esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Tests
### Results and Discussion
#### 2.2.2 Discussion  
Effect of Reinforcing Material  
The reference pile (PREF) and the CFRP pile (PCFRP1000) had the same reinforcement ratio and initial prestressing level. However, the CFRP had a much higher tensile strength than the 1420 MPa steel, which resulted in a different failure mode and much higher ultimate flexural capacity. As the load increased, the tensile stress on the bottom  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimen** & **Cracking Load** & **Ultimate Load** & **Deformation at Failure (mm)** & **Ultimate Flexural Capacity (kN-m)** & **Failure Mode** \\ \hline PREF & 27.6 & 71 & 120 & 95.3 & Steel rupture \\ PCFRP1000 & 57.0 & 145 & 107 & 188.4 & Concrete crushing \\ PCFRP1500 & 86.5 & 180 & 101 & 233.4 & Concrete crushing \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristic loads and failure mode.  
Figure 9: Load–deflection diagram.  
reinforcement increased. For the PREF specimen, as the tensile stress on the steel reached 1420 MPa, very limited strength could be further developed. However, for the specimen PCFRP1000, the CFRP continuously provided increasing tensile resistance until crushing of the top concrete, which indicated that the compressive strength of the concrete was fully developed. There is a still high possibility that the flexural capacity could be further improved if higher-strength concrete was adopted, as the strength of the CFRP was not fully developed. It can be concluded, for PHC piles, that high-strength reinforcing material tends to make more use of the concrete strength and significantly improve the flexural capacity.  
Effect of Prestressing Level  
The pile PCFRP1000 and the pile PCFRP1500 were reinforced with the same CFRP material, and had the reinforcement ratio. The CFRP rods in these two specimens were pretensioned to prestressing levels of 1000 MPa and 1500 MPa, respectively. The higher prestressing level provided more compressive stress surplus on the bottom concrete, which significantly postponed the occurrence of cracking. As mentioned, the CFRP strength of pile PCFRP1000 was not fully developed, and the increased prestressing level helped develop the tensile strength of the CFRP, which resulted in a higher ultimate capacity on PCFRP1500.
## 2 Materials and Methods  
The study was conducted following the Declaration of Helsinki and with the approval of the Ethics Committee of a tertiary medical centre. We included 23 patients with CIs. All patients had a slim straight electrode inserted through the round window into the scala typpani by the same surgeon, allowing the greatest possibility for mutual comparison. The depth of the electrode insertion was measured radiographically as degrees of angular rotation within the cochlea. The duration of the electrode insertion was consistently 2 min in 52.2% and less than 2 min in 47.8% of patients. In all cases, the lumen of the cochlea was checked on CT and MRI preoperatively, and the choice of inserting a slim straight electrode in patients with otsclerosis was made when the lumen of the cochlea was not affected by otsclerosis.  
Questionnaires that were given to the patients at home were used to collect demographic data. We acquired the intraoperative NRT measurements, preoperative and postoperative tone audiometry thresholds measured without additional hearing aids, together with tone and speech audiometry measurements with an implanted CI. Postoperative measurements were performed approximately 6 months after the cochlear implantation.  
All data were collected and organized in Microsoft Office ExcelTM version 2103 (16.0.13901.20400). It was then exported to IBM SPSSTM version 28.0.0.0. For the NRT data set, we calculated and used the average value of all 22 electrode thresholds in microvolts. Some studies measured NRT on specific electrodes, which varied from case to case. Consequently, we decided to take the average of all NRT electrode thresholds for better comparison. We also compared preoperative and postoperative tone audiometry thresholds to assess the level of postoperative residual hearing preservation.  
Nominal variables are presented with frequencies (percentages) and numerical variables with mean \(\pm\) standard deviation or median (95% CI) when the normality assumption was violated. Normality was tested using the Shapiro-Wilk normality test. The Pearson correlation coefficient was calculated for normally distributed variables and the Spearman correlation coefficient for non-normal distributions. The Fisher exact test, Student \(t\)-test, and Mann-Whitney U test were used in the comparison of otsclerosis and SNHL groups.
