, as well as zircon U-Pb age and Hf isotopic compositions of Early Devonian volcanic rocks. This new dataset enables us to examine the exact age, origin and tectonic setting of the volcanic rocks. Our results indicate that a continuous arc-related magmatism occurred in the early Devonian and thus we argue that there is a late Silurian-early Devonian magmatic gap in the northern margin of the YB.