esa-sceva/satcom-chunk-collection huggingface dataset:
# The Development and Evaluation of a Training Monitoring System for Amateur Rugby Union
## 4 Discussion
### Subjective Measures of Monitoring Training  
As described in the methods section, justification for the measures included in the TMS was based largely on previously published research [1; 4; 5; 31; 34; 39]. This study gives further support to the use of subjective measures as a method of monitoring training [1; 4]. Lack of consistency of data collection aside, four S&C coaches found value in the pre-session measures on a coloured five-point Likert scale as a method of starting conversations with the players and, importantly, knowing which players in particular to prioritise speaking with in order to get more context around their score. Due to the external confounders associated with amateur-level athletes (e.g., their occupation), having this information will inform the preparedness of the player for the upcoming training session or match. From the coaches' perspective, sRPE ranked highest in order of importance to both their training prescription practices and attempts to reduce injury risk throughout the season followed by muscle soreness and sleep duration (Table 5). This is consistent with the results of the question regarding the streamlining of the system as these three variables again received the greatest selection. Similarly, the players ranked muscle soreness most useful to both their own training/match preparation and to reducing their risk of injury. However, in contrast, fatigue ranked second and sRPE and RTT were ranked next. Coaches saw less value in the fatigue and RTT measures compared to the players. Neither players nor coaches ranked the mood or sleep quality measures highly. Although all seven measures are validated based on previous research [1; 4; 5; 31; 34; 39], the results of this study suggest that muscle soreness, sRPE, fatigue, sleep duration, and RTT may be more valuable in a practical setting.  
A monitoring system should be as efficient and succinct as possible to reduce player and data analysis burden and in turn increase the consistency of the data gathered [6]. Consequently, only measures that will inform the coach's training practices regularly should be included in the system. Its first-place ranking in relation to both training prescription and design and injury risk (Table 5) combined with the S&C coaches' representative quotes (Table 2) further demonstrates the usefulness of sRPE as an effective monitoring tool [4; 14; 44].
###### Abstract  
This paper proposes an adaptive fuzzy controller based on a switching notch filter to address the rotor unbalance vibration control problem of an active magnetic bearing (AMB) high-speed motor system in the full rotational speed range. Aiming at the complex nonlinear and time-varying characteristics of the AMB rigid rotor system, this study designs an adaptive fuzzy controller (AFC) that obtains fuzzy quantities by blurring the rotor vibration information and vibration rate of change as the input signals and then obtains the fuzzy set through fuzzy reasoning and modifies the parameters of the initial fuzzy controller. The initial fuzzy controller parameters are modified through fuzzy reasoning to improve the control effect and ensure the stable suspension of the rotor during high-speed rotation. At the same time, in order to effectively suppress the vibration of the rotor in high-speed operation due to unbalance and other factors, this paper introduces an adapting notch filter (ANF) as a vibration control strategy on the basis of AFC, and the notch filter is able to monitor the rotor vibration signals and adaptively adjust the center frequency and bandwidth. Finally, the correctness and effectiveness of the adaptive fuzzy controller based on a switching notch filter (AFC-ANF) are verified via simulations and experiments. The simulation results demonstrate that compared to traditional PID control, the AFC reduces the response time by 0.11 s. Under constant-speed operating conditions, the AFC-ANF strategy decreases rotor vibration by 60%, while under variable-speed conditions, it reduces rotor vibration displacement by 40%, showcasing significant vibration suppression effectiveness. This research provides a novel solution for vibration control in magnetic bearing systems, offering both important theoretical significance and practical application value.  
active magnetic bearings (AMBs); adaptive fuzzy controller (AFC); adapting notch filter (ANF); vibration control +
Footnote †: journal: Applied sciences  
0000-0001-5000-5000][PERSON]  
1 School of Electrical and Control Engineering, Shaarxi University of Science & Technology,  
Xi'an 710021, China; [EMAIL_ADDRESS] (W.L.); [EMAIL_ADDRESS] (J.C.)  
