esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 2 NB-IoT
### NB-IoT Key Technologies
#### 2.2.5 eDRX  
Discontinuous reception (DRX) is a technology to deactivate the UE modem during a specific period that is negotiated with base station. Hence, the UE turns on the modem and monitors the downlink channel only during a specified time. Consequently, the transmission is delayed until the next monitoring period when the base station has a packet to send and the UE modem is deactivated.  
DRX is classified into idle and connected DRX according to the radio resource control (RRC) connection state. In idle DRX, the RRC is not connected with base station, and the UE monitors only the paging signal for 1 ms per DRX period. Thus, it is possible to save energy by activating the modem only in the monitoring period. Moreover, as the DRX cycle becomes larger, the average energy consumption decreases and the battery life increases. However, a long DRX period increases the response time, thus compromising the user experience. Taking the user experience effects into account, the maximum DRX period of an LTE network is 2.56 s. In contrast, energy consumption is more important than an immediate response for the IoT. Thus, the DRX period can reach 175 min in the NB-IoT by employing hyper frame number. Figure 5 shows the maximum period of DRX and eDRX along with the UE power state.  
Figure 4: Time relation for data transmission.  
Figure 5: _Cont_.
# Research on Sentiment Classification of Online Travel Review Text
## 4 Discussion
The second part of the research involved comparing the two types of sampling methods and identifying which of these is more suitable for use in solving the uneven sentiment distribution problem in online review texts. This article fully describes the key aspects of online travel review text sentiment classification, establishes an effective sentiment classification research framework for online travel review text, and validates the proposed method based on a relatively extensive sample. The work put forward in this paper aims to emphasize and improve the methodological relevance and applicability of sentiment analysis. However, there are some limitations:  
* Studies have shown [10] that deleting comment text without emotional content can improve the accuracy of emotional classification. This idea is worth examining in future. * In terms of keyword selection, this study only selected the keyword with the largest TextRank value. The questions of how to choose keywords with the same value, whether more keywords can be introduced for semantic expansion, the relationship between these choices, and the accuracy of sentiment classification are also worthy of further study. * In the Word2 vec-based text representation method, the use of different Word2 vec corpora will yield different results. The best approach would be to train a corpus of specific topics [73]. * In terms of automated classification methods, studies have shown that the combination of LSTM (Long Short-Term Memory) and attention mechanisms [74] has resulted in excellent emotion classification results. However, the question of whether these novel methods are suitable for the research object of this article is worthy of further study. * In terms of experimental subjects, this article only studies English reviews from TripAdvisor, and does not investigate other online travel platforms and other languages. Therefore, it is highly advisable to investigate data in other languages and other platforms to verify the applicability of this method. Conceptualization, W.C., Z.X. and Y.L. ; Methodology, W.C.; Software, W.C.; Validation, W.C., Q.Y. and X.Z. ; Data curation, W.C.; Writing--original draft preparation, W.C.; Writing--review and editing, W.C., Z.X. and Y.L. ; Supervision, Y.L. ; Project administration, Y.L. ; Funding acquisition X.Z., Q.Y. and Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China under Grants 61972439, 61672039, 61702010, 61772034. The authors would like to acknowledge all of reviewers and editors. The authors declare no conflict of interest.
## 4 Results and Discussion
### Regular Waves  
Since the floating wind turbine operates in a marine environment, the platform is subjected to waves, resulting in periodic motion. It is well known that the waves acting on a floating platform are very complex, the low-order waves will cause the platform to vibrate in the same period near the initial position, while the higher-order waves cause the platform to move in the direction of the wave [42]. In addition, because the platform is limited by the mooring system, it will oscillate near the equilibrium position after a period of time. Considering that the surge, heave, and pitch motions of the floating platform are the most obvious motions, these three components are then selected to be examined,  
