esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Calibration of STMD Using Numerical Optimization
### Analysis of the Optimized STMD at Its Performance Limit
#### 5.4.2 Characterization of the Control Force  
It is underlined that the enhanced efficiency of the optimized STMD is not associated with extensive control force demands. In Figure 10a, the force demands for the semi-active damper in the STMD are compared with those for the active actuator in the ATMD and the viscous damper in the TMDs. The figure displays the maximum force requirements at steady-state as a function of frequency, assuming a force excitation of \(F_{exc}=8\) kN.  
Figure 10a reveals that the maximum control force of the optimized STMD is only 6% greater than that of the ATMD and 9% greater than that of the sub-optimal STMD. This occurs even though the optimized STMD at \(K=3\) is operating at its performance limit and the correction factors are significantly high. The only slight increase observed in the control force of the optimized STMD can be attributed to its dependency on relative motion, which is considerably reduced compared to both the sub-optimal STMD and ATMD, as previously explained. Note that the results in Figure 10a are not normalized. The actual force demands will depend on the assumed worst-case excitation force and the specific model parameters.  
Figure 10b provides further insights into the control force characteristics of the optimized STMD by comparing the clipping coefficients \(\alpha\) for both the optimized and sub-optimal STMDs. Throughout the entire frequency range, the degree of force clipping for  
Figure 9: Frequency responses for the optimized STMD in comparison to those for the sub-optimal STMD and ATMD, all with \(\mu=1\%\) and \(K=3\) and TMD with \(\mu=1\%\) and \(3\%\): (**a**) Dynamic amplification of primary structure displacement; (**b**) dynamic amplification of relative displacement.  
the optimized STMD is noticeably higher (\(\alpha\) is smaller) than for the sub-optimal STMD. This difference arises because the optimized stiffness \(k_{sa}\) used in the calculations of the control force (46) is significantly greater than its corresponding value \(k_{a}\) in the sub-optimal STMD. The active force component, \(k_{sa}x_{d}\), consequently intensifies, leading to a higher degree of clipping. In the following, we discuss how this increased stiffness force enhances the efficiency of the optimized STMD, despite the higher degree of force clipping.
## 3 Materials and Methods
### Geospatial and Network Research Segment--A Case Study  
For the case study in this paper, a three-segment road connected by a geodesic line, in the geo-space of RS, BiH, between the cities of BL and Db, consisting of the following road segments, was chosen:  
1. A segment of the 9 th January Motorway (M9J), 72 km long, between the Jakupovci toll station, near the city of BL, and the Kladari toll station, near the town of Db;
2. A segment of the M16 Main Road, about 6 km long, on the route Jakupovci (entrance to the city of BL);
3. A segment of the M17 trunk road, about 10 km long, located between the Kladari toll station and the town of Db.  
In the observed geo-space, the research focus is on the fourth generation (4G) telecommunications network based on LTE network technology, managed by the M:tel BL provider [1; 2]. Figure 1 shows a part of the geographical map (Google Earth) of the RS and BiH with marked areas of road segments, where the area marked in blue is covered by LTE Carrier Aggregation (CA), and the area in green is covered by LTE Frequency Division Duplexing (FDD) technology.  
LTE CA is one of the key technologies used to achieve very high data transfer speeds in 4G networks. The principle is based on combining more than one signal carrier (in the same or different bands) in order to increase the bandwidth and channel capacity. In the case study, out of the total geographical area, 14.75% is covered by LTE CA technology, and 85.25% by LTE FDD technology, which enables duplex communication between eNB and UE. It is based on paired spectrums with sufficient spacing between frequency domains to allow simultaneous sending and receiving of data.
# Quantum Coding via Quasi-Cyclic Block Matrix
## 1 Introduction
In this paper, with the advantage of the convenient implementation of the jacket transform, we applied a quasi-cyclic method via a low-density block matrix to gain long-length quantum codes. If a prime number in the proposed construction method is taken to properly choose the jacket matrix with a basic matrix and block circular matrix combined together, then a longer length of matrix to encode classical data can be obtained. This paper is arranged as follows: In Section 2, we present some preliminary information which is necessary for QECCs. Then, in Sections 3 and 4, we investigate the construction of long-length quasi-cyclic quantum codes which are generated from block jacket matrices based on a basic matrix and circulant permutation matrices. Furthermore, the construction conditions with low density are also analyzed. Finally, conclusions are drawn in Section 5.