[PERSON]  
2 School of Automation, Northwestern Polytechnical University, Xi'an 710129, China; [EMAIL_ADDRESS] 2
## 2 Materials and Methods  
InGaN green micro-LEDs with a size of 80 \(\upmu\)m \(\times\) 80 \(\upmu\)m were fabricated from commercial III-nitride LED wafers grown on a c-plane sapphire substrate by metalorganic chemical vapor deposition (Xiamen Changlight Co., Ltd., Xiamen, China). The LED had a typical p-i-n structure with an u-GaN buffer layer, n-GaN layer, InGaN/GaN multiple quantum wells (MQWs), and a p-GaN layer. Photolithography and inductively coupled plasma (ICP) etching, with SiO\({}_{2}\) as a hard mask, were performed to define the mesa structure [31]. Then, current spreading layer (CSL) was deposited with a 100 nm indium-tin-oxide (ITO)  
Figure 1: Underwater attenuation at different wavelengths.  
Figure 2: Illustration of underwater vehicles using total reflection communication links at the air-water interface.  
layer by sputter. In order to achieve ohmic contact between CSL and p-GaN, rapid thermal processing (RTP) was used in an atmosphere of nitrogen and oxygen. A Ti (20 nm)/Al (100 nm)/Ti (20 nm)/Au (50 nm) stack layer was evaporated onto the CSL and n-GaN as the p-electrodes and n-electrodes by E-Beam Evaporator. The pictures of the 80 \(\upmu\)m \(\times\) 80 \(\upmu\)InGaN-based micro-LED are shown in Figure 3.  
Figure 4 demonstrates the UWOC system using an InGaN-based micro-LED light source and non-return-to-zero on-off keying (NRZ-OOK) modulation. At the transmitter end, the micro-LED was driven by a bias tee (mini-circuits ZFBT-6 GW+) that combines a direct current (DC) from KEYSIGHT B2902A with pseudo-random binary sequences (PRBS) from Agilent J-BERT E8403A. The emission light was collimated by a plano-concave lens and then transmitted through a water tank of dimensions 1.8 m \(\times\) 0.5 m \(\times\) 0.3 m. In the water tank, we achieved the communication links by utilizing the total internal reflection at the air-water interface. At the receiver end, the optical output signal was focused by a Convex lens and then captured by a 100 MHz bandwidth photodetector (Hamamatsu C12702-11). The distance between the light source to the lens, the lens to tank entrance, tank length, tank end to collection lens, and collection lens to the detector were 1 cm, 10 cm, 180 cm, 10 cm, and 4 cm, respectively. The electrical output signal of the detector was amplified by the power amplifier (mini-circuits ZFHL-6A-S+) and then tested by Agilent E8403A error detector to obtain BER. In this proposed UWOC system, a wave generator (SCP-150M) was used to achieve waves. Its power consumption is 50 W at maximum. It can make square waves or sine waves by changing the frequency and the magnitude.  
Figure 4: Experiment setup of the proposed UWOC system based on an 80 \(\upmu\)m \(\times\) 80 \(\upmu\)InGaN-based Micro-LED: (**a**) schematic diagram, (**b**) transmitter side, (**c**) image of the proposed UWOC system based on a 80 \(\upmu\)m \(\times\) 80 \(\upmu\)InGaN-based MFicro-LED, (**d**) receiver side.  
Figure 3: Image of the 80 \(\upmu\)m \(\times\) 80 \(\upmu\)InGaN-based Micro-LED: (**a**) schematic diagram, (**b**) micro-scope image.
# A Survey on Fault Diagnosis of Rolling Bearings
## 3 Rolling Bearing Fault Detection
### Filter-Based Fault Detection Methods  
The filter-based detection method is to construct a narrow-band filter to remove the noise and interference components from the bearing vibration signals and retain the fault-related pulses. The key to the filter-based method is to determine the center frequency and bandwidth of the narrow-band filter. A typical filter-based detection method is Kurotgram, proposed by [PERSON] et al. [39; 40] in 2006, which uses the bandpass filter of a tree structure to divide the signal spectrum and then calculates the time-domain kurtosis of the filtered signal as a measure of fault information to adaptively select a narrow-band signal with the most fault information for subsequent analysis. The Kurotgram method has two shortcomings: one is that the parameters (center frequency and bandwidth) of the constructed filter are not accurate enough; the other is that the Kurtosis index is easily disturbed by noise, resulting in interference with the selection of the optimal filter. For this reason, [PERSON] et al. [41] and [PERSON] et al. [42] performed a wavelet packet transform on the bearing signal and used each wavelet node as a narrow-band filter to replace the tree structure filter of Kurotgram and proposed two new indicators for evaluating the fault information in the filtered signal, i.e., power spectral kurtosis and power spectral sparsity, to select the optimal filter. Similarly, [PERSON] et al. [43] and [PERSON] et al. [44] used the dual-tree complex wavelet transform and the maximum overlapping discrete wavelet packet transform to generate a series of narrow-band filters, respectively. In addition, many improvements to Kurotgram focus on proposing new evaluation indexes to replace kurtosis, such as spatial spectrum set kurtosis [43], envelope spectrum correlation kurtosis [45], 12/11 norm [46], negative entropy [47; 48], Gini index [49], and weighted cyclic harmonic noise ratio [50], to avoid the wrong selection of filters in the case of excessive non-Gaussian noise or accidental impact.  