## 3 Results and Discussion
### Model Establishment and Significance Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Y1: TSS/TA Ratio (\%)**} & \multicolumn{6}{c}{**Y2: Sensory Evaluation (Points)**} \\ \hline
**Source** & **Quadratic Sum** & **Mean Square** & **\(F\)** & **\(p\)** & **Quadratic Sum** & **Mean Square** & **\(F\)** & **\(p\)** \\ \hline Model & 51.16 & 3.65 & 61.74 & \textless{}0.0001 ** & 1701.2 & 121.51 & 50.99 & \textless{}0.0001 ** \\ A-Homogenisation & 0.015 & 0.01 & 0.25 & 0.6260 & 19.38 & 19.38 & 8.13 & 0.0128* \\ B-Ultersonication & & & & & & & & \\ time & 0.33 & 0.33 & 5.65 & 0.0323 * & 25.83 & 25.83 & 10.84 & 0.0053 ** \\ C-Sterilisation & & & & & & & & \\ temperature & 0.90 & 0.9 & 15.14 & 0.0016 ** & 169.95 & 169.95 & 71.32 & \textless{}0.0001 ** \\ D-Sterilisation time & 0.34 & 0.34 & 5.8 & 0.0304 * & 193.2 & 193.2 & 81.08 & \textless{}0.0001 ** \\ AB & 0.06 & 0.06 & 0.93 & 0.3505 & 24.01 & 24.01 & 10.08 & 0.0068 ** \\ AC & 0.72 & 0.72 & 12.21 & 0.0036 ** & 1 & 1 & 0.42 & 0.5276 \\ AD & 0.14 & 0.14 & 2.38 & 0.1455 & 13.14 & 13.14 & 5.51 & 0.0341* \\ BC & 0.20 & 0.2 & 3.35 & 0.0887 & 112.76 & 112.76 & 47.32 & \textless{}0.0001 ** \\ BD & 0.12 & 0.12 & 2.07 & 0.1722 & 6.5 & 6.5 & 2.73 & 0.1208 \\ CD & 0.03 & 0.03 & 0.43 & 0.5214 & 22.56 & 22.56 & 9.47 & 0.0082 ** \\ A2 & 0.37 & 0.37 & 6.32 & 0.0247 * & 18.05 & 18.05 & 7.57 & 0.0156 * \\ B2 & 0.53 & 0.53 & 8.91 & 0.0098 ** & 136.62 & 136.62 & 57.33 & \textless{}0.0001 ** \\ C2 & 27.47 & 27.47 & 464.15 & \textless{}0.0001 ** & 458.32 & 458.32 & 192.34 & \textless{}0.0001 ** \\ D2 & 20.55 & 20.55 & 347.21 & \textless{}0.0001 ** & 386.72 & 386.72 & 162.29 & \textless{}0.0001 ** \\ Residual error & 0.83 & 0.06 & & & & & & \\ Lack-of-fit & 0.62 & 0.06 & 1.16 & 0.4801 & 25.84 & 2.58 & 1.37 & 0.4069 \\ Error & 0.21 & 0.05 & & & 7.52 & 1.88 & & \\ Total Sum of Squares & 51.99 & & & & & & & \\ \hline \hline \end{tabular} Note.* * indicates a significant difference (\(p\) < 0.05); ** indicates an highly significant difference (\(p\) < 0.01); \(R^{2}\) = 0.9861; \(R^{2}\)Adi = 0.9721.  
\end{table} 
Table 7: Table of Variance of Regression Model. had a significant effect on the sensory evaluation (_p_ < 0.05). The influence of each factor on the sensory evaluation score was in the order: D (sterilisation time) > C (sterilisation temperature) > B (ultrasonication time) > A (homogenisation time). Through optimisation, the parameters yielding the maximum sensory score are homogenisation time of 2.83 min, ultrasonication time of 2.95 min, sterilisation temperature of 105.46 \({}^{\circ}\)C, and sterilisation time of 89.95 s, resulting in a predicted sensory score of 87.6536 points and an actual value of 87.65 points.
# Uplink Transmit Power Control for Single-Carrier Grouped FDMA with Iterative Multiuser Detection
## 3 Proposed Power Control for SC-GFDMA
### Proposed Power Control Algorithm  
The basic strategy of the proposed power control algorithm is to make the signal powers of the users in chunk \(m\) satisfy the relation of the geometric sequence. That is, the algorithm checks the inequality \(\zeta_{u_{k},m}\geq a_{U_{m}}\cdot\gamma^{U_{m}-k}\) for each user \(u_{k}\) in the decreasing order of \(k\). If \(\zeta_{u_{k},m}\geq a_{U_{m}}\cdot\gamma^{U_{m}-k}\), it decreases the signal power \(\zeta_{u_{k},m}\) as  
\[\zeta_{u_{k},m}=a_{U_{m}}\cdot\gamma^{U_{m}-k}. \tag{54}\]  
Conversely, if \(\zeta_{u_{k},m}<a_{U_{m}}\cdot\gamma^{U_{m}-k}\), the proposed method considers that \(u_{k}\) is not suitable to be successfully detected at chunk \(m\), and hence, the algorithm excludes \(u_{k}\) from the user set \(\mathcal{U}_{m}\) of chunk \(m\). That is, if \(\zeta_{u_{k},m}<a_{U_{m}}\cdot\gamma^{U_{m}-k}\), update \(\mathcal{U}_{m}\) and \(U_{m}\) to \(\mathcal{U}_{m}-\{u_{k}\}\) and \(U_{m}-1\), respectively.  
