esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
After experiencing different temperatures, the uniaxial compressive strength, average modulus, modulus of deformation, initial modulus, ultimate strain, and longitudinal wave velocity of coarse sandstone samples show that the high temperature effect has a certain influence on the physical and mechanical parameters of coarse sandstone. Since the coarse sandstone sample is a heterogeneous anisotropic body, the test results are scattered widely, but there are obvious rules on the whole. For uniaxial compression, the load-bearing capacity and deformation resistance of the specimen are  
Figure 17: Relationship between three axis strength and confining pressure and temperature after high temperature.  
strengthened when the temperature is less than 500 \({}^{\circ}\)C. Compared with the room temperature of 25 \({}^{\circ}\)C, the strength, average modulus and peak deformation of the specimen are improved in varying degrees. When the temperature exceeds 500 \({}^{\circ}\)C, the average strength increases by 34%, the average modulus increases by 17.4%, the peak deformation increases by 27.8%, the deformation modulus decreases slightly by 7.5%, especially the wave velocity, which decreases by 53%.  
The coarse sandstone sample is a kind of high-porosity rock. The rock particles are not very compact, and there are more pores and fissures between them. These larger pore structures can accommodate deformation and prevent crack propagation when the temperature is lower than 500 \({}^{\circ}\)C. The thermal expansion of mineral particles may also cause the primary cracks in coarse sandstone sample to close gradually, and the number of cracks decreases, the internal compactness of the sample is improved. When the temperature decreases, the contact relationship between mineral particles can be improved, the friction characteristics can be enhanced, and the load-bearing capacity and anti-deformation ability of the samples can be strengthened.  
When the temperature exceeds 500 \({}^{\circ}\)C, the different thermal expansion rates of the mineral particles inside the coarse sandstone sample cause the inconsistency among particles, which makes the mineral particles or within particles, i.e., structural thermal stress. Once the structure thermal stress reaches or exceeds the ultimate strength of the material, micro-cracks will occur, which will weaken the bearing capacity and deformation resistance of the coarse sandstone sample. With the increase of temperature, the inter-particle stress will further increase, resulting in produce more micro-cracks in the coarse sandstone sample, or the expansion, broadening and connection of primary cracks. At the same time, the reduction of the rigidity of the cements will also affect the deformation of the sample. Macroscopically, the mechanical properties of the coarse sandstone will deteriorate, and the bearing capacity and deformation resistance of the sample will decrease. It can be seen that the threshold temperature of mechanical parameters of coarse sandstone is 500 \({}^{\circ}\)C.
# Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia
## 3 Methodology  
This study evaluated the performance of three global precipitation product and their downscaled versions (CHIRPSv2, TAMSATv3, PERSIANN_CDR, CHIRPS_D, TAMSAT_D, and PERSIANN_CDR_D) at different spatial and temporal scales against 27 ground gauge stations from 1983 to 2014. Categorical and quantitative evaluation index techniques were applied to WSRB, Ethiopia.
## 1 Introduction
Most of them require a precise system model and are sensitive to parameter tuning. The variants of the PIDs such as NPID and SPID have more tuning parameters than the standard PID of our reference vehicle, which could make the design and optimization difficult. Therefore, this article presents the design and control of the hybrid powertrain of the VW Crafter based on the online measurement CAN bus data, comparing the ICE and hybrid powertrain of the proposed vehicle. The research proposes GA-PID and compares with PSO-PI and FOPID control algorithms to reduce the proposed vehicle's fuel consumption and CO\({}_{2}\) emissions, justifying the effectiveness of the proposed controller. This article is an improved version of our paper submitted at the IEEE PEMC 2024 conference [35]. However, this research reuses the existing experimental findings of our previous studies based on a new methodology for the vehicle monitoring system to give an in-depth analysis of the conventional and hybridized powertrain of the proposed vehicle. The primary contributions of this article are as follows:  
* This article presents transforming a conventional VW Crafter into a hybrid vehicle, validating the power and torque requirements for the first time. A very few literature performed similar research. * An enhanced GA-PID is proposed and comapred with PSO-PI and FOPID controls to optimize fuel consumption, reduce CO\({}_{2}\) emissions, and extend the 2011 Nissan Leaf battery pack range for the VW Crafter hybrid vehicle. * This research adapted the experimental setup of our previous article for the electric mode from our previous study [35; 36], which was obtained based on a novel data acquisition (DAQ) approach, enabling online CAN bus analysis for advancing the existing literature for optimizing the hybrid powertrain and proving the detailed analysis of the transformation process from the conventional to hybrid. * This paper also presents a comprehensive performance comparison between the conventional and hybrid powertrains of the VW Crafter, providing valuable insights into the benefits and limitations of electrified hybrid powertrains. The remaining part of this paper is arranged as follows: Section 2 presents the development workflow and the mathematical background descriptions of the vehicle systems. Section 3 presents the development of the control strategy and optimization. Section 4 presents the model development and simulation. Section 5 presents the experimental and simulation results. Section 6 presents a detailed discussion of the results. Finally, Section 7 presents the conclusions.