Although Kurtogram and its improved methods can remove fault-independent noise and harmonic interference from vibration signals, there is still a problem with the accuracy of filter construction, which may lead to the loss of the signal information and affect the extraction of fault-related pulses. As opposed to the traditional method of dividing the frequency band layer by layer, the optimal wavelet filter methods are proposed [51; 52; 53; 54; 55]. [PERSON] et al. [51] used the Morlet wavelet as the filter, took maximizing sparsity of the filtered signal as the objective, and applied a genetic algorithm (GA) to locate the center frequency and bandwidth of the optimal Morlet wavelet for automatic filter construction. Similarly, [PERSON] et al. [52] utilized the asymmetric real Laplace wavelet as the filter and determined its center frequency and bandwidth by simultaneously maximizing the impulse and cyclostationary characteristics of the filtered signal.
## 3 Results  
### Quantitative Characteristics and Analysis of Grain Size Parameters  
The grain size distribution of modern flood sediments in the lower Yellow River exhibited the following characteristics: According to the GM, the sediment grain sizes were relatively fine, with the mean grain size ranging from 3.74 to 6.25 \(\Phi\), a mean value of 5.03 \(\Phi\), and a standard deviation of 0.45 (Table 1), indicating that the sediments were silty sand. The sorting coefficient ranged from 1.22 to 1.81, with a mean value of 1.34 and a standard deviation of 0.46, suggesting poor sorting. The skewness ranged from 0 to 0.61, with a mean value of 0.30 and a standard deviation of 0.08, indicating positive skewness. The kurtosis values ranged from 0.84 to 2.06, with a mean value of 1.33 and a standard deviation of 0.19, suggesting the existence of a sharp peak. According to the MM, the sediment grain size was relatively fine, characterized by an average grain size between 3.52 and 6.26 \(\Phi\) (Table 1), a mean value of 4.95 \(\Phi\), and a standard deviation of 0.46, indicating that the sediments were silty sand. The sorting coefficient ranged from 0.92 to 1.75, with a mean value of 1.28 and a standard deviation of 0.15, suggesting poor sorting. The skewness ranged from 0.91 to 2.07, with a mean value of 1.39 and a standard deviation of 0.18, indicating significant positive skewness. Additionally, the kurtosis ranged from 1.58 to 2.61, with a mean value of 1.95 and a standard deviation of 0.17, indicating a flat peak.  
The sedimentological characteristics of modern flood sediments in the lower Yellow River were studied, revealing that they had a fine grain size and could be classified as poorly sorted silty sand. However, the results obtained using the two methods differed. Specifically, the skewness obtained using the GM was positive, whereas that obtained using the MM was extremely positive. Furthermore, the kurtosis obtained by the GM was sharp, whereas that obtained using the MM was flat.  
A linear regression analysis was performed on the data obtained from the GM and MM (Figure 2). The mean grain size regression exhibited a strong correlation, with a slope of 0.9769 and a coefficient of determination (\(R^{2}\)) of 0.9961 (Figure 2a). In contrast, the sorting coefficient results showed a significant correlation between the two methods, with a slope of 0.5555 and a coefficient of determination of 0.6966 (Figure 2b). Similarly, the correlation between the two methods for skewness was also significant, with a coefficient of determination of 0.6371 and a slope of 0.3617 (Figure 2c). Conversely, the correlation between the two methods for kurtosis was weak, with a coefficient of determination of 0.0577 and a slope of 0.2597, indicating a poor correlation or no correlation between them (Figure 2d).
