esa-sceva/satcom-chunk-collection huggingface dataset:
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 2 Submerged Membrane-Filtration Applications and Benefits
### Pretreatment for RO Desalination and Reclamation  
Adequate pretreatment of the feed to reverse osmosis (RO) systems is essential to ensure optimal performance. Low-pressure membrane pretreatment is increasingly implemented prior to the RO unit operation in seawater reverse osmosis (SWRO) plants, and also RO plants for the treatment of surface water and treated municipal effluent [7; 18; 44; 48; 49; 50; 51]. Similar to surface-water treatment, the low solids content in these applications allows the low-pressure membranes to be operated in the dead-end filtration mode with intermittent backwashing. The main advantages of membrane filtration compared to conventional pretreatments such as coagulation, flocculation, and sand filtration are improved water quality, smaller footprint, less chemical requirements, and consistent quality of the filtrate [38; 39; 52; 53]. Higher energy demand and membrane fouling are the main disadvantages of having a membrane-filtration system for RO pretreatment. RO membranes are very sensitive to foulants, so enhanced pretreatment via low-pressure membranes can significantly improve the performance and reduce the energy cost of RO plants [51]. In particular, RO systems with submerged membrane pretreatment have been proven to exhibit a consistently lower silt-density index (SDI) relative to conventional pretreatment [7; 40; 41; 54; 55]. Gravity-driven membrane filtration, which was initially developed as a low-energy process for surface water and diluted wastewater treatment, has also shown potential for seawater pretreatment that requires less energy and no chemical cleaning [56; 57].
# A Replica-Selection Algorithm Based on Transmission Completion Time Estimation in ICN
## 4 Algorithm Description
### Estimated Transmission Rate of Replica Nodes  
We perceive the changes in the transmission rate of the replica nodes by passive measurements. For the measurements of the transmission rate, we smooth based on the time interval between two rate measurements, to prevent large jitters in the rate estimate. Inaddition, in order to solve the problem that the replica node may not be selected again due to poor performance in a short period of time, we also designed an \"activation\" mechanism.  
We maintain the \(RateRN\) value of the replica node, which represents the total rate of DATA packets transmitted between the edge node and the replica node.  
Therefore, the transmission rate \(Rate\) of the replica node is:  
\[Rate=\frac{RateRN}{\max(N_{c},1)}\;, \tag{2}\]  
where \(N_{c}\) is the number of NDC transmission processes that the current edge node uses the replica node.  
\(RateRN\) consists of two parts:  
\[RateRN=RateRN_{real}+RateRN_{extra}\;, \tag{3}\]  
where we update \(RateRN_{real}\) based on the measurement of the transmission rate of DATA packets between the replica node and the edge node.  
\[RateRN_{real}=RateRN_{real}*(1-X(t))+\frac{DataSum}{t}*X(t)\;, \tag{4}\]  
where \(t\) is the time since the last \(RateRN_{real}\) update. \(DataSum\) is the total size of DATA packets received within time \(t\). \(X(t)\) is a function about \(t:X(t)=c*t/(1+c*t)\) (\(c\) is an adjustable constant). \(X(t)\) can dynamically adjust the weight of the historical value and the current measurement value according to the time \(t\). When \(t\) is larger, the validity of the historical value is lower, so the weight of the current measurement value is larger. Conversely, historical values have a larger weight to smooth changes in the \(RateRN_{real}\) value.  
When the \(Rate\) calculated by the replica node is too small to be selected again, its \(RateRN_{real}\) will become 0, because there is no new data transmission. Even if its link condition improves, we cannot perceive it. Therefore, we add \(RateRN_{extra}\):  
\[RateRN_{extra}=\left\{\begin{array}{ll}RateRN_{extra}+t*d,&if\;RateRN_{ real}=0\\ 0,&if\;RateRN_{real}\;!=0\end{array}\right., \tag{5}\]  
where \(t\) is the time since the last \(RateRN_{extra}\) update, and \(d\) is an adjustable constant.  
In this way, even if there is no new data transmission, since \(RateRN_{extra}\) increases over time, the replica node will be reselected after a period of time, thus being \"activated\".