Figure 8: Floating offshore wind turbines (FOWT) platform motions in the free-decay test. (**a**) LCI; (**b**) LCII; and (**c**) LCIII. Figure 7: Comparison of the sway and yaw responses between the experiment and the in-house developed code. (**a**) sway; (**b**) yaw. It can be found that with an increasing number of offset columns, the maxima of the surge response gradually decrease, see Figure 9. Likewise, the minima and averages also have the same pattern. For example, in LC2, when the number of offset columns is changed from 3 to 6, the maximum surge is reduced from 6.16 m (Model0) to 5.88 m (Model1) and 5.26 m (Model2), while the minimum surge is reduced from 3.17 m (Model0) to 2.94 m (Model1) and 2.49 m (Model2). Comparing the three load conditions LC1-LC3, it can be found that the maximum value of the surge response increases slightly due to the increase of wave height and period. Under the same load condition, with the increase of the number of offset columns of the floating platform structure, the maximum value of the surge response still gradually decreases. It should be pointed out that the amplitude of the surge motion is also decreased as increasing the number of the columns. The amplitude surge motion of the 3, 4, and 6-columns platform is 0.69 m, 0.64 m, and 0.55 m, respectively, for LC1. While for LC2, the amplitude surge motion is decreased from 1.46 m (3 columns), to 1.44 m (4 columns) and 1.41 m (6 columns). For LC3, it is 2.61 m (3 columns), 2.57 m (4 columns), and 2.52 m (6 columns). The surge amplitude will affect the aerodynamic load and dynamic response of the whole system due to the additional relative wind speed caused by the surge motion, as a result, a small variation of hydrodynamic load due to these surge amplitude motions will be introduced, which has been discussed in detail in the studies by [PERSON] et al. [43] and [PERSON] et al. [44]. For the heave motion, the amplitude is relatively small, basically in between 0 m and 1 m, as shown in Figure 10. For LC1, when the number of columns is 3, the maximum value is positive, but close to 0. If the number of columns increases to 4, the maximum decreases slightly. Different from
# Dynamic Modeling and Optimization of Tension Distribution for a Cable-Driven Parallel Robot
## 6 Conclusions  
The cable tension distribution of cable-driven parallel robots is one of the research focuses because it has a direct impact on the movement characteristics. To make the cable tension distribution more continuous and even, an optimization method based on the firefly algorithm is proposed for the tension distribution of cables. The structural design of the parallel robot is introduced in detail. The dynamic equation of the cable-driven parallel robot is derived from the Newton-Euler method. Then, an optimization algorithm for cable tension based on minimum variance optimization is presented to solve the problems of the insufficient stiffness of the optimized system and the discontinuity and non-uniqueness of cable tension. Finally, a simulation model is built using MATLAB software to verify the proposed method. The following conclusions can be obtained:  
1. The cable tension optimization method introduced by minimum variance is adopted so that the optimized cable tension is evenly distributed near the average value of the preset extreme tension, and the optimization range of the tension is determined by the extreme tension value.
2. Taking the improved minimum variance of the correlation force as the optimization objective and using the improved minimum variance method to optimize the cable tension, the change in cable tension by the proposed method is smoother than that of the traditional method. In addition to this, all cable tension curves are continuous. Nevertheless, they are discontinuous for traditional methods.  
It should be pointed out that this paper mainly considers optimization when cables are not. In the future, we will strive to do further research on cable tension distribution, taking into account the plastic deformation of cables.  
Conception, K.W. and Z.H.H.; methodology, K.W.; investigation, C.S.Z. and C.W.D.; writing--original draft preparation, K.W.; writing--review and editing, K.W., Z.H.H. and C.S.Z.; software, K.W. and C.S.Z.; validation, K.W., Z.W.H. and K.W.; formal analysis, K.W. and Z.H.H.; project management, K.W., Z.W.H. and C.S.Z.; supervision, K.W. and Z.H.H.; funding acquisition, K.W., Z.H.H. and C.W.D. All authors have read and agreed to the published version of the manuscript.  
Figure 18: The stiffness improvement coefficient is 0.3.  
**Funding:** This study was supported by the National Natural Science Foundation of China (62303205), the Natural Science Foundation of Shandong Province (ZR2023 QF137), and the Doctoral Startup Fund of Liaocheng University (318052231).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Engineering Overview
### Bursting Vibration Data  
The blasting vibration monitoring adopts the Blast UM type blasting vibration monitoring instrument, including vibration sensors, converters, amplifiers, data processors, recorders, and other equipment. The system can simultaneously record the original waveform of blasting vibration in the \(x\), \(y\), and \(z\) directions. The range of the instrument is as follows: vibration speed of 0.001-35 cm/s. Frequency range: 5-300 Hz. According to the testing requirements, we set the sampling rate to trigger an electrical frequency of 0.035 cm/s and recorded for 2 s. The measuring point is located along the west bank of Yanshan Iron Mine's highway. The \(x\)-direction vibration velocity of the particle is between 0.1645 to 1.7489 cm/s, and the main frequency of vibration is distributed between 5.3 and 29.3 Hz. The \(y\)-direction vibration velocity of the particle is between 0.1336-1.4509 cm/s, and the main frequency distribution is between 5.8-29.3 Hz. The \(z\)-direction vibration velocity of the particle is between 0.0903 to 1.5523 cm/s, and the main frequency distribution is between 5.0 and 29.8 Hz. The combined velocity of particle vectors mainly ranges from 0.1471 to 2.1185 cm/s.  