## Materials and Methods
### Data Collection
#### 2.1.2 Measurement Setup  
Gas samples were collected with 10 Teflon sampling lines with an inner diameter of \(6\,\mathrm{mm}\) and an orifice every \(8\)-\(10\,\mathrm{m}\). One line was below the ridge, the rest at a height of around \(3.2\,\mathrm{m}\). Four lines were outside, the other lines inside the building, all with a distance of at least \(4\,\mathrm{m}\) to the walls [22]. Lines were sampled one by one for \(10\,\mathrm{min}\) each with one of two high resolution Fourier Transform Infrared (FTIR) spectrometer (Gasmet CX4000, Gasmet Technologies Inc., Karlsruhe, Germany) monitoring carbon dioxide and methane concentrations from 1 November 2016 until 30 August 2017. In consequence, per sample line 7276 hourly values (approximately 10 months) of gas concentrations were available. We obtained the values of ambient air temperature at a distance of \(5\,\mathrm{m}\) from the building and estimated average flow conditions (wind speed and direction) from the values measured with an anemometer at the roof of the building. Amount, mass and yield of the dairy cattle were obtained as daily herd averages from the farm administration.  
We estimated indoor methane (CH\({}_{4}\)) and carbon dioxide (CO\({}_{2}\)) concentrations by averaging all indoor sample lines for each hour. The outdoor sample line with the lowest CO\({}_{2}\) concentration at each hour was used to obtain CO\({}_{2}\) and CH\({}_{4}\) concentrations in the incoming air. We applied the CO\({}_{2}\) mass balancing method (Equation (1)) to calculate ventilation rates.  
\[Q=\frac{N\cdot P_{\text{CO}_{2}}}{[\text{CO}_{2}]_{\text{inside}}-[\text{CO}_ {2}]_{\text{outside}}} \tag{1}\]  
Here \(Q\) is the ventilation rate, i.e., the volume flow, in \([\text{m}^{3}\text{ h}^{-1}]\), \(N\) is the amount of cows and \([\text{CO}_{2}]_{\text{inside}}-[\text{CO}_{2}]_{\text{outside}}\) is the difference of the carbon dioxide concentration of the air indoor and outdoor in \([\text{g}\text{ m}^{-3}]\). CO\({}_{2}\) production per cow (\(P_{\text{CO}_{2}}\) in \([\text{g}\text{ h}^{-1}]\)) was derived from cow mass, milk yield, days in milk and ambient air temperature [23]. Outdoor air temperature was used as a proxy for the indoor temperature, since the offset between both is usually small and temperature sensitivity of the CO\({}_{2}\) production model is also low [24]. The indoor-outdoor methane concentration difference \([\text{CH}_{4}]_{\text{inside}}-[\text{CH}_{4}]_{\text{outside}}\) in \([\text{g}\text{ m}^{-3}]\) was multiplied with the ventilation rate \(Q\) and scaled with the cow mass to calculate normalized methane emissions \(E_{n}\)\([\text{g}\text{ h}^{-1}\text{ LU}^{-1}]\) as illustrated in Equation (2).  
\[E_{n}=\frac{Q\cdot([\text{CH}_{4}]_{\text{inside}}-[\text{CH}_{4}]_{\text{ outside}})\cdot\text{LU}}{N\cdot m} \tag{2}\]  
Here one LU is the body mass equivalent of 500 kg, \(N\) is the amount of cows in the building and \(m\) is their average mass in kg.
###### Abstract  
This article considers an approach to synthesizing a multilevel system to control movements of a multilink manipulator (MM) mounted on an unmanned underwater vehicle (UUV) for performing autonomous manipulative operations in the mode of dynamic positioning over various objects on the seafloor. The system is based on an upgraded method that, using point clouds received from the machine vision systems (MVS), provides high-accuracy determination of the shape and location of the work object relative to UUV. The preset trajectories of the MM working tool are overlaid on the identified surface of the object, with possible silting, fouling or deformation of the latter taken into account. To execute the programmed trajectories with the MM working tool, the following methods have been implemented in software: stabilization of UUV in hovering mode near the object, high-precision control of working tool's movements, and also corrections of its trajectory taking into account UUV's displacements relative to the object. The synthesized system has been developed in the C++ programming language. The operation of the system has been numerically simulated using a model of UUV with MM, as well as models of the environment and the target object, in the Matlab/Simulink and V-REP software packages. The results of the study show a high efficiency of the system both in processing sensor information and in providing the dynamic control of movements of an UUV with a MM.  
unmanned underwater vehicle; multilink manipulator; control system; machine vision system; object identification; trajectory building; undersea operations
# A Novel Adaptive Model Predictive Control Based Three-Phase Inverter Current Control Method
## 3 Adaptive Model Predictive Current Control for Inverters
### Modulation  
In this A-MPC current control method, conventional PWM modulation technique can be applied to generate the commands for the switches. The instantaneous values of three-phase inverter switching states variables \(S_{a}\), \(S_{b}\) and \(S_{c}\) are defined as 1 when the upper switch is on, and as 0 when it is off. Therefore, the equivalent sinusoidal switching state variable in three-phase coordinate system is with a DC bias of 0.5. For the modulation process, the output \(D_{a}\), \(D_{b}\) and \(D_{c}\) must meet following requirements: (i) The values in two-phase rotating coordinate system of the switching variables are equal to \(S_{d}\) and \(S_{q}\), and (ii) The DC bias of switching variables is 0.5.  
