esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Frequency and Signal Purity Inputs  
Similar to some of the findings of this study (Table 1), several studies have reported well-retained \(f_{\text{in}}\)[12; 27] and \(P_{\text{in}}\)[25]. When comparing \(f_{\text{in}}\) with \(f_{\text{set}}\), [PERSON] et al. (2009) [26] found slight differences between 0 and +1 Hz. Similarly, [PERSON] et al. (2014) [27] analysed several commercial platforms and showed very good agreement between \(f_{\text{in}}\) and its corresponding \(f_{\text{set}}\). In line with those studies, the LVP \(f_{\text{in}}\) has been found to be slightly higher than \(f_{\text{set}}\) (+0.5 Hz) and consistent across postures (Table 1). Controversially, two studies describe differences between +3 Hz [31] and up to \(-\)13 Hz [18]. However, the \(f_{\text{in}}\) has been shown as a stable parameter from its \(f_{\text{set}}\)[12; 14]. Regarding \(P_{\text{in}}\) (Table 2), which is considered satisfactory above 80% [22; 24; 25], the results for the LVP on this investigation showed purity values consistently over 99%. Similarly, [PERSON] et al. (2018) [25] reported \(P_{\text{in}}\) over 93% across \(f_{\text{set}}\) and postures. Thus, the LVP \(f_{\text{in}}\) and \(P_{\text{in}}\) were well-maintained from its \(f_{\text{set}}\) among trials and does not seem to be affected by the large dimensions of the LVP.
# Hand Posture Recognition Using Skeletal Data and Distance Descriptor
## 3 Proposed Method
### Distance Descriptor  
The Distance Descriptor is a novel method proposed in this paper. It encodes information about distances between hand points corresponding to fingertips and the palm center. It uses only information about the positions of these points. Normal vectors and vectors pointed by the fingers are not required. The descriptor can be computed as follows.  
1. For each point \(P_{i}\):  
Compute distances (using Euclidean or city block metric) between the other points \(P_{j}\), \(j\
eq i\)  
Figure 1: Point Pair Descriptor construction.  
1.2 Sort points \(P_{j}\) by the calculated distances in ascending order. 1.3 Assign consecutive integer values \(a_{ij}\), starting from one, to the sorted points \(P_{j}\).
2. Create a feature vector consisting of integer values, assigned to the points \(P_{j}\) in step 1.3, and ordered as follows \([a_{12},a_{13},a_{14},a_{15},a_{21},...,a_{65}]\).
3. Create a reduced feature vector by adding together integer values corresponding to the same pair of indices \(i\), \(j\): \([a_{12}+a_{21},a_{13}+a_{31},...,a_{56}+a_{65}]\).  
The purpose of step 3 is not only to reduce the number of features. After this step, for each point \(P_{i}\), the descriptor determines not only which of the remaining points \(P_{j}\) are its nearest neighbors but also which of the \(P_{j}\) points consider \(P_{i}\) as their nearest neighbor. Features of the Distance Descriptor are normalized to the interval [0-1] by dividing them by \(2(n-1)\), where \(n=6\) is the number of points.  
DD is an alternative to other positional-based and distance-based features describing hand skeleton. Such features are simple 3D or 2D coordinates or distances between fingertips and palm center [15,18,39,40,45] or between fingertips and palm plane [15,18]. Some of them are normalized with respect to the hand position and orientation, however, such normalization is not fully accurate. Moreover, even after the normalization, these features--unlike DD descriptor--are scale dependent, making methods difficult for recognizing gestures performed by people with different hand sizes (especially in the case of child hands). Some of these features are often not distinctive enough to differentiate between similar hand postures. It is because they do not include positional relations between fingertips (only between each fingertip and palm or plane center), whereas DD features include relations between each fingertip and between fingertips and palm center.  
The Matlab codes for Point Pair Descriptor and Distance Descriptor can be downloaded from our website [46].
# EXOLOv3: An Efficient Real-Time Detection Model for Floating Object on River
## 5 Conclusions and Prospect  
In this paper, we propose an EXOLOv3 model. Considering the characteristics of floating objects, a deep multi-scale feature extraction and fusion network is designed to make full use of multi-scale feature information to achieve the complementary effect of different features and enhance the feature expression ability; in the training stage, the balanced Focal Loss Function is used to dynamically adjust the weight of positive and negative samples, so that the training can focus on hard samples and solve the imbalance problem of positive and negative samples. K-means++ is used to cluster the anchor boxes of the floating objects, and the anchor boxes redesigned match the floating better; improved NMS algorithm is used to reduce the missed detection of objects with high overlap. The experiments show that the EXOLOv3 is robust to the floating detection under different lightings. The detection accuracy of our model is improved to 82.3%, and the detection speed can reach 35 FPS, which meet the requirements of real-time floating detection.  