# Area-Efficient Universal Code Generator for GPS L1C and BDS B1C Signals
## 4 Proposed Area-Efficient Universal Code Generator
Therefore, in the case of the GPS L1C signals, the data channel and the pilot channel can share the Legendre ROM. In conclusion, the proposed universal code generator where the Legendre ROMs and associated circuitry in the structure of the Weil-generation universal code generator shown in Figure 4[17] are shared can be seen in Figure 5. Unlike the structure of the Weil-generation universal code generator with four 10,223 Legendre ROMs, two 10,243 Legendre ROMs, and two 3607 Legendre ROMs, the proposed universal code generator has only one each of the 10,223 Legendre ROM, 10,243 Legendre ROM, and 3607 Legendre ROM, and the complexity is reduced as these Legendre ROMs are shared. Although the latency increases due to the flip-flop and time-multiplexing to store the intermediate Legendre value as a trade-off, there is no degradation because the throughput exceeds 1.023 Mbps at operating frequencies not lower than the general MHz. In the case of the proposed universal code generator shown in Figure 5, 3.2 Kb of ROM is required with no RAM, and the latency increases by six clock cycles at the maximum for time multiplexing.
# Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review
## 3 Interpretation of Mining-Induced Microseismicity
### Microsesismic Focal Mechanism
[78] developed a preliminary criterion to determine the rock fracture mechanism utilizing source rupture parameters obtained by moment tensor analysis and revealed that the fracture and movement of the overlying rock structure is the main reason for mining-induced strong seismicities in the Dongtan coal mine. [PERSON] et al. [79] conducted moment tensor analysis to invert the focal mechanism of microseismicities with energy exceeding 50 J, determining the dip angle and the azimuth angle of the concealed reverse fault to be 73\({}^{\circ}\) and 307.5\({}^{\circ}\) in relation to the orbital roadway of the Dongjahe coal mine, respectively. Figure 4: \(E_{s}/E_{p}\) ratio of microseismicities and its correlation with local magnitude, revised from [72]. Figure 5: Beach ball representations of focal mechanisms for microseismic events in and around the residual coal pillars in Dongtan coal mine, revised from [76].
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 2 Background and Related Work
### Related Work
#### 2.2.3 Machine Learning-Based Methods  
Machine learning-based methods, on the other hand, utilise supervised learning algorithms to learn the model parameters using the data collected from encoded video sequences to provide optimal discriminative solutions to the CU size selection problem. For instance, Convolutional Neural Network (CNN) has been proposed in [34] to assist in the early CU size selection in HEVC intra-coding. The algorithm proposed by [PERSON] et al. does not rely on the spatial information, thereby facilitating parallel execution of the algorithm. However, use of a CNN as an inference engine adds up the complexity overhead of the decision-making process, which demands additional hardware acceleration elements. Following a different approach, [PERSON] et al. [35] and [PERSON] et al. [36] formulated CU size selection as a Bayesian classification problem. In this case, the former utilises Discrete Cosine Transform (DCT) coefficients; while the latter extends the classification to two-stage cascading classifiers. The three-class classifier used in the first stage in [36] groups the CUs into \"split\", \"non-split\", and \"undecided\" categories based on the dynamics of the feature space. In addition, the machine learning algorithms such as random forests and data mining techniques have been used in [37; 38], respectively, to address the complexity reduction problem in HEVC intra-prediction. SVM classification is often considered as a popular machine learning algorithm for binary classification problems that can also mediate the resulting overhead complexity on the encoding loop. In this regard, [39] utilises two linear SVM models that employ the depth difference and HAD and RD cost ratio as features to perform the decisions of early CU split and early CU termination. Following a similar approach, [PERSON] and [PERSON] [14] propose an early termination algorithm for recursive CU evaluation using Support Vector Machines (SVMs). In addition, [PERSON] et al. [10] propose an early termination/early split mechanism where multiple SVMs are utilised. The two-stage approach proposed in [10] utilises offline trained models and weight parameters for the decision making process. However, the offline computation of SVM weights and hyperplane and support vectors make the CU split decisions obtained through the models less content-adaptive.