**Step 1. Initialization**  
Initialize the subcarrier chunk index: \(m=1\).  
Initialize two indices: \(n=U_{m}\) and \(k=U_{m}\).  
Set \(P_{u,m}=1,\ \forall u\), and \(\mathcal{U}_{m}=\{u\ |\ \zeta_{u,m}\geq\Gamma_{\mathit{th}}\sigma_{N}^{2}\}\).  
**Step 2. Power control**  
1. Define \(p=\Gamma_{\mathit{th}}\sigma_{N}^{2}\cdot\frac{\gamma^{U_{m}-n}}{\Gamma_{u_{ k},\text{am}[\Lambda_{\mathit{th}},m}]^{2}}\).  
**If \(P_{u_{k},m}\geq p\), set \(P_{u_{k},m}=p\) and update \(n\gets n-1\) else update user set: \(\mathcal{U}_{m}\leftarrow\mathcal{U}_{m}-\{u_{k}\}\)**
2. **If \(k>1\), update \(k\gets k-1\) and go to Step 2-1) else go to Step 3.**  
**Step 3. User scheduling**  
Perform the pattern-aware user scheduling in [23] by considering \(\mathcal{U}_{m}\) as the input (initial) set.  
**Step 4. Decision of termination**  
If \(m<M\), set \(m\gets m+1\) and go to Step 2. Otherwise, terminate the algorithm.
# Principles and Characteristics of Different EDM Processes in Machining Tool and Die Steels
## EDM Process Details
### Various EDM Process Variants
#### 2.4.2 Wire EDM  
In wire EDM, a continuously moving metallic wire of small diameter follows a well-defined path to cut a workpiece. Discrete sparks between the wire and the workpiece cause erosion in the machining area. As in sinker type EDM, the wire and workpiece do not have any contact during machining in wire EDM. Furthermore, both of them should be immersed in dielectric fluid. The wire used in this process is usually thin in diameter, as small as 0.1 mm or so; it is generally made of copper, brass, coated steel materials, or even metals like molybdenum. A high peak current with a short duration time is applied in this process. The machine control unit enables the necessary movement through a computer numerical control (CNC) system to cut complicated shapes [95]; it contains a dedicated microprocessor to maintain the gap within a suitable range (25-50 \(\upmu\)m). In addition, the unit controls the speed of the wire through the workpiece, which produces surfaces with very high accuracy. De-ionized water is a common dielectric fluid used in this process. The wire EDM process has a wide range of applications, such as in die making, medicine, electronics, and the automotive industry [50]. Figure 3 shows a schematic diagram of wire EDM.
## 3 Mathematical Model
### Motion Equations
Equation (1) is used to predict the maneuvering motion. The direction (\(\psi\)) and position (\(x_{0},y_{0}\)) of the vessel in space are also essential information. Equation (4) is also considered along with Equation (1). \[\left\{ \begin{aligned} \dot{x_{0}}&=u\cos\psi-v\sin\psi\\ y_{0}&=u\sin\psi+v\cos\psi\\ \dot{\psi}&=r\end{aligned} \right. \tag{4}\]  
Next, the forces acting on the hulls, the thrust of the propeller, and the wind load on the superstructure and hulls individually will be explained.