# Scheduling Non-Preemptible Jobs to Minimize Peak Demand
## 3 Algorithms
### An Approximation Algorithm
#### 3.2.1 Integer Linear Programming Formulation  
The PDM problem can be formulated as an ILP using the following notation and variables:  
\(J\)--Set of jobs.  
\(I_{j}\)--A finite set of valid execution intervals for job \(j\in J\) (an interval \([s_{j},s_{j}+l_{j})\) is valid for job \(j\) if and only if \(a_{j}\leq s_{j}\) and \(s_{j}+l_{j}\leq d_{j}\)).  
\(h_{j}\)--Height of job \(j\).  
\(L\)--Set of all left hand time points of intervals in \(\bigcup_{j}I_{j}\).  
\(H^{\max}\in\mathbb{R}\)--Peak demand.  
\(x_{i,j}\in\{0,1\}\)--Indicates if interval \(i\in I_{j}\) is scheduled.  
The ILP is:  
\[\min H^{\max}\]  
subject to:  
\[\sum_{i\in I_{j}}x_{i,j}=1\ \ \ \forall j \tag{2}\] \[\sum_{i\in\bigcup_{j}I_{j}:t\in i}h_{j}x_{i,j}\leq H^{\max}\ \ \ \forall t\in L \tag{3}\]Constraint (2) ensures that exactly one interval will be selected for each job, and (3) ensures that \(H^{\max}\) will denote the peak height of the schedule. Only elements of \(L\) need to be considered in (3) as a consequence of Lemma 3.  
The maximum height of any schedule must occur for some \(t\in L\).  
Proof.: Schedule height only increases at the moment a new job begins processing, and thus the peak height of a schedule must be initiated at the start time of some job. Since \(L\) is the collection of all possible start times for all jobs, some point in \(L\) must correspond to the arrival time of the peak height of the schedule.
# Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems
## 4 Communication Optimization Schemes
### Gradient Accumulation  
Training accuracy may decrease due to layer dropping. The loss of training accuracy can be avoided by applying gradient accumulation [24]. Therefore, to avoid the loss of training accuracy, we perform layer accumulation. Gradient accumulation is performed in each worker. To perform layer accumulation, gradients of the hidden layer that are not sent to the parameter server are stored and accumulated in the local cache of each worker. When the average of the accumulated hidden layer gradients stored in each worker's local cache exceeds the threshold, the gradients stored in the local cache are sent to the parameter server. The specific layer compression and accumulation method is as follows. When there are N gradients of the hidden layer in the \(t\)th iteration, the \(i\)th gradient is \(gradients(t)_{i}\), and the \(i\)th of the N accumulated gradients of the hidden layer is \(accum(t)_{i}\).  
\[accum(t)_{i}=accum(t-1)_{i}+gradients(t)_{i} \tag{1}\]  
\[\frac{\sum_{i=1}^{N}accum(t)_{i}}{N}<Threshold \tag{2}\]  
The initial value of \(accum(0)_{i}\) is zero, and the gradients computed by each worker are added to the gradients stored in each worker's local cache for each iteration. After that, the average of the gradients of each hidden layer is calculated. Hidden layers whose average gradients are smaller than the threshold are stored in the worker's local cache again.
# Comparative Analysis of Current Approaches to Quality Estimation for Neural Machine Translation
## 4 Brief Introduction of the WMT20 QE Sub-Tasks
### Sub-Task 2  
Sub-task 2 is word- and sentence-level post-editing efforts. The objective of sub-task 2 is to improve post-editing by tagging which tokens have been mistranslated, along with the overall quality of the sentence. At the word level, this task consists of evaluating whether the translation was successful for each token in the MT output and source sentence based on the human post-edited sentences. The tokens of the source and target sides are tagged as OK or BAD. In the case of the target sentences, a gap tag is added considering the case of missing words between the tokens. If the number of tokens in the target sentence is N, the total number of tag tokens is 2N+1. Participating systems predict tags for MT output tokens and source sentence tokens.  