# Impact of Intersection Left Turn Guide Lines Configuration on Novice Drivers' Behavior
## 3 Results
### Impact Analysis Based on Steering Angle and Speed Difference
#### 3.2.1 Small Intersections  
**Steering angle.** Figure 9 illustrates steering angle variation and range during left-turn maneuvers by novice drivers in different guide line scenarios at small intersections. The steering angles during left turns are generally similar across the three scenarios. In the straight section, the steering angle variation is smaller; in the circular section, the steering angle increases. The maximum rate of change in steering angle is 123.49%, 94.35%, and 74.59% for Scenarios 1-A, 1-B, and 1-C, respectively; Scenarios 1-A and 1-B occur while entering the circular section, while Scenario 1-C occurs while leaving, indicating a relatively stable left-turn process for novice drivers with full guide lines in small intersections. The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **M-Dual-N** & **M-Dual-O** & **L-Dual-O** & **L-Dual-F** \\ \hline M-Dual-O & \(p\) = 0.8501 & & & \\ M-Dual-F & \(p\) = 0.0082 & \(p\) = 0.0041 & & \\ L-Dual-O & & & \(p\) = 0.0005 & \\ L-Dual-F & & & \(p\) = 0.0005 & \(p\) = 0.2899 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Wilcoxon rank-sum test results for trajectories in the dual left-turn lanes scenarios.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenarios** & **L-Triple-N** & **L-Triple-O** \\ \hline L-Triple-O & \(p\) = 0.0065 & \\ L-Triple-F & \(p\) = 0.0413 & \(p\) = 0.1510 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Wilcoxon rank-sum test results for trajectories at the triple left-turn lanes scenarios.  
range of steering angles during the left turn is comparable across the scenarios, with maximum ranges of 0.41 rad, 0.34 rad, and 0.30 rad, respectively. This suggests similar stability in the left-turn process across the scenarios, with relatively better stability observed with full guide lines.  
**Speed difference.** Figure 10 illustrates speed difference distribution during left turns by novice drivers in different guide line scenarios at small intersections. Notably, the speed differences are relatively large when vehicles are entering or about to leave the straight section of the intersection, while the fluctuation in speed difference is smaller in the middle section. Both one guide line and full guide line scenarios show reduced fluctuations. The mean speed differences in the three scenarios are 1.523 km/h, 0.357 km/h, and 0.437 km/h, respectively, highlighting the impact of guide line settings on novice drivers' velocity stability during left turns.
## 3 Compact Modelling
### UTC Photodiode Compact Modeling
#### 3.2.2 Photocurrent  
Dynamic photocurrent \(I_{ph}(\omega)\) mainly depends on transit times across the absorber, \(\tau_{a}\) (in s), and the collector region, \(\tau_{c}\) (in s). Its amplitude decreases over the frequency range. The dynamic response of the photocurrent has been modified in the model to take the form [17]:  
\[I_{ph}(w)=S.J_{ph}(\omega=0)\frac{1}{1+j\omega\tau_{a}}.sinc\Big{(}\frac{ \omega\tau_{c}}{2}\Big{)}.e^{-j\frac{\omega\tau_{c}}{2}} \tag{4}\]  
where \(J_{ph}(\omega=0)\) is the static photocurrent density (in A/m\({}^{2}\)) and \(\omega\) is the angular frequency (in rad/s).  
Note that due to the low power and current density used in our experimental results, the dependence of the absorption coefficient on temperature has not been taken into account for photocurrent calculation.
# Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions
## 4 Experimental Evaluation
### Diagnosis of a Bearing with an Outer Race Fault  
In the outer race fault case, the right bearing is replaced by an outer race defect, as shown in Figure 17. Aiming to demonstrate that the method is robust to rotational speed changes and random shock interference, the vibration signals under three different acceleration conditions are analyzed, as illustrated in Table 5. In addition, white Gaussian noise (SNR = \(-\)3 dB) is added to the collected signal to make the detection more challenging.