The two basic signals of the modulation process are modulation wave and carrier wave. The modulation wave can be obtained by transforming the switch state variables (\(S_{d}\) and \(S_{q}\)) from rotating coordinate system to three-phase coordinate system (\(dq\) to \(abc\)). Assuming that during a switch period the range of carrier is set as [-\(M\), \(M\)] and the value of modulation wave is \(A\), as shown in Figure 4, the duty cycle of this switch period is  
\[D=\frac{A+M}{2M}=\frac{A}{2M}+0.5 \tag{12}\]  
When \(M=0.5\), \(D=A+0.5\), the DC component of the modulation output is 0.5. Therefore, the carrier wave is ranged from \(-0.5\) to 0.5. The modulation process helps increase the numbers of switching states in one period, comparing with other FCS-MPC based methods. In FCS-MPC method, the switching state keeps constant in one control period, and only one switching state, which minimizes the cost function, is selected [23]. As a result, the error of output current is inevitable. In contrast, PWM modulation process can provide more than one switching state and their duty cycle [24]. FCS-MPC methods can only select the switching state with the least current error, resulting in variable switching  
Figure 4: Carrier and modulation waves of PWM modulation.  
frequency and complicated harmonic content [24]. In summary, the modulation method can achieve constant switching frequency and reduce harmonics.
# Finding Patterns in Signals Using Lossy Text Compression
## 3 Algorithms for Exact and Lossy RRLE Compression
### Warm Up: Exact RRLE Compression
For each such iteration, it computes the smallest period, if any, of a string of length \(O(n)\), which takes linear time using the preprocessing of the Knuth-Morris-Pratt (KMP) algorithm [27]. Then, the corresponding entry in \(D\) is computed using the \(O(n)\) precomputed values. Hence, the total time complexity of the algorithm is \(O(n^{3})\).
## 3 Results
### Robustness Test  
Adjusting for the level of consistency is a common method of determining QCA robustness ([PERSON] et al., 2018). According to [PERSON] et al. (2018), the robustness of a method refers to its capacity to remain unaffected by small variations in the methodological parameters. Hence, it can be ascribed to \"reliability\".  
In this research, the consistency level threshold was increased to 0.8 from 0.75, and group analysis was performed again using the same frequencies. The results show that there were no substantial changes in the number of condition configurations, overall consistency, and coverage. Hence, the model is robust and reliable.
###### Abstract  
Pacaya volcano is a persistently active basaltic cone complex located in the Central American Volcanic Arc in Guatem. In May of 2010, violent Volcanic Explosivity Index-3 (VEI-3) eruptions caused significant topographic changes to the edifice, including a linear collapse feature 600 m long originating from the summit, the dispersion of ~20 cm of tephra and ash on the cone, the emplacement of a 5.4 km long lava flow, and ~3 m of co-eruptive movement of the southwest flank. For this study, Interferometric Synthetic Aperture Radar (InSAR) images (interferograms) processed from both spaceborne Advanced Land Observing Satellite-1 (ALOS-1) and aerial Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired between 31 May 2010 and 10 April 2014 were used to measure post-eruptive deformation events. Interferograms suggest three distinct deformation processes after the May 2010 eruptions, including: (1) subsidence of the area involved in the co-eruptive slope movement; (2) localized deformation near the summit; and (3) emplacement and subsequent subsidence of about a 5.4 km lava flow. The detection of several different geophysical signals emphasizes the utility of measuring volcanic deformation using remote sensing techniques with broad spatial coverage. Additionally, the high spatial resolution of UAVSAR has proven to be an excellent compliment to satellite data, particularly for constraining motion components. Measuring the rapid initiation and cessation of flank instability, followed by stabilization and subsequent influence on eruptive features, provides a rare glimpse into volcanic slope stability processes. Observing these and other deformation events contributes both to hazard assessment at Pacaya and to the study of the stability of stratovolcanoes.  
volcano deformation; interferometric synthetic aperture radar; ALOS-1; UAVSAR 115 October 2015; 8 January 2016; 19 January 2016 Academic Editors: [PERSON] and [PERSON] 115 Department of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, MI 49931, USA; [EMAIL_ADDRESS]  
2
## 6 Conclusions  
The main aim of this study was to construct a new hybrid model using bagging-based kernel logistic regression (KLR), called BKLR, for spatial prediction of landslides in the Shangnan area in China. The prediction capability of 15 conditioning factors was assessed for the modeling process using the LSSVM algorithm. The performance of the new hybrid model was evaluated using several popular statistical measures, including PPR, NPR, sensitivity, specificity, the kappa index, and RMSE. A landslide susceptibility map was produced using the hybrid model and it was compared with the susceptibility maps produced by the single KLR model and SVM algorithm.  