Figure 2 shows the process of obtaining the velocity of particle blasting vibration. Through spectrum analysis of multiple test signals, it was found that the main frequency components of the effective signal are concentrated between 0-60 Hz. Based on engineering requirements, a low-pass filter with a cutoff frequency of 60 Hz was selected. Taking the \(z\)-direction as an example, the frequency spectrum analysis of particle vibration velocity in this direction is shown in Figure 2a. By applying high-pass filtering to the waveform, the true three-dimensional waveform of blasting vibration can be obtained, as shown in Figure 2b. We combined the waveforms in the \(x\), \(y\), and \(z\) directions into a vector to obtain the particle velocity, as shown in Figure 2c; this is also the blasting waveform input in the subsequent numerical simulation dynamic calculation. These waveforms have been filtered and calibrated using the method described in reference [33; 34] to meet the  
Figure 1: Overview of the mining area.  
accuracy required for dynamic calculations. Since velocity-time waveforms can be applied to boundaries other than the adhesive boundary (stress waves, velocity waves, acceleration waves, and displacement waves can all be applied to free boundaries, but only stress waves can be applied to adhesive boundaries), the processed velocity-time waveforms are directly applied to the free boundary of the model, which measures 50 m \(\times\) 50 m on the upper side, to simulate borehole blasting.  
Figure 2: _Cont._
## 4 Conclusions  
The results verified that all three different optical glass fibers can be applied for FCS applications. Following this, the resistance of the optical glass fibers to highly alkaline environment was evaluated where all three fiber coating materials showed degradation and thus not provide full protection against highly alkaline attack. However, optical glass fibers with carbon coating showed the most promising results. Carbon coated optical glass fibers of two different fabrication batches were evaluated. One fabrication batch showed almost no degradation due to alkaline attack while the failure strain of fibers of the second fabrication batch decreased with increasing exposure time to highly alkaline solution. Therefore, it is assumed that micro defects of the fiber coating of the second fabrication batch caused the degradation of the failure strain. The evaluation of the sensor response of the 1D-FCS before and after exposure to the NaOH solution indicated that the FCSs are relatively stable against alkaline pore water attack. One reason for this might be that the PVA that is used to stabilize the FCS is known to be relatively inert against chemicals. In addition, the response of the fiber optic sensors inside the FCS were investigated for three different fiber configurations. The purpose of the second and third configurations was to explore the spatial variation of the optical fiber inside the FCS and whether this results in a stronger bond between the optical glass fiber and FCS. The obtained results suggest that the bonding length between the optical fiber and the carbon filament should be at least \(\geq\)150 mm long in order to provide sufficient sensor sensitivity. Currently, work is ongoing to develop a theoretical model that will be used to simulate the work presented herewith, paving way to evaluate the optimum conditions for improved durability.  
Conceptualization, K.B., F.W., and B.R.; software, K.B. and M.K.; validation, K.B. and Y.Z., F.W. and M.K.; formal analysis, K.B., M.K., and F.W.; investigation, K.B., Y.Z., F.W., L.S.M.A., and M.K.; writing--original draft preparation, K.B. and L.S.M.A.; writing--review and editing, B.R.; supervision, B.R. and R.H.; project administration, B.R. and R.H.  
This research was funded by the Bundesministerium fuer Bildung und Forschung (BMBF) within Grant Number 03 ZZ0345 (Carbon Concrete Composite (C3)). B.R. acknowledges support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122). The publication of this article was funded by the Open Access fund of Leibniz Universitat Hannover.  
The authors declare no conflict of interest.
# Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things
## 4 WUR in Cellular Standards  
Existing cellular signals employ complex modulation techniques that enable high data throughput but require high-performance and high-power receivers. For example, legacy NB-IoT DL uses OFDM with QPSK, and legacy 5G NR uses OFDM with 64-QAM. Designing a WUS with a simpler modulation scheme can help reduce WUR overhead, but might sacrifice, for example, throughput (data rate) and bandwidth efficiency.
## 3 Results and Discussion
### Scenario 2  
In this scenario, the BER and throughput performance of SCU and SEU are investigated using WHT-NOMA with IRC compared to conventional NOMA with and without IRC. To measure the performance, for simplicity, a two-UE-pair case is used, i.e., an SCU (high-gain user) paired with a SEU (low-gain user). Both SCU and SEU experience ICI from a MU that is not able to connect to the SBS; the interfering signal strength is considered as 20 dBm. To manage the situation, IRC is used for ICI suppression at both SCU and SEU. The results are generated using power allocation as SCU = 0.2 and SEU = 0.8, where the total power is equal to 1, i.e., \(P_{SCU}+P_{SEU}=1\). The scenario is shown in Figure 5b and the results are presented in Figures 1  
Figure 16: BER performance with IRC using QAM modulation; NOMA vs. WHT–NOMA, power allocation: SCU = 0.2 and SEU = 0.8.  
Figure 17: BER performance with IRC using 8–QAM modulation; NOMA vs. WHT–NOMA, power allocation: SCU = 0.2 and EU = 0.8.  