## 4 Results and Discussion
### Differences per Geographical Region before and after FSMS Implementation
Finally, East, and West Asian companies established in Myanmar, Vietnam, China, Jordan and UAE reported similar changes to Indian companies yet greater significant increases in KPI implementation, Geographic infrastructure adequacy, Multi FSMS, Workers' training, Sustainability, Waste reduction programs, Traceability, Crisis management, Food Defense, and Food Fraud systems implementation. Such as in Iran and Pakistan, companies  
Figure 7: Median values of enterprise attitudes before and after implementation of FSM regarding specific constraints. Probability (\(p\)) values concern changes in attitude when both developed and developing countries are considered (Wilcoxon sign rank test, \(p<0.01\)). established in East and West Asia increased the integration of HACCP systems from 20-50% to 80% after FSMS implementation. Attitudes towards constraints of FSMS implementation as an expensive and complicated task were reduced for North American, African, Latin American, and Asian companies (except for Iran and Pakistan). The constraint of non-familiarity of FSMS to customers and consumers increased for North American, European, African, Latin American, and Asian companies (except for Iran and Pakistan). For North American and African companies, the incentives of reduction in product loss and meeting customers' requirements were decreased, while the incentive of providing evidence for legal compliance increased. European companies were the only geographical sector that increased the belief in avoiding duplication between processes. Companies from Latin America and India presented a positive shift in the belief that FSMS standards implementation improves the quality of management.
# A Multi-Dimension Spatial Method for Topology Awareness and Multipath Generating
## 2 Background and Problem Statement
### Multipath Diversity  
Multipath diversity means the transmission differences of multiple paths, and higher multipath diversity indicates that there exist fewer joint nodes or links in underlay networks between paths. Multipath diversity is an important issue that significantly influences the performance of multipath transmission--for instance, paths obtaining high multipath diversity could efficiently aggregate different resources of multiple paths, and one individual path would bring less impact to overall paths when multipath diversity is higher. Multipath diversity is determined by many factors in bearing networks and we should explore and exploit the diversity of transmission resources in existing underlay bearing networks and enhance multipath diversity.
## 2 Materials and Methods
### Assessment of Segmentation Accuracy
#### 2.5.3 Local Definition of Accuracy  
Following the establishment of the one-to-one relationship between the two parcel groups, three local metrics of similarity were computed based on the relationship between the areas of reference and the segmented parcels. First, the metric overlapping area rate (OlpAR) was determined by calculating the ratio of the overlapping area between each segment (S) and its reference parcel (R) to the total reference parcel area:Figure 8: 100 cadastral parcels selected as a ground truth are outlined in bold and are superimposed on their corresponding coverage classes. The location of the test area is shown as the red dot in the incept.  
Figure 9: (**a**) Parcels selected as ground truth due to their effective recognizability in the orthophoto (outlined in blue). Conversely, the yellow outlines indicate other parcels that were present in the initial cadastral parcel dataset but not detectable in the orthophoto. Therefore, they were discarded from the sampling. (**b**) An example of an establishment of one-to-one correspondence between segment and reference. Indeed, among all the segments (outlined in grey) intersecting the reference parcel, only those exhibiting the highest degree of overlap (outlined in yellow) with the reference parcel (outlined in blue) were selected as corresponding segmented parcels.  
Subsequently, the metric overflowing area rate (OfAR) was determined by calculating the ratio of the segment area overflowing the boundaries of its reference to its total area (Figure 10):  
\[OfIARi=\frac{A(Si\cap Ri)}{A(Si)} \tag{2}\]  
Subsequently, each overflowing area rate was subtracted from the corresponding overlapping area rate. Indeed, the ideal total correspondence is defined as a 100% of the overlapping area rate between the segment and its reference, with a 0% overflowing area rate outside the boundaries of the reference. The result, which potentially ranges between \(-100\%\) and \(+100\%\), was then normalized to a positive range of 0-100% to define the local overall accuracy (LOA):  
\[LOAi=\frac{[(OlpARi-OfIARi+1)}{2}\times 100 \tag{3}\]  
Concurrently, the photointerpretation of each case determined that an 80% local overall accuracy threshold generally represented the minimum required to observe correctly delineated segmentation parcels.  
Finally, a third local metric named area difference (AD) was calculated to complement the local overall accuracy with additional information: the difference between each segment's area and that of the corresponding reference.  
\[\mathrm{AD}i=\mathrm{A}(Si)-\mathrm{A}(\mathrm{R}i) \tag{4}\]  
This subtraction resulted in a binary evaluation: positive values indicated that segmented areas were larger than reference areas, thus indicating undersegmentation errors in cases in which local overall accuracy was below 80%. Conversely, negative values, indicating segmented areas smaller than reference areas, served as a proxy for oversegmentation errors with the same accuracy threshold.