# Dual-Probe Near-Field Phaseless Antenna Measurement System on Board a UAV
## 2 System Description
### Near Field Measurements Processing
Then, the complex NF can be used for antenna diagnostics (NF-NF transformation) or for the calculation of the radiation pattern of the AUT (NF-FF transformation). The reason why pSRM [16; 18] was chosen over other iterative phase retrieval techniques [15; 17] is due to its capacity to work with non-canonical, non-uniformly sampled acquisition surfaces, which is likely to occur in the case of airborne-based measurement systems. As explained in [16], either the SRM or its phaseless version, pSRM, are based on the electromagnetic integral equations relating an equivalent currents distribution defined on a surface enclosing the AUT with the field radiated by these equivalent currents. These integral equations are discretized using piecewise basis functions. Other phaseless techniques for antenna measurement are based on wave mode expansion (e.g., planar [17], cylindrical [25], and spherical [26]), which are computationally much more efficient than the SRM/pSRM, but they require the NF to be uniformly sampled. Compressed sensing techniques can deal with sparse measurements as well [27], even in the case of phaseless measurements [28], resulting in measurement time reduction. However, sparse sampling patterns can be complex to be translated into a UAV pre-defined flight path.
# Joint Design of Transmit Waveforms for Object Tracking in Coexisting Multimodal Sensing Systems
## 4 Waveform-Dependent Performance Optimization Methods
### Approach I: Coexistence Waveform Design Approach by Minimizing System Interference  
We designed the optimal radar parameter set in Equation (7) by minimizing the interference between the radar and the MU communications system. This requires that the radar have some prior knowledge on the transmit parameter set of the collocated communications system. As expected, the performance of the CoWS design is improved as additional prior information on the communications transmit waveforms becomes available the radar.  
In the following sections, we assume that the coexisting radar and MU communications system are using the CoWS design based on the OLFM waveforms with FM rates as in Equation (25) and shared bandwidth \(B\).
## 2 Review of Phase-Locked Loops  
This section presents a review of the results widely used in PLL design. Specifically, the mathematical models for PLLs are reviewed in the continuous-time and discrete-time domains, with a special emphasis on each digital integration method implementing the loop filter and the NCO.