Figure 18: Throughput performance with IRC using BPSK modulation; NOMA vs. WHT-NOMA, power allocation: SCU = 0.2 and SEU = 0.8.  
Figure 19: Throughput performance with IRC using QPSK modulation; NOMA vs. WHT–NOMA, power allocation: SCU = 0.2 and SEU = 0.8.  
Figure 20: Throughput performance with IRC using QAM modulation; NOMA vs. WHT–NOMA, power allocation: SCU = 0.2 and SEU = 0.8.
###### Abstract  
Tool condition monitoring is one of the classical problems of manufacturing that is yet to see a solution that can be implementable in machine shops around the world. In tool condition monitoring, we are mostly trying to define a tool change policy. This tool change policy would identify a tool that produces a non-conforming part. When the non-conforming part producing tool is identified, it could be changed, and a proactive approach to machining quality that saves resources invested in non-conforming parts would be possible. The existing studies highlight three barriers that need to be addressed before a tool condition monitoring solution can be implemented to carry out tool change decision-making autonomously and independently in machine shops around the world. First, these systems are not flexible enough to include different quality requirements of the machine shops. The existing studies only consider one quality aspect (for example, surface finish), which is difficult to generalize across the different quality requirements like concentricity or burrs on edges commonly seen in machine shops. Second, the studies try to quantify the tool condition, while the question that matters is whether the tool produces a conforming or a non-conforming part. Third, the qualitative answer to whether the tool produces a conforming or a non-conforming part requires a large amount of data to train the predictive models. The proposed model addresses these three barriers using the concepts of computer vision, a convolution neural network (CNN), and transfer learning (TL) to teach the machines how a conforming component-producing tool looks and how a non-conforming component-producing tool looks.  
tool condition monitoring; tool change policy; Industry 4.0; machine learning; CNN; AI +
Footnote †: journal: Applied Sciences
## 2 Statistical Models
### Latent Dirichlet Allocation  
Latent Dirichlet Allocation (LDA) [12] is an algorithm representing documents as a mixture of topics based on the probability of a word belonging to those topics. The method can be described in a few steps. For every document, parameters \(\alpha\) and \(\beta\) are computed according to the known a priori number of topics, denoted as \(T\), where \(\alpha\) is the probability distribution assignment of documents to topics and \(\beta\) is the probability distribution of assignment of words to topics. Afterwards, for each document \(d\) parameter \(\theta_{i}\) that is the probability distribution of topic assignment in document \(i\) is drawn from the Dirichlet distribution parametrized by \(\alpha\). Next, for each topic parameter \(\phi_{t}\), the probability distribution of word assignment to topic \(t\) is drawn. Afterwards, for each word \(j\) in each document \(i\), topic \(z_{ij}\) is drawn from multinomial distribution parametrized by \(\theta_{i}\).  
In the beginning, it is necessary to prepare data for the LDA algorithm. A dictionary \(V\) of size \(M\) and a matrix of size \(D\times\max(N)\) are created, where \(D\) is a number of documents and \(\max(N)\) is the maximum length of a document in terms of the number of words among all documents. The next step is to randomly assign topics to words and to calculate the frequency of assignments of each word to each topic. Then, a matrix of size \(M\times T\) of the initial probability distribution of words over topics is computed. By analogy, the matrix of size \(D\times T\) of the probability of assigning documents to topics is calculated.  
Instead of sampling from multinomial distribution, the Gibbs sampling algorithm [12] is often used (Appendix B).  
After the Gibbs sampling algorithm finishes, the probability distribution of the assignment of the document to one of \(T\) topics can be calculated by summing a number of words assigned to each topic.
# Two-Dimensional Modelling Approach for Electrokinetic Water Transport in Unsaturated Kaolinite
## 2 Materials and Methods
### Numerical Model Implementation  
The proposed conceptual model corresponds to a new version of the M4 EKR tool, which is fully implemented in COMSOL Multiphysics [30]. As in other previous adaptations of the M4 EKR code, all equations were implemented using the constitutive models presented in Section 2.1; therefore, no preprogrammed COMSOL modules were used. In the simulations, two partial differential equations for the mass balance of water in the soil and the electric charge balance were solved. \(P_{\rm L}\) and \(\psi\) were selected as state variables. In addition, N + 1 ordinary differential equations (ODEs) were used to solve the variation in the electrolyte volume in the 'N' reservoirs present in each simulation and an additional ODE for calculation of the energy consumption.  
The above equations are solved simultaneously using a monolithic, fully coupled approach. COMSOL Multiphysics controls the preprocessing (discretization, initial, and boundary conditions), operates the solver of the system of the set of partial and ordinary differential equations, and manages the postprocessing of the results.