esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model
### Network Structure and Training Process  
The constructed RUL prediction model of CNN-GRU-MHA is shown in Figure 5.  
The model firstly extracts the shallow features of bearing degradation through three sets of 5 \(\times\) 5 convolutions and 2 \(\times\) 2 pooling layers, and at the same time adds BN to prevent the problem of gradient disappearance or explosion during training; adds multi-attention mechanism multi-features for weighting in the model; then adds three sets of 3 \(\times\) 3 convolutions and 2 \(\times\) 2 pooling layers to extract the deep features of bearing degradation and, to improve the prediction accuracy and avoid overfitting, adds L1 regularization to the model; finally, the higher-order features extracted by CNN are inputted into GRU to realize the time information of the bearings. To improve the prediction accuracy and avoid overfitting, L1 regularization is added to the model; finally, the higher-order features extracted by CNN are inputted into GRU to realize the extraction of temporal information of the bearing and are inputted into the fully connected layer to realize the prediction of the RUL of the bearing. The number of convolutional kernels in the six sets of convolutional layers in the CNN-GRU-MHA model is 32, 64, 128, 256, 512, and 1024, respectively, and the activation functions used in the model are all Relu, with the number of heads of the Multi-head attention being 2, the number of hidden nodes in the two GRU layers being 512 and 128, and the number of nodes in the two fully connected layers being 64 and 1, respectively.  
The training process of the CNN-GRU-MHA model is shown in Figure 6.  
1. In the preprocessing part, firstly, the vibration signal is subjected to noise reduction by discrete wavelet transform; secondly, the vibration signal is normalized to between 0 and 1 as the bearing HI using the maximum-minimum normalization method.
2. Construct the degradation labels of the receding bearings, in which the training set data and test set data according to all contain labels and the validation set data do not contain labels.
3. Input the training set data into the CNN-GRU-MHA model, and the spatial information of the degradation features of the rolling bearing is sufficiently obtained by CNN.
4. Input the CNN extracted features into GRU for further modeling the temporal information of the degraded features.  
Figure 5: RUL prediction model of CNN-GRU-MHA model.  
5. Input the degraded features into two fully connected layers to realize the RUL prediction of rolling bearings. 6. Calculate the loss function of the model. 7. Utilize the loss-tuned parameters of the model to complete the training of the training set data when the number of iterations m of the network reaches N. Otherwise, repeat steps (4) to (6). 8. Freeze the structure and parameters of the feature extraction layer of the model and continue training the top layer of the model on the test set data. 9. Repeat steps (4)-(7). 10. Complete the test set model training. 11. Output the prediction results of the validation set data.
# Maintenance of Discovered High Average-Utility Itemsets in Dynamic Databases
## 3 Preliminaries and Problem Statement
**Property 1** (Downward closure of \(auub\)). : _Let an itemset \(Y\) be the superset of an itemset \(X\) such that \(X\subseteq Y\). According to the downward closure property of \(auub\), we obtain that:_  
\[auub(Y)^{D}\leq auub(X)^{D}. \tag{8}\]  
Thus, if \(auub(X)^{D}\leq TU^{D}\times\delta\), then \(auub(Y)^{D}\leq auub(X)^{D}\leq TU^{D}\times\delta\) holds for any superset of an itemset \(X\). **Definition 8** (High average-utility upper bound itemset, HAUUBI). : _An itemset \(X\) is a high average-utility upper bound itemset (HAUUBI) in the original database if it satisfies that condition:_  
Table 2.0  
For instance, assume that the minimum average-utility threshold is set to 14.4%. Thus, the itemset (\(a\)) is a high average utility upper bound itemset (HAUUBI) since \(auub(a)^{D}\) (\(=68>244\times 14.4\%\))(\(=35.13\)). However, the itemset (\(ab\)) is not a HAUUBI since \(auub(ab)\) (\(=12<35.13\)). For the incremental mining problem, we assume that several transactions are inserted into the original database. In this example, transactions for insertion are shown in Table 2.1 Thus, the problem definition of this paper is defined as follows. **Problem Statement:** For incremental HAUIM, it is necessary to design an efficient algorithm for transaction insertion to maintain and update the discovered information, and multiple scans of the updated database should be avoided. Thus, an itemset is considered as a HAUI if it satisfies the following condition:  
Table 2.2  
where \(au(X)^{U}\) is the updated average-utility of \(X\), \(TU^{D}\) is the total utility in the original database \(D\), and \(TU^{d}\) is the total utility in the modified database d. Moreover, \(\delta\) is a user specified minimum average-utility threshold, which can be adjusted by experts for different domains.