# Incorporating New Technologies in EEIO Models
## 1 Introduction
There are also examples for the Canadian economy, adding four new industries and eight new commodities [16], for the Chinese economy adding bio-ethanol and analyzing 28 different scenarios [17], for the Australian economy [18] adding a biofuel sector that substitutes a % of petrol demand and for the U.S. economy incorporating cellulosic ethanol [19]. The Canadian and Australian cases analyzed the economic and employment impacts. The Chinese and United States cases analyzed the economic, social and environmental impacts. The previous literature has concentrated on specific case studies for specific technologies. Here, we contribute to the current literature by proposing a generalized methodology to assess the environmental impacts of a new product, being produced by one or multiple new technologies, that substitutes for a product in the current economy. The proposed methodology is innovative in several ways. First, it is applicable for any new product for which the perfect substitution assumption, with an existing product in the economy, is reasonable. More about this assumption is given in Appendix B. Second, it can be applied to both rectangular and square input-output matrices. To the best of our knowledge, this is the first time a rebalancing methodology for rectangular matrices has been proposed. This makes the augmentation methodology more flexible and realistic considering that several countries have rectangular input-output accounts. Third, it can be computationally implemented in any existing EEIO model. Fourth, the new product can be produced by one or multiple new technologies, allowing for flexibility in the type of questions that can be analyzed with the augmented model results. In the results section, two different perspectives on how the model can be used are presented. The methodology is based on a matrix augmentation hybrid IO approach [8], which adds a new product and \(k\) new technologies that can produce it. The methodology includes the augmentation process and a new method to rebalance modified rectangular Make and Use tables. The methodology assumes that a product in the current economy will be perfectly substituted on a percentage basis by the new product (This assumes that the price and usability of the new product will be exactly the same as the price of the product it is replacing. If not, the amount substituted will respond to market interactions, and therefore the % substitution would not be coherent). The paper is organized as follows. Section 2 describes the methodology. Section 3 describes the case study of the application to advanced biofuels using the US Environmentally Extended Input-Output model (USEEIO). Section 4 presents the results for the case study. Finally, Section 5 presents our discussion.
# An Intelligent Access Channel Algorithm Based on Distributed Double Q Learning
## 5 Conclusions  
In this paper, in order to solve the problem of channel conflicts and external malicious jamming caused by random access in wireless communication networks, we derive a throughput optimization problem to find the optimal selection of subframes and channel policies for each user. In order to solve this optimization problem, a distributed double Q learning algorithm is proposed. Each user learns the subframe and channel selection according to the received congestion level using two Q-learning, respectively. Compared with the existing algorithms, the convergence performance and robustness of the proposed scheme are improved.  
Conceptualization, G.Z. and PL.; methodology, Y.N.; software, Y.N.; validation, Y.L. and Y.N.; formal analysis, G.Z.; resources, G.Z.; data curation, Y.L.; writing--original draft preparation, Y.N.; writing--review and editing, PL. and Y.L.; visualization, Y.N.; supervision, G.Z. and L.Z.; project administration, L.Z.; funding acquisition, G.Z. All authors have read and agreed to the published version of the manuscript.  
This research is supported by National Science Foundation of China under contract No. U19B2014.  
Not applicable.  
The data presented in this study are available from the author, G.Z., upon reasonable request.  
The authors declare no conflict of interest.  
Figure 5: Relationship between normalized throughput and iteration times.
# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 2 Materials and Methods
### The Model of Short-Term Traffic State Prediction
#### 2.3.3 Temporal Feature Extraction  
The variation in traffic over time is usually nonlinear and unstable. LSTM model, as the most extensive network, is used to deal with time series-related problems. The GRU is proposed based on LSTM. Compared with LSTM, the GRU model has a simpler structure, smaller parameter values and faster training speeds. The GRU model can effectively deal with short-term information with gated mechanism for different task processes.  
As shown in Figure 7, the structure of GRU contains an update gate \(z_{t}\), a rest gate \(r_{t}\), and a memory unit \(h_{t}\). The reset gate is used to control the degree of ignoring the status information of the previous moment. The update gate is used to control the state information of the previous moment, so that the useful information can be brought into the current state.  
The states of update gate, rest gate, and memory unit are defined as follows:  
\[z_{t}=\sigma(\mathbf{W}_{z}\cdot[h_{t-1},x_{t}]) \tag{7}\]  
\[r_{t}=\sigma(\mathbf{W}_{r}\cdot[h_{t-1},x_{t}]) \tag{8}\]  
\[\widetilde{h}_{t}=\tanh(\mathbf{W}_{h}\cdot[r_{t}\otimes h_{t-1},x_{t}]) \tag{9}\]  
\[h_{t}=(1-z_{t})\otimes h_{t-1}+z_{t}\otimes\widetilde{h}_{t} \tag{10}\]  
where \(x=\{x_{1},x_{2},\dots,x_{t}\}\) is the output of the GCN model, which has extracted spatial features of the traffic data. \(\mathbf{W}_{z}\), \(\mathbf{W}_{r}\) and \(\mathbf{W}_{h}\) represent the weight matrixes of \(z_{t}\), \(r_{t}\) and \(h_{t}\) respectively. \(\sigma(\cdot)\) and \(\tanh(\cdot)\) are the two activation functions, which can be defined as  
\[\sigma=\frac{1}{1+e^{-x}} \tag{11}\]  
\[\tanh=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{12}\]  
In summary, the GCN model is used to extract the topological structure of intersection for obtaining spatial features. The GRU model is used to extract the dynamic variation of traffic state in the network for obtaining temporal features.