In future work, we consider further improvement of NMS or use of better NMS algorithm, such as Syncretic-NMS algorithm proposed by [PERSON] et al. [36]. The algorithm takes the traditional NMS as the first step, processes the boundary boxes obtained by the traditional NMS, judges the adjacent boundary boxes of each boundary box, and combines them with the corresponding boundary boxes. Syncretic-NMS algorithm has achieved excellent performance when applied to instance segmentation. In addition, combining multiple object detection networks is also the trend of future research. [PERSON] et al. [37] proposed a deep learning model based on YOLO, SSD and EfficientDet mainstream object detection network. It is applied to the detection of sea mines on the surface and underwater, which provides a useful idea for the detection of floating objects on the surface.  
Conceptualization, L.Z. and Z.X.; methodology, L.Z. and Z.X.; validation, Z.X., M.X. and Y.Z.; resources, G.W.; data curation, L.Z. and G.W.; writing--original draft preparation, L.Z., Z.X. and Y.Z.; writing--review and editing, L.Z. and Z.X.; supervision, L.Z.; project administration, L.Z. and G.W.; funding acquisition, L.Z. and G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Nation Nature Science Foundation of China (grant nos.420755191, 91847301, 92047203, and 52009080); The science and technology innovation project of Guangdong Province Water Resources Department (No.2021-07).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Method
### Model Parameters' Estimations
The process would be described as follows:  
* Consider, for instance, two chains with 1000 iterations with an initial value such as 1 and 1. * Normalize the initial values based on the data distribution and replace updated values by the assigned starting values, highlighting the plausibility of initial values that came out from the proposed distribution. * Generate the first value in the first chain by adding up the related starting value by some random numbers: It should be noted that the generated values are created by adding up initial values by a random number from the normal distribution, or assigned distribution. * Check the plausibility of generated sample numbers coming from the population distribution; accept the generated value if it is more plausible as the related starting value, otherwise use the starting value. * For value in the second chain, the value of the first chain would be used. 4. An updated proposed sample would become as a base for the next sample in the MCMC chain. 5. Return to step 2 for a next iteration. 6. Stop when there are enough samples to the number of iterations, based on the assigned value. 7. Discard the warm-up, or burn-in samples, as they are likely to be inaccurate. As the starting values were set arbitrary and it take time for the algorithm to get approximately close to the posterior distribution, the observations would go up and down very abruptly; and the distribution is unlikely to be from a posterior distribution. The use of sampling is helpful as the model could use the chains to run sampling many times with different starting values. The following paragraphs would outline some background about model adjustments based on JAGS. A random number of the generator (RNG) was used for each chain, and each chain has its own RNG [7]. Although the numbers seem entirely random, they would be determined by the initial values. It should be noted that the RNG is supplied by the Witchmann-Hill algorithm, which is a pseudorandom number generator. To reduce autocorrelation between the generated numbers, thinning would be used to keep observations based on some intervals, while rejecting the others. JAGS is built on top of BUGS so the codes were written based on the BUGS language. The BUGS contain various implemented models' distributions for various scenario, and act as a black box as the sampling based on the distribution is not accessible based on the JAGS interface itself.
# Energy and Spectrally Efficient Modulation Scheme for IoT Applications
## 5 Conclusions  
In this paper, the multiple-input multiple-output orthogonal shaping pulses minimum shift keying (MIMO-OSP-MSK) has been presented as a low-cost modulation/demodulation scheme. The OSP-MSK fulfills a low-power budget by using a nonlinear PA at the TX side and by applying a low resolution one-bit ADC at the RX side. Moreover, the OSP-MSK harnesses in a new fashion the orthogonality between the shaping pulses to boost up the spectral efficiency. By means of this orthogonality, the OSP-MSK increases the number of transmitted bits per symbol of the MSK/GMSK without additional cost for extra bandwidth. Mathematical closed-form expressions for assessing the ABER performance of the OSP-MSK modulation over the AWGN channel, Rayleigh fading channel, and Nakagami-m fading channel were derived and analyzed thoroughly. In addition, a mathematical closed-form expression for the power spectral density of the proposed OSP-MSK was also derived and studied in detail. The simulation results corroborate the effectiveness of the conducted analysis by revealing a close match to the obtained analytical formulas. Furthermore, the simulation results evinced that the proposed OSP-MSK outweighs the MIMO-CEM with the one-bit ADC, in sense of the spectral efficiency and without any deterioration in the ABER performance.  
Conceptualization, H.S.H.; Methodology, H.S.H. and M.E.; Implementation/testing and software, M.E., H.S.H. and M.F.; Supervision, H.S.H. and U.S.M.; Writing-original draft, H.S.H. and M.E.; Writing-review & editing, M.F. and U.S.M.  