Similar to sub-task 1, a sentence-level post-editing effort task is used to measure the quality score for the MT output based on the human translation error rate (HTER) [41]. HTER is similar to the translation error rate (TER), wherein the TER compares the MT output with a reference translation and counts how many edits (substitutions, deletions, and insertions) must be performed to obtain a correct sentence. This value divided by the reference length is the TER score. HTER differs from TER in that humans create new reference translations for the MT output. Using these new reference translations can lead to correct sentences with minimal modifications compared to the use of other reference translations. Referring to the source sentence and the MT output, the participating system predicts the quality of the MT output sentence based on the HTER.
# Altitude Optimization and Task Allocation of UAV-Assisted MEC Communication System
## 1 Introduction
Then, we derive a semi closed-form expression of the optimal flying altitude of the UAV and a closed-form expression of the optimal task allocation ratio in Section 3. The simulation results are presented in Section 4 to confirm the accuracy of the theoretical results. Finally, we conclude the paper in Section 5.
# Efficient Mobile Vehicle Data Sharing Scheme Based on Consortium Blockchain
## 4 Problem Formulation and Solving
### Stackelberg Game Solution
#### 4.2.2 Algorithms for Achieving Stackelberg Equalization  
This section creates an algorithm based on ternary search, to ensure both parties can reach the equilibrium point. In detail, since the buyer's bid must not exceed the historical average price \(p_{avg}\), we select four points in the range between 0 and \(p_{avg}\):\(L,\;R,\;p_{mid},\;p_{midmid}\) and there are:  
\[L=0,\;R=p_{avg},\;p_{mid}=\frac{L+R}{2},\;p_{midmid}=\frac{p_{mid}+R}{2} \tag{10}\]  
The execution steps of the algorithm are:  
1. Based on the buyer's bid \(p_{mid}\), use Formula (8) to find the optimal decision \({d_{k}}^{*}\) of \(RSU_{j}\) each seller's vehicle, and then, based on the decision set \(\{{d_{1}}^{*},\;{d_{2}}^{*},\;\ldots,{d_{n}}^{*}\}\) of all sellers and the buyer's bid \(p_{mid}\), the buyer's benefit function \(U_{b_{i}}\) can be calculated. For the sake of convenience and differentiation, we use \(U_{b_{i}}\), which represents the value of the benefit function in the case of the bid \(p_{mid}\) from the buyer;
2. According to the buyer's bid \(p_{mid}\), we find the seller's decision set and the buyer's benefit, using \(\tilde{U}_{b_{i}}\), which represents the buyer's benefit;
3. Determine the relationship with the size of \(\tilde{U}_{b_{i}}\) and \(\tilde{U}_{b_{i}}\); if \(\tilde{U}_{b_{i}}>\tilde{U}_{b_{i}}\), the upper limit of \(U_{b_{i}}\) appears in the \(L\) to \(p_{midmid}\) interval, the interval is reduced, and \(R=p_{midmid}\). Otherwise, the upper limit of \(U_{b_{i}}\) appears in the interval from \(p_{mid}\) to \(R\), the interval is reduced, and \(L=p_{mid}\).  
Repeat until the difference between \(L\) and \(R\) the minimum unit \(\tau\) of the price becomes small enough. Considering \(U_{b_{i}}\) is a true concave function, the algorithm ternary search should be able to find the bid \({p_{i}}^{*}\) that \(U_{b_{i}}\) is capped. Based on the buyer's bid \({p_{i}}^{*}\), each seller determines the amount of resources \({d_{k}}^{*}\) it provides to achieve the most substantial benefits. The cycle continues until the buyer's bid cannot be further divided, and both parties to the transaction achieve an equilibrium point. That is to realize simultaneously \(s_{k}\) seller's optimal shared data volume \({d_{k}}^{*}\) and \(p_{i}\) buyer's optimal bid \({p_{i}}^{*}\).
## 3 System Design and the Proposed SEAC-Based Resource Allocation Methodology
### Network State Representation  
In 5G-NR-V2X Mode 2 networks, the state includes essential information for each vehicle's decision making regarding radio resource allocation. Accurate observation and processing of these parameters are crucial for efficient collision-free communication in vehicular networks.  
The state \(s_{t}\) at a given time t includes the following components:  
\[s_{t}\ =\left\{C^{k\times m},\ \Delta d,\ I_{-}sf\right\} \tag{1}\]