## 6 Conclusions  
In this paper, we presented a server-client-based online ASR system employing a BLSTM AM, which is a state-of-the-art AM. Accordingly, we adopted a CSC-based training and decoding approach for a BLSTM AM and proposed the following: (a) the parallel acoustic score computation to support more clients concurrently and (b) DNN-VAD to reduce the waiting time for a user to receive the recognition results. On the designed server-client-based ASR system, a client captures the audio signal from a user, sends the audio data to the ASR server, receives a decoded text from the server, and presents it to the user. The client can be deployed in various devices, from low to high performance. On the other hand, a server performs speech recognition using high-performance resources. That is, the server manages the main thread and decoder thread for each client and an additional worker thread for the proposed parallel acoustic computation method. The main thread communicates with the connected client, extracts speech features, and buffers them. The decoder thread performs speech recognition and sends the decoded text to the connected client. Speech recognition is performed in three main steps: acoustic score computation using a CSC BLSTM AM, DNN-VAD to detect a short pause in a long continuous utterance, and Viterbi decoding to search an optimal text using an LM. To handle more concurrent clients in real time, we first proposed the acoustic score computation method by merging the speech feature vectors collected from multiple clients, to reduce the amount of data transfer between the CPU and GPU, and calculating the acoustic scores with the merged data to increase GPU parallelization and to reduce the transfer overhead between the CPU and GPU. Second, we proposed DNN-VAD to detect a short pause in an utterance for a low latency response to a user. The Korean ASR experiments conducted using the broadcast audio data showed that the proposed acoustic score computation method increased the maximum number of concurrent clients from 22 to 44. Furthermore, by applying the frame skip method during Viterbi decoding, the maximum number of concurrent clients was increased to 59, although SyllER was degraded from 11.94% to 12.53%. Moreover, the average user-perceived latencies were reduced to 5.41 and 3.09 s with a minibatch of 200 frames and 100 frames, respectively, when the proposed DNN-VAD was used.  
The authors discussed the contents of the manuscript. Methodology, validation, writing, editing, and formal analysis, Y.R.O. and K.P.; project administration, J.G.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute for Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No.2019-0-01376, Development of the multi-speaker conversational speech recognition technology).  
The authors declare no conflict of interest.  
Figure 8: Histogram of the user-perceived latency for all words in test set 1. (a) Without VAD (b) WithVAD, minibatch 200 (c) WithVAD, minibatch 100.
# Timing Performance Testing and Regularity Analysis of eLoran System
## 2 eLoran Timing Error
### Authorization Performance Assessment Metrics
#### 2.2.2 Minimum Timing Deviation  
The minimum value of the timing deviation was monitored during the valid data period and indicated the lowest deviation in the eLoran timing system. The maximum and minimum values reflect the range of variation in the UTC obtained by the user. To illustrate the dispersion of the timing deviations, this study uses extreme deviation (the difference between the maximum and minimum values) to represent the fluctuation range of the timing deviation.
# Research on Characteristics of Flow Noise and Flow-Induced Noise
###### Abstract  
The noise control of flank array sonar is a primary approach to enhance the sonar detection range. During submarine navigation, hydrodynamic noise is the main noise source in the platform region of the flank array sonar, which includes flow noise and flow-induced noise. Therefore, an in-depth investigation of hydrodynamic noise is necessary. In this paper, we firstly take the teardrop submarine as a computational model to validate the computational method. Afterwards, we numerically simulate the flow and flow-induced noise characteristics for the cylindrical shell model, and investigate differences in noise at different detection points along the X, Y, and Z axes. Finally, experiments are conducted to confirm the accuracy of the simulation results. The research findings reveal that, at the same frequency, flow-induced noise exceeds flow noise, and the noise decreases as the distance between the walls of the cylindrical shell increases. The experimental and simulation results are consistent, suggesting that the selected computational method can precisely simulate the submarine's noise.  
flow noise; flow-induced noise; fluid-structure interaction; hydrodynamic noise; numerical simulation +
Footnote †: journal: Applied