Results indicated that the BKLR ensemble model had the best goodness-of-fit and prediction power for landslide susceptibility mapping, followed by the SVM and KLR models. The BKLR ensemble model can be used as a promising technique for spatial prediction of landslides in the current study area, and can be applied to other similar landslide prone areas for better landslide susceptibility mapping. Hybrid modeling is an efficient technique for improving the predictive capability of weak individual classifiers.  
**Author Contributions:** [PERSON], H.S., S.Z., K.K., A.S., K.C., B.T., P.T.Z., L.Z., H.C., J.M., Y.C., X.W., R.L., and B.B.A. contributed equally to the work. T.Z., L.Z., H.C., J.M., Y.C., X.W., and R.L. collected field data and conducted the landslide mapping and analysis. W.C., H.S., S.Z., K.K., A.S., and K.C. wrote the manuscript. W.C., H.S., K.K., A.S., K.C., B.T., and B.B.A. provided critical comments in planning this paper and edited the manuscript. All the authors discussed the results and edited the manuscript.  
**Acknowledgments:** The authors wish to thank [PERSON] (Northwest Nonferrous Survey and Engineering Company) for useful information provided. This study has received financial support from the National Natural Science Foundation of China (Grant Nos. 41807192, 41602359, 41602212), China Postdoctoral Science Foundation (Grant Nos. 2018T111084, 2017 Mc13168), Project funded by Shaanxi Province Postdoctoral Science Foundation (Grant No. 2017 BSHYDZZ07), the Doctoral Scientific Research Foundation of Xi'an University of Science and Technology (Grant No. 2013 QDJ038), and the Universiti Teknologi Malaysia (UTM) based on a Research University Grant (Q.J130000.2527.17H84).  
**Conflicts of Interest:** No potential conflict of interest is reported by the authors.
# 6G Mobile Networks: Key Technologies, Directions, and Advances
## 4 6G Technologies
### Spectrum-Sharing
The future should accept more system dynamics of spectrum sharing, or Dynamic Spectrum Access (DSA), in which frequency bands may be communicated across various operation contexts, including marketable/government, heritage/new, licensed/unlicensed, or numerous classes of spectrum right holders, along various specialized confines, such as frequency, time, space, and direction.
# Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
## 3 System Model
### Data Generation for Training from the IRS Network  
The data generation and model training processes are illustrated in Figure 5. In the first phase, a random binary data sequence is generated for transmission. The generated bits are then transformed into OFDM symbols, using 128 subcarriers with quadrature phase shift keying (QPSK) modulation for symbol generation. The data symbols are transmitted through a simulated transmission channel that considers IRS reflection. The channel state information (CSI) is acquired using the Rayleigh fading model. Pilot signals are transmitted through the IRS channel, and the CSI is estimated based on the received data. Once the CSI is obtained, the training signals are transmitted through the channel and used for model training. The channel simulation parameters are listed in Table 1. The direct link is also considered when the received signal is calculated. Upon reception, the signal is demodulated, and the data symbols are retrieved. Sixteen unique symbols are used, and each symbol is assigned as a label value for classification. The real and imaginary parts of the symbols are separated to generate training data, as complex numbers cannot be directly used in the machine learning training process. A total of 64,000 samples were generated for the training and testing process. Of these, 512,000 data symbols were used for training, and 128,000 symbols were used for testing. Two separate datasets were saved as training and test datasets for model training and evaluation. Each model was then trained using RNN techniques, and the training progress was monitored. Hyperparameters were tuned to optimize performance and improve model accuracy. After training, the test data were used to evaluate each model's performance on unseen data and were measured as test accuracy. Following this step, the model could be used for IRS-based channel prediction.  
Figure 5: Training data generation and training process flowchart.
# Phosphorus Mobilizing Enzymes of _Alnus_-Associated Etomycorrhizal Fungi in an Alaskan
## 1 Introduction
_Alnus_ associates with a limited number of EMF partners and is often reciprocally selective [22; 23; 24; 25], suggesting that the composition of _Alnus_-EMF communities may be tied to the ability of individual fungal species to mobilize various forms of inorganic and organic soil P.  
Whereas phosphatase activity is frequently measured on EMF root tips [19; 26; 27], there is little information as to whether these associations can mobilize P from the more complex forms of organic P in the field, or how this capacity varies among EMF species. Because different enzymes