## 3 PHM System Driven by Model Data Hybrid
### System Architecture
#### 3.1.2 Health Management Layer  
Based on the fusion of telemetry data and simulation data, the health management layer can provide health management services, which can be divided into four aspects: health monitoring, fault diagnosis, fault handling decision and status prediction.  
1. Health monitoring. The basis of the health management service is the real-time monitoring of the running status of the digital model of the satellite constellation. With the injection of real-time telemetry data, the digital model can track the dynamic changes of the physical entity. Then, simulation data, real data, and fused data between the two are extracted from digital models to fully reflect the running status of the constellation using visualization techniques. At the same time, the monitored data shall be interpreted in real time according to specific interpretation rules by means of threshold analysis and deviation analysis. If the data are judged to be abnormal, fault diagnosis shall be performed.
2. Fault diagnosis. The fault diagnosis service is enabled when the health monitoring service has a fault warning. It analyzes the characteristics of the change process of abnormal telemetry parameters by combining abnormal telemetry parameters and the corresponding model simulation parameters. Then, using the simulation data as a reference, advanced fault identification algorithms are used to determine the faulty components and causes.
3. Decision on fault handling. After the fault diagnosis service identifies the cause of the fault, the O&M personnel shall select a set of maintenance strategies from the prearranged maintenance plan and verify the effectiveness of such strategies on the digital model before implementation.
4. Status prediction. Status prediction may occur in two scenarios: (1) Before executing a task, the O&M personnel obtain the running data of the next time period through status prediction and then evaluate the risk of the task based on the data and make predictive adjustments to the task plan. (2) The initial, inconspicuous fault features discovered by the health monitoring service may not be identified by the fault diagnosis algorithm. Through status prediction, more definite fault data can be obtained to support the fault diagnosis service.
## 4 Results
### Neurophysiological Characterization of Mental Workload in Industry 5.0
These were calculated according to the following equations:  
\[\text{TAR}=\frac{P_{\theta}}{P_{\alpha}},\]  
\[\text{TBR}=\frac{P_{\theta}}{P_{\beta}},\]  
\[\text{EI}=\frac{P_{\beta}}{P_{\alpha}+P_{\theta}}\]  
where \(P_{\theta}\), \(P_{\alpha}\), and \(P_{\beta}\) represent the spectral powers in theta, alpha, and beta frequency bands, respectively. TAR is an index of workload and mental effort, based on the assumption that an increase in mental load is associated with a decrease in alpha power and an increase in theta power. TBR is associated with working memory, attention, and drowsiness and assumes that an increase in alertness and task engagement results in an increase in beta power and a decrease in theta power, while EI reflects mental effort, vigilance, attention, alertness, and task engagement [55; 56]. Concurrently, a subjective assessment was conducted utilizing a subjective questionnaire, namely the NASA Task Load Index, with the objective of establishing a correlation between the scores obtained by the latter and the EEG indices. The intention was to ascertain which EEG indices are responsive to alterations in specific workload components as measured by the NASA-TLX. The correlation analysis revealed a significant positive correlation between the TAR and the mental demand score, a significant positive correlation between the TBR and the mental demand score, a significant negative correlation between the TAR and the overall performance score and a significant negative correlation between the EI and the effort score. EEG and fNIRS features collected during an examination of the mental workload of workers in an intelligent factory environment were used in a machine learning approach to predict traditional measures (subjective and behavioural) [11]. Two methods were employed: the first one was linear regression, which was used to identify correlations between variables. The second method was artificial neural networks (ANN), which were used to capture any non-linear relationships. Models were constructed using a variety of predictor variables (EEG only, fNIRS only, or a combination of EEG and fNIRS) and target variables (subjective measures (NASA-TLX score), behavioural measures (missed beeps and reaction times). Finally, the KNN (K-Nearest Neighbours) classification was also implemented to assess the accuracy of the predictions. For NASA-TLX as the target, the highest accuracy, where 69.4% was achieved using all EEG frequency band powers as features while leaving one subject out for testing (first cross-validation method), whereas the second cross-validation method resulted in the highest accuracy of 58.3% when only fNIRS features are used.