This work is sponsored by Egyptian Science and Technology Development Funds (STDF) under STDF project #5147. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.  
The authors would like to express their gratitude to King Khalid University, Saudi Arabia for providing administrative and technical support.  
The authors declare no conflict of interest.
## 5 Discussion
### The Selection of the Vessel Traffic Accident Frequency as the Index  
The vessel traffic risk can be measured in two dimensions by the risk matrix, which displays the frequency and the consequence of the vessel traffic accident at the same time. As discussed in the introduction section, the time window selection should be considered in the process of uncertainty analysis of the vessel traffic risk. As accident consequences are generated through a fuzzy development process after accidents occur and are often determined by incorporating expert judgements [18], the vessel traffic accident frequency is selected as the representative index for the time window selection in the uncertainty analysis for simplification. Then the epistemic uncertainty of the accident consequence is quantified based on all the information included in the selected time window. If the vessel traffic risk is measured by a one-dimension value, the algorithm for time window selection should apply to the time series of the product of the accident frequency and the accident consequence.  
In order to discuss the possible effects of the simplification, the algorithm for time window selection applies to the time series of fatalities in Table 1. According to Equation (1), the confidence intervals of fatalities per accident for the 2000-2016 period can be estimated for the confidence value 0.95 and can be arranged to form the uncertain time series, which is shown in Figure 4. Fatalities per  
Figure 3: The risk matrix of the vessel traffic risk.  
accident in Figure 4 are represented by diamond icons and are connected by a line; the limits of the confidence interval are shown and connected by dotted lines.  
According to Section 3, the uncertain time series in Figure 4 can be split into four segmentations, which are the years 2000-2004, 2005, 2006-2011 and 2012-2016. By comparison with the segmentation result in Figure 2, we find that accident frequency declines while fatalities per accident remain stable from the years 2000 to 2004. In addition, the accident frequency remains stable while fatalities per accident decline from the year 2006 to 2016. The variation of the accident consequence is not always in accordance with that of the accident frequency and sometimes the volatility of the accident consequence is strong because of one accident with multiple fatalities. In this study, the vessel traffic accident frequency is selected as the representative index for the time window selection in the uncertainty analysis, which is one of the limitations of the work and should be studied further in the future.
# Criteria for Cutting Head Clogging Occurrence during Slurry Shield Tunneling
###### Abstract  
Cutting head clogging is more frequently encountered as more tunnels are being excavated by slurry shield machines. So establishing criteria for cutting head clogging occurrence based on the machine driving parameters is of great engineering significance. Three construction cases of the Beijing south-to-north water diversion auxiliary project, the Jinan Huanghe River Crossing tunnel construction, and the Wuhan Metro Line 8 Yangtze River Crossing tunnel construction, are introduced. Development of the main driving parameters in the construction cases, including the total thrust, the cutting head torque, the advance rate, and the cutting head rotation speed of the tunneling machines before, during, and after the cutting head clogging, are presented and analyzed. The fact is that the total thrust and the cutting head torque of tunneling machines will increase, or will not, once the cutting head clogging occurs. It is recommended to take two combined parameters of total thrust/penetration depth and cutting head torque/penetration depth into account to judge whether the cutting head clogging will occur or not. The maximum increases of the composite parameters by 2-6 times are found in the construction cases. But for the minimum increase, a 30-50% increase of the composite parameter should be noted. The findings can be of great help for similar projects.  
slurry shield tunneling; cutting head clogging; driving parameters; criterion Article
## 4 Discussion
Additionally, incorporating a fixed gain term \((K)\) in the estimator can aid or hinder state estimation, depending on how the fixed gain term is tuned. However, the proof indicates that the inclusion of this fixed term is not necessary for the state estimation error to converge to a neighborhood around zero. The outlined theorem in this paper pertains to accounting for model uncertainty and stochastic noise. If noise term is removed \((v_{x}=0)\), the need for the robust analysis subsides. From here, our previous finding, outlined in [16], can be used to ensure \(\{e_{x},\hat{e}_{y}\}\xrightarrow[t\to\infty]{}0\). However, the result in [16] can be extended by considering the control law defined in Equation (18), which truncates the Lyapunov analysis expressed in Equation (32) to the following:\[\dot{V}_{eL}+2 aV_{eL} \leq a|\operatorname{tr}(L_{*}\gamma_{y}^{-1}\Delta L^{\ddagger})| \tag{81}\] \[\leq a\underbrace{\operatorname{tr}(L_{*}^{\ddagger}L_{*}))^{\frac{ 1}{2}}\operatorname{tr}(\gamma_{y})^{\frac{1}{2}}(2V_{eL})^{\frac{1}{2}}}_{= \frac{M_{L}}{2}}\] \[=M_{L}(2V_{eL})^{\frac{1}{2}},\]  
where \(M_{L}\) is a scalar value greater than zero (\(M_{L}>0\)). Following a similar analysis described earlier in this text, the error state norm is bounded by the following radius of convergence, denoted as \(R_{*}\):  
\[\limsup_{t\to\infty}\|e_{x}\|\leq\frac{M_{L}}{a\sqrt{\lambda_{\min}(P_{x})}} \equiv R_{*}. \tag{82}\]  
where \(R_{*}\) represents a radius around a neighborhood of zero.
# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 4 Evaluation Methods
### Nms  
NRMS (normalized root mean square) uses a color reference image in the quality assessment. Although the name suggests that it is derived from the RMS method, this is not the case. The quality rating using this method is calculated according to Expression (14), where \(I_{C}\) indicates the intensity of a single channel (red, green, and blue) in the original image, and Y the intensity of the pixel in the decolorized image [9].  
\[NRMS=\frac{1}{3}\sum_{C}\frac{\|I_{C}-Y\|}{I_{C}} \tag{14}\]  
The expression in the numerator indicates the Frobenius norm as defined by Formula (15), and the total sum is divided by three to obtain the average differences between the intensity of each of the three channels in the original image and the intensity in the decolorized image.  
\[\|I_{C}-Y\|=\sqrt{\frac{1}{M\cdot N}\sum_{x=0}^{N-1}\sum_{y=0}^{M-1}(I_{c}[x, y]-Y[x,y])^{2}} \tag{15}\]There are two basic differences in the results of this measure compared to RMS. First, the result will always be in the interval between zero and one, which gives a much more representative relationship between two different images. Another difference is that a lower value is interpreted as a better result. Namely, the smaller the average difference between the intensities, it is to be expected that the decolorized image will better show the relationships in contrast to the original image. However, even this measure does not give a perfect estimate, because the intensities of individual channels in the color image can differ significantly, while taking the average does not give a representative relationship.
# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
## 4 Performance Evaluation
### Performance Evaluation in the Effectiveness of Decision Fusion of Multiple Partial Models
Since the test time of each fault diagnosis algorithm is relatively short, if the test time is selected as the evaluation index of the algorithm, it will not be able to accurately show the actual operating efficiency of each algorithm. Hence, the training time is used as the main index for evaluating the operating efficiency of the algorithms. The training time of the TBIDF, FDB-IDS, S-LSTM and MLMF-CR algorithms is shown in Figure 12. It can be seen from Figure 12 that the proposed MLMF-CR and FDB-IDS algorithms are relatively faster training speeds because they adopt a distributed processing framework and can analyze and process the vibration  
Figure 9: F1-Score indicators under different information sources. data and current data of the bearing in parallel. The S-LSTM algorithm adopts the fusion method of data splicing, directly combining the bearing vibration and current signals into a fusion signal as input. Hence, the training time of the algorithm is far longer than that of the other three algorithms. In addition, although the FDB-IDS algorithm is slightly stronger than the MLMF-CR algorithm in model training time, considering various factors such as the accuracy of fault identification, F1-Score, and comprehensive performance of each algorithm, the proposed MLMF-CR algorithm still has more advantages in the field of industrial motor bearing fault diagnosis.
## 6 Conclusions
Therefore, ensuring the reliability of the material properties is essential for the appropriate utilization of these developed models in seismic damage assessment. It is essential to recognize that the present study primarily focuses on demonstrating the effectiveness of ML models in classifying the damage states of URM buildings, without considering various failure modes and the reasons for failure. Future research efforts should aim to develop a comprehensive database that encompasses URM buildings with diverse floor plans and increased numbers of stories, thus encompassing a wider range of buildings globally. Additionally, there is also a need for the development of ML-based damage pattern prediction and fragility assessment for URM buildings. This expansion of scope will contribute to a more comprehensive understanding of URM building behaviour under seismic events. Conceptualization, N.R. ; methodology, N.R. ; software, N.R. ; validation, N.R., O.M. and B.P. ; formal analysis, N.R. ; investigation, N.R. and B.P. ; resources, N.R. and B.B. ; data curation, N.R. ; writing--original draft preparation, N.R. and B.P. ; writing--review and editing, O.M. and B.B. ; visualization, N.R. and B.P. ; supervision, B.B. and O.M. ; project administration, N.R. and B.B. ; funding acquisition, B.B. All authors have read and agreed to the published version of the manuscript. This research is supported by Ratchadapisek Somphot Fund for Postdoctoral Fellowship, Chulalongkorn University. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
ABAdaptive Boosting  
CoVCoefficient of Variation  
DADiscriminant Analysis  
DSDamage State  
DTDecision Tree  
ELM  
Ensemble Learning Method  
ERTRExtremely Randomized Tree Regressor  
FEFinite Element  
GDIGini's Diversity Index  
GPRGaussian Process Regression  
IDRInter-story drift ratio