## 3 Overview of the Improved Model Reference Adaptive Control Framework
### Standard Model Reference Adaptive Control (MRAC)
The actuators in HDVs--namely, the powertrain and braking system--have strict physical limits in terms of maximum driving torque and braking force. When the control inputs calculated using MRAC exceed physical constraints, the vehicle cannot perform the commanded action. This results in degraded tracking accuracy, increased steady-state errors, and potential instability. Furthermore, excessive demands on actuators may compromise hardware safety and durability. Second, while MRAC's high adaptation gain is beneficial for rapid response, it can introduce high-frequency oscillations in the control input. These oscillations may propagate through the vehicle system, leading to undesirable effects, such as reduced ride comfort, increased powertrain vibrations, and premature wear of mechanical components, including the brakes and suspension system. In heavy vehicles, where ride stability, component longevity, and energy efficiency are crucial, such high-frequency behaviors are particularly problematic. In other words, while MRAC provides a strong theoretical foundation for adaptive control, its direct application to HDVs requires additional considerations. Practical constraints, such as actuator limits and system robustness against high-frequency adaptation, must be explicitly addressed through modifications to the standard MRAC structure. This need for adaptation highlights the necessity of HDV-specific changes to MRAC, such as the incorporation of input saturation handling mechanisms, which will be discussed in the following section.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AGC & Automatic Generation Control \\ ANNs & Artificial Neural Networks \\ Bi & Bidirectional \\ CENACE & Centro Nacional de Control de Energia \\ CV(RMSE) & Coefficient of Variation of the Root Mean Square Error \\ DALF & Day-Ahead Load Forecasting \\ DWT & Discrete Wavelet Transformation \\ EMD & Empirical Mode Decomposition \\ EV & Electrical Vehicle \\ CCRORI & Gerencia de Control Regional Oriental \\ GHG & Greenhouse Gas \\ GHI & Global Horizontal Irradiance \\ HVAC & Heating, Ventilation, and Air Conditioning \\ LSTM & Long Short-Term Memory \\ LSTM NN & Long Short-Term Memory Neural Network \\ MAPE & Mean Absolute Percentage Error \\ MSE & Mean Square Error \\ N-BEATS & Neural Basis Expansion Analysis \\ NARX & Non-Linear Auto-Regressive with Exogenous Inputs \\ NNs & Neural Network \\ nRMSE & Normalized Root Mean Square Error \\ NSRDB & National Solar Radiation Database \\ RLCs & Representative Load Curves \\ RMSE & Root Mean Square Error \\ RNNs & Recurrent Neural Networks \\ RT & Real Time \\ S2S & Sequence to Sequence \\ SEN & Sistema Electrico Nacional \\ STLF & Short-Term Load Forecasting \\ Uni & Unidirectional \\ \end{tabular}
# Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier
## 4 Application to Seismic Records of Typhoon Periods
### Typhoon Soudelor in 2015  
Since the rainfall and damage caused by Typhoon Morakot occurred in southern Taiwan, the seismic data collected during Typhoon Soudelor on 6-10 August 2015 were used to examine the present classifier for the storm event occurred in Northern Taiwan. Visual detection of events in the earthquake catalog of the CWB indicated 17 landslide events and 70 earthquake events during Typhoon Soudelor. Of the landslide events, 15 records were measured and identified by at least three seismic stations, and the remaining two were detected by single stations. The accuracy of classification was 62.1%, as shown as Table 4. The present classifier succeeded in identifying all 17 landslide events, so the sensitivity for landslide cases was 100%. Of the 70 earthquake events, 37 records were classified correctly, and the remaining 33 records were identified as landslide events. The sensitivity for earthquake cases was 52.9%. Of the 50 records classified as landslide events, 17 were landslide events, and the other 33 belonged to earthquake events. The precision for landslide cases was 34.0%. All 37 records classified as earthquake events were confirmed by visual detection, so the precision for earthquake cases was 100%. Comparing the classification results of Typhoon Morakot and Typhoon Soudelor, the results for Typhoon Soudelor had lower accuracy (62.1%), but the sensitivity for landslides and earthquakes and the precision for earthquakes were comparable.
## 3 System Model  
We consider a cognitive radio ad hoc network consisting of \(M\) PUs and \(N\) SU nodes as shown in Figure 2. PUs occupy the specific authorized channels completely and only hold their assigned portion of spectrum. SUs have no licenses for any spectrum bands and opportunistically transmit data in the idle time of PUs. Let \((i,j)\) denotes a unidirectional wireless link from node \(i\) to node \(j\), where \(0\leq i,j<M,i\
eq j\).  