###### Abstract
Muscle contraction force provides the contraction information emitted by a muscle associated with a movement, enabling the observation of abnormal conditions in a muscle related to the specific movement. Electromyography (EMG) is one of the common modalities to estimate the contraction force of the muscle [8; 9]. Most studies utilized EMG, as it directly correlates with muscle activity and showed promising results. Nevertheless, EMG is acknowledged for its complexity, applicability, and low signal-to-noise ratio (SNR). Several studies have highlighted the limitations of EMG in estimating muscle contraction force due to interference patterns, delay limitations, the need for subjective perception, and complex reconstruction or self-calibration procedures [7; 9; 10; 11]. Despite the direct correlation with muscle, the nature of EMG is associated with several shortcomings regarding usability and wearability aspects for the user. Considering the alternative modality to the muscle, several studies have introduced an excellent correlation between the stretch sensor and EMG [12; 13; 14]. Studies show a strong correlation between stretch sensors and EMG for evaluating muscle activation timing [12]. Stretch sensors offer advantages such as softness, flexibility, low power consumption, and ease of integration with microelectronics, capturing finger kinematic mechanisms and facilitating the logging of complex movements [13]. These findings suggest that the stretch sensor has great potential as an alternative to EMG for muscle monitoring. Despite its great features and potential, it remains unexplored whether the stretch sensor can accurately estimate the muscle contraction force, as represented by the root mean square (RMS) of the EMG signal, which becomes the main issue addressed in this study. This study attempted to address the issues of convenience and practicality associated with using EMG sensors as monitoring tools for home-based rehabilitation. This study proposes a method to estimate the contraction force of the forearm muscles through a stretch-sensor glove, where the muscle contraction force is expressed as the RMS value of the linear envelope of the electromyography (EMG) signal. Kinematic information of the finger is captured by the stretch sensor as input to learn the forearm muscle contraction force. The experimental design was constructed by adopting the essence of cylinder grasping activity with varying sizes and weights based on the home-based therapy program of GRASP.
## 4 Feasibility of PAZ as an Opportunity Illuminator in a Passive Bistatic Radar System
### Instrumented Coverage Area  
Due to the high distance between the OI and the PR receiver, \(L>2(R_{T}R_{R})\), the coverage area is split into two ovals centered on the OI and the PR receiver, respectively. The one centered on the receiver is the coverage area to be exploited. The system area of interest must be located inside this oval.  
The instrumented coverage area is determined by the OI antenna footprint. It depends on the selected imaging mode: stripmap, ScanSAR or spotlight. Due to the satellite and the SAR sensor antenna beam movement, the following issues must be considered:  
* The reference channel must include tracking techniques to guarantee the reference signal reception.
* The surveillance channel must reject the OI direct signal (direct path interference (DPI)) as the OI beam moves. The PR receiver and the area of interest are in the same coverage oval, so both are illuminated by the OI. Targets to be sought are on the surface or at low altitudes compared to the OI position, making the rejection of the direct OI signal feasible (incidence angles in \([15^{\circ},65^{\circ}]\)). The DPI rejection requires a surveillance antenna system capable of generating notches toward the satellite elevation and a receiver with properly-designed filtering techniques.
* If the OI antenna beam cannot be considered stationary or quasi-stationary during the acquisition time (stripmap or ScanSAR imaging modes), the coverage area will vary with the footprint movement.  
In Section 5, a study of the system geometry is presented assuming a spotlight acquisition mode (Figure 18).  
Figure 17: Two-line element (TLE) parameters for the TerraSAR-X orbit (13 April 2015) [36].
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 5 SRv6-Based Lossless Migration
### Supporting Buffering and SRv6 Behavior
#### 5.2.1 END.Buffer Behavior  
The End.Buffer behavior is an extension of the End behavior with the USD described in [23]. When a packet arrives to a node with a destination address matching a node's End.Buffer SID, it is first checked whether the current destination address of the outer IPv6 header is the last segment of the SRv6 list. In that case, the packet is decapsulated. If, instead, the SRH is still needed, the packet is updated as follows. The IPv6 outer header Hop Limit field and the Segment Left field of the SRH are decreased by 1. Finally, the IPv6 outer header destination address is updated with the current active segment. Whether the packet has been decapsulated or not, the packet is placed in the buffer, ready to be forwarded without further modifications once the buffer flush is triggered.  