The available spectrum can be divided into two categories. The data transmission channel (DTC) is for data communication. It consists of a set of orthogonal spectrum bands \(C=\{c_{1},c_{2},\ldots,c_{m}\}\), and the available DTC of \(SU_{i}\) is represented as \(C_{i}\). The common control channel (CCC) is used by SUs to exchange the negotiation. Each SU is equipped with a scanner, a half-duplex normal radio for negotiation transmission and a half-duplex cognitive radio which can reconfigure among \(C\) for data communication.  
The occupation duration of PUs in each spectrum bands is modeled as an independent and identically distributed alternating ON (when the channel is busy) and OFF (when the channel is idle) process [4]. If the ON and OFF periods of a PU are exponential distribution, and the arrival rate and departure rate of the PU are \(\theta_{a}\) and \(\theta_{d}\), then the probability of the PU channel being busy is denoted by:  
\[P_{on}=\theta_{a}/(\theta_{a}+\theta_{d}) \tag{7}\]  
Apparently, the probability of the PU channel being idle can be represented as:  
\[P_{off}=\theta_{d}/(\theta_{a}+\theta_{d}) \tag{8}\]  
The Probability Density Function (PDF) of the OFF periods is given by:  
\[f(\tau)=\begin{cases}\theta_{d}e^{-\theta_{d}\tau}&\tau\geq 0\\ 0&\tau<0\end{cases} \tag{9}\]  
Therefore, the probability of the PU being in ON state is given by:  
\[\begin{array}{ll}P_{collision}&=1-P(t\geq\tau)\\ &=1-\int_{\tau}^{\infty}f(t)dt\\ &=1-e^{-\theta_{d}\tau}\end{array} \tag{10}\]  
Figure 2: Cognitive radio ad hoc networking scenarios (PT: primary user transmitter, PR: primary user receiver).  
The PU mean departure rate \(\theta_{d}(\mu,\sigma)\) is computed using the transform in [22] which has an expected mean \(\mu\) and deviation \(\sigma\). SUs track the channel statistic through sensing and learning the spectrum usage pattern. Generally, the channel statistic changes slowly and the parameter evaluation can be found in [23; 24]. With some localization devices, SUs can only acquire their own location information.
# A Named Entity and Relationship Extraction Method from Trouble-Shooting Documents in Korean
## 2 Previous Research
### Dependency Parsing  
Dependency parsing is a method of analyzing grammatical composition and/or phrases by identifying dependencies between words within a sentence [14]. Dependency parsing is conducted by comparing two words within a phrase, mapping the modifier and the modified word with a dependency relationship, and specifying the dependency relationship type to clarify the syntactic relationship between two words. The word receiving the modification is referred to as the head or the governor, while the word that modifies the head is referred to as the dependent or the modifier. There are also words that do not modify any other word and are treated as modifying a virtual root node.  
After dependency parsing, every word becomes a dependent of at least one node, with no cyclic relationships. Thanks to the usage of these dependency relationships, dependency parsing demonstrates an ability to resolve ambiguities caused by various interpretations of which words are modified by another word in a sentence; therefore, it is widely used in natural language processing research.  
The two main ways of conducting recognition are transition-based dependency parsing and graph-based dependency parsing. Transition-based dependency parsing procedurally generates a dependency parsing tree by analyzing the dependency relationships in order [15]. The way of making a dependency parsing tree consists of shift calculation, followed by LEFT-ARC and RIGHT-ARC calculation. The shift calculation moves each word within a phrase from the buffer to the stack, and LEFT-ARC and RIGHT-ARC calculation determines the relationship between the words that have been moved. Classical transition-based dependency parsing is unable to take into consideration the meaning of a word or its part-of-speech (POS) tag. However, researchers have actively sought to improve these limitations, such as utilizing POS tag information and ARC-labels as features in the neural network to improve accuracy and runtime [16]. Graph-based dependency parsing considers the dependency relationships between all possible word pairs within a sentence to create all possible dependency trees of the sentence and compute the parsing result using the most probable dependency tree [17]. Since this method considers all possible cases, its runtime is slower than that of transition-based dependency parsing.  
Some previous studies have used Korean dependence syntax analysis. [PERSON] et al. [18] proposed a dependency parsing approach to infer \"subject-predicate-object\" triples from texts in Korean. [PERSON] et al. [19] also proposed a rule-based approach with a dependency parsing method to extract the necessary phrases from texts in Korean. Both studies showed that dependency syntax analysis can infer relationships among words, but they focused on a syntactic analysis based on the structural relationships in each sentence rather than extracting specific entities. This paper used the dependency parser in KLUE [8], which is a transition-based dependency parsing method.
## 2 Problem Description and Assumptions
### Assumptions  
The stochastic system analyzed in this research was based on the following assumptions:  
A1. In the beginning, a new production unit was operational and the influence of deterioration was minimal, as in [PERSON] et al. [7].  
A2. The demand was known and constant during the considered production horizon, as in [PERSON] et al. [1].  
A3. The production unit experienced increasing wear with usage and it deteriorated. The deterioration was modeled with the use of an increasing function, which varied with the production rate, as in [PERSON] [11].  
A4. The deterioration of the production system had a negative effect on the number of defects generated, as in [PERSON] et al. [5].  
A5. At failure, minimal maintenance was performed, where the condition of the machine was left in as-bad-as-old (ABAO) conditions, as in [PERSON] et al. [29].  
A6. Major maintenance was available and was a perfect repair that rejuvenated the production unit to as-good-as-new (AGAN) conditions, as in [PERSON] et al. [28].  
A7. Shortages may be observed if the time required to perform repairs and major maintenance took longer than the time to deplete the stock level used as protection, as in [PERSON] et al. [4].  
A8. Subcontracting was available to support internal production and was an alternative to satisfy product demand, as in [PERSON] et al. [14].  
A9. The subcontractor was unreliable due to several disturbances caused by random failures, periods where it was busy serving other customers, other random events, etc.; as such, the subcontractor exhibited random availability. This assumption has not been used in the context of production-quality-maintenance joint optimization.  
The notations used in the model are presented in Appendix A.
## 7 Conclusions  
The GP-DE back-analysis method was used to back-analyze the rock strata parameters of the station constructed using the arch cover method. The main conclusions are as follows:  
1. The advantages of the GP-DE (Gaussian process differential evolution co-optimization) algorithm in establishing a nonlinear mapping relationship and its strong optimization ability are used to back-analyze the surrounding rock parameters of the power station. The calculation results showed that the error between the forward calculation of inversion parameters and the measured values was only 3.62%, which can meet the needs of the project to guide the next step of construction.
2. The current content is \"based on the parameters inversed by the GP-DE algorithm\"; it was studied that the optimal length for removing the station middle partition (including Class-IV surrounding rocks) was 18m, which can not only reduce the budget cost for removing the station middle partition, but also ensure the safety and efficiency of tunnel construction.
3. With different training samples, the inversion effect of the mechanical parameters of the upper soft rocks and the lower hard rocks through the long-span arch cover method was compared with that through the GP-DE, BP, LSSVM, and GP methods. Compared with the other three algorithms, the inversion effect of the GP-DE algorithm was generally good, which performed well with a small-sample training amount.
4. For the key parameters of DE, the variation coefficient F, and the cross-coefficient CR, the control variable method was used to analyze the impact of these two parameters on the DE algorithm. Based on the iteration rate and convergence effect, it is recommended that the selection range of parameter F be 0.5\(\sim\)0.7 and the value of CR be 0.7\(\sim\)0.9.  
Conceptualization, F.Z. and A.J.; methodology, F.Z.; software, X.G.; validation, F.Z.; formal analysis, F.Z.; investigation, Q.M., Q.Y. and F.Z.; resources, A.J., Q.M. and Q.Y.; data curation, X.G., Q.M. and Q.Y.; writing--original draft preparation, F.Z. and X.G.; writing--review and editing, A.J.; visualization, A.J.; supervision, A.J.; project administration, A.J.; funding acquisition, A.J. 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, grant number 52078093; LiaoNing Revitalization Talents Program, grant number XLYC1905015; and Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University, grant number 2022 YBPY009.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.