Figure 12 reports an example of the End.Buffer behavior, used in the mode described in Section 5.1.1. The incoming packet (on the left) has the End.Buffer SID of the SR Endpoint2 router as the destination address of the outer IPv6 Header. Firstly, the behavior modifies the SRH outer IPv6 header. It decreases the Segment Left field. Then, it sets the IPv6 destination address equal to the active segment. Subsequently, it decreases the Hop Limit header field. Finally, the resulting packet is placed in the buffer.  
Figure 11: Data plane in Enhanced Mode with Unchanged gNB path migration with buffering.  
Figure 12: The End.Buffer behavior.
###### Abstract  
This study investigates the impact of using _Lactiplantiobacillus plantarum_ and _Saccharomyces cerevisiae_, either individually or in co-culture, on the fermentation of rose beverage. We comprehensively analyzed the resulting changes in quality characteristics and volatile compound profiles. Fermentation significantly altered the physicochemical properties, appearance, color, and free amino acid/organic acid content. Both microbial strains significantly increased total polyphenols and flavonoid content, with co-fermentation exhibiting a more pronounced effect compared to single-strain fermentations. Furthermore, the volatile compounds in rose beverages fermented with different microorganisms were characterized by an electronic nose (E-nose) and headspace-solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME/GC-MS). E-nose analysis demonstrated distinct volatile profiles distinguishing the four fermentation samples. HS-SPME/GC-MS identified a total of 245 volatile compounds, among which alcohols constituted the most abundant class. Integrating GC-MS data with odor activity value (OAV \(\geq\) 1) analysis pinpointed 34 key aroma compounds. Partial least-squares discriminant analysis (PLS-DA) based on variable importance in projection (VIP) identified eight key volatile markers: eugenol,phenylethyl alcohol, (E)-3,7-dimethyl-2,6-octaidenoic acid, methyleugenol, ethyl octanoate, citronello1, D-citronello1, and 2,4-bis(1,1-dimethylethyl)phenol. These findings provide valuable insights into the microbial influence on rose beverage quality and offer a theoretical basis for optimizing industrial fermentation processes.  
rose beverages; co-fermentation; quality characteristics; volatile compounds; biomarkers 1411868
###### Abstract  
The second-generation of the Beidou Navigation Satellite System (BDS-2) has been officially providing positioning, navigation, and timing (PNT) services within the Asia-Pacific region for six years, starting from 2013. A comprehensive analysis of BDS-2 satellite broadcast navigation message performance during the past six years is highly demanded, not only for the regional service but also for the global service announced in December 2018. Therefore, this study focuses on the performance assessment of six-year BDS-2 broadcast navigation messages from 2013 to 2018 in three aspects: Message availability, anomaly detection, and signal-in-space user range errors (SIS UREs). Firstly, our results, based on International GNSS service (IGS) Multi-GNSS Experiment (MGEX) navigation files, indicate that the BDS-2 Geosynchronous Earth Orbit (GEO) and Inclined Geosynchronous Satellite Orbit (IGSO) satellites have >98.51% broadcast navigation message availability, and the Medium Earth Orbit (MEO) satellites has a ~90.03% availability. Secondly, the comparison between broadcast navigation messages and IGS precise products reveals that the User Range Accuracy Index (URAI) contained in the broadcast message could not reflect satellite performance correctly. Another satellite status indicator, space vehicle (SV) health, can only partially detect a satellite anomaly. The anomaly detection result using IGS precise products for reference shows 20241 anomalies out of 651038 broadcast navigation messages within six years. Finally, compared with the IGSO and MEO satellites, the orbit qualities of GEO satellites are significantly worse due to their large along-track orbit error. The clock performance of all satellites are at the comparable level. The satellite orbit type (GEO/IGSO/MEO) does not impact the orbit-only URE, global-average URE, and worst-case URE.  
BDS-2; anomaly detection; global-average URE; orbit-only URE; worst-case URE 14 May 2019; Accepted: 18 June 2019; Published: 20 June 2019
# Femtosecond Laser Textured Surfaces for Radiative Cooling: Black Metals
## 3 Results and Discussion
### Ablation with High-Intensity fs-Laser Pulses
The electron density is estimated considering one electron per atom donated to metallic bonding \(n_{e}^{(m)}=N_{A}/V_{m}^{(m)}\), where \(N_{A}\) [mol\({}^{-1}\)] is the Avogadro number and \(V_{m}^{(m)}\) [cm\({}^{3}\)] is the molar volume calculated above. With all parameters defined and \(\lambda=1030\) nm, the following ablation thresholds are obtained from Equation (1): 0.40 J/cm\({}^{2}\) (Cu), 0.26 J/cm\({}^{2}\) (Al), 0.34 J/cm\({}^{2}\) (W). These are single-pulse ablation thresholds. As the surface is ablated and reflectivity is gradually reduced with subsequent pulses, the accumulation dose or \(F_{p}\) times the number of pulses \(N\) per spot of diameter \(D\): \(N=D/v_{s}/f\) at scanning speed \(v_{s}\) and laser repetition rate \(f\) can reach the ablation threshold even at slightly lower pulse energies. The experiments in this study were carried out at an irradiance up to 30-100 times above the threshold; moreover, with up to \(N=10\) pulses per focal spot. Hard X-rays generated at such conditions [38] require the screening enclosure of the sample chamber, especially when multi-pulse or burst modes of exposure are used [39].
## 2 Proposed Prognostic Framework
### Particle Filter Based Prognosis  
Particle filters (PF) or Sequential Monte Carlo (SMC) methods are based on Bayesian inference and are widely used in the field of Prognostics and Health Management (PHM). The main advantage of PFs is that they can be readily applied to non-linear systems with non-Gaussian noise present in them. The basic idea of PFs is to represent the unknown model parameters by a set of weighted particles that describe the state of the system. The particle weights are recursively updated using Bayes theorem. The state space model of the system used in PFs can be represented as:  
\[a_{k}=f_{k}(a_{k-1},v_{k-1}) \tag{5}\]  
\[z_{k}=h(a_{k},n_{k}) \tag{6}\]  
where \(k\) is the impact index at which the sample is scanned using the OTS technique; \(a_{k}\) and \(a_{k-1}\) represents the current and previous damage states respectively; \(v_{k-1}\) and \(n_{k}\) represent the process and measurement noise, respectively; and \(z_{k}\) represents the new measurement data. The state-transition function \(f(.)\) contains information about the damage propagation model, and \(h(.)\) represents the measurement function that provides the mapping between the damage state and the new measurement data. The PF algorithm estimates the damage evolution process in two steps.
# Entropy Analysis of a Flexible Markovian Queue with Server Breakdowns
## 2 Model Formulation and Previous Results  
[PERSON] et al. [10] studied a versatile single-server queueing system where service is regulated by an integer threshold level \(c\geq 2\), and can be either single or batch as follows--if the queue length is less than \(c\), then service is single and exponential with parameter \(\mu_{1}\). If the queue length is equal to \(c\), then service is batch of size \(c\) and follows the exponential distribution with parameter \(\mu_{2}-\mu_{1}>0\). Finally, if the queue size is greater than \(c\), then service is again batch of fixed size \(c\) and follows the exponential distribution with parameter \(\mu_{2}\). The server is subject to breakdowns which happen according to a Poisson process with rate \(\alpha_{1}\) when service is single and \(\alpha_{2}\) when service is batch. Repairs that follow breakdowns are exponentially distributed with rate \(\beta_{1}\) when service is single and \(\beta_{2}\) when service is batch. Assume that costumers arrive according to a Poisson process with positive rate \(\lambda\).  
We let \(X(t)\) represent the number of customers in the system at time \(t\) and introduce \(w_{n}\), \(n~{}=~{}0,1,2,\cdots\) the probability of \(n\) customers in the system in the steady-state when the server is in a working state, and \(p_{n}\) the probability of \(n\) customers in the system in the steady-state regardless of the server state. Also, for \(|z|\leq 1\), we introduce the probability generating functions:  
\[W(z)=\sum_{n=0}^{\infty}w_{n}z^{n}\quad\text{ and }\quad P(z)=\sum_{n=0}^{ \infty}p_{n}z^{n}.\]  
Then,  
\[W(z) = \frac{A_{1}(z)S_{1}(z)+A_{2}(z)w_{0}z^{c-1}+A_{3}(z)z^{c}w_{1}}{z^ {c}(\lambda+\mu_{2})-\lambda z^{c+1}-\mu_{2}}, \tag{1}\] \[P(z) = \left(1+\frac{\alpha_{2}}{\beta_{2}}\right)W(z)+\left(\frac{ \alpha_{1}}{\beta_{1}}-\frac{\alpha_{2}}{\beta_{2}}\right)S_{1}(z), \tag{2}\]  
where  
\[A_{1}(z):=(\mu_{2}-\mu_{1})z^{c}+\mu_{1}z^{c-1}-\mu_{2},\qquad\qquad\qquad A_ {3}(z):=\frac{\mu_{1}^{2}(z^{c-1}-1)}{\mu_{1}-\mu_{2}},\]  
\[A_{2}(z):=(\lambda+\mu_{1})z-\mu_{1}+\frac{\lambda z(\mu_{1}z^{c-1}-\mu_{2})}{ \mu_{2}-\mu_{1}},\qquad S_{1}(z):=\sum_{n=0}^{c-1}w_{n}z^{n}.\]  
The \(c\) unknown probabilities \(w_{n},n=0,1,\cdots,c-1\), are determined by solving the system of \(c\) equations:  
\[A_{1}(z)S_{1}(z)+A_{2}(z)w_{0}z^{c-1}+A_{3}(z)z^{c}w_{1}\Big{|}_{ z=z_{i}} = 0,\quad i=1,2,\cdots,c-1, \tag{3}\]  
\[\sum_{n=0}^{c-1}a_{n}w_{n} = 1, \tag{4}\]  
where \(z_{i}\) are the \(c-1\) roots inside the open unit ball of the equation  
\[-\lambda z^{c+1}+z^{c}(\lambda+\mu_{2})-\mu_{2}=0,\]
## 3 Data and Methods
### Data Processes  
MOD11C3, MOD13C2 and MOD15A2 were downloaded from the NASA homepage [74], and MOD17A2 was downloaded from the University of Montana [65]. Regarding the spatial resolution of these MODIS products, MOD11C3 and MOD13C3 have 0.05-degree spatial resolutions; MOD15A2 and MOD17A2 have 1-km resolutions. All of these products are sinusoidally projected, and all data are monthly except MOD15A2 (8-day). To use these data in one model, we must unify them into the same projection, the same spatial resolution and the same temporal resolution.  
First, all data were reprojected and masked from a sinusoidal projection to the Geographic WGS84 projection as a 0.05-degree pixels using the MODIS Reprojection Tool [75]. Then, MOD15A2 LAI/FPAR was monthly stacked. We employed the maximum value composite (MVC) method that selected the highest pixel value to represent the composite period [17,49].  
The spatial variability of GPP and NPP over the global is enormous, ranging from approximately 1,000 g C m\({}^{-2}\) for evergreen tropical rain forests to less than 30 g C m\({}^{-2}\) for deserts. For bare areas such as the Sahara Desert in North Africa and deserts in central-eastern Asia and western China, there are no calculated GPP data, and the values were defined as 0 in the present study.  
For the TANSO CO\({}_{2}\) data, we averaged all of the TANSO FTS Level 2 points in each month for each continent as the monthly value. From June 2009 to November 2011, there were 66,126 CO\({}_{2}\) points in Africa, and the point numbers were 21,088, 37,651, 11,796 and 10,504 in Australia, Eurasia, North America and South America, respectively.
## 2 Materials and Methods
### Statistics  
All continuous variables are reported as median values and interquartile range (IQR), and categorical variables as numbers and percentages. Statistical significance level was considered to be 95% CI (two-sided alpha \(p<0.05\)). Differences in demographic and clinical features between treatment responders and non-responders was assessed using Mann-Whitney U test on continuous variables, and the chi-square test or Fisher's exact test on categorical variables. To investigate the relationship between demographic and clinical features and the effect of treatment, we used univariate and multivariable logistic regression models including age, gender, disease duration, EDSS, concomitant use of physiotherapy, concomitant pharmacological treatments for spasticity and use of disease-modifying therapies. \(p\) values \(<0.05\) were considered to indicate statistical significance. IBM SPSS Statistics 24.0 software was used to conduct data analyses.
## 4 Intraoral Molar Distalization Methods
### Tooth Tissue-Supported Intraoral Molar Distalization Methods
#### 4.1.2 Super-Elastic Nickel-Titanium (Ni-Ti) Open Coil Springs  
Ni-Ti open coil springs have gained large popularity for distalization purposes due to their good spring-back action and light continuous force delivery. They produce about 100 gm of force when they are compressed by 10 mm between the first premolars and first molars on a 0.016 \(\times\) 0.022 inch stainless steel wire [27].  
[PERSON] et al. [28] showed that compressed Ni-Ti coil springs were effective in moving maxillary first molars distally, particularly in the treatment of noncooperative patients with Class II molars malocclusion. However, molar distalization occurred at the expense of increased crown tipping and anchorage loss at the anterior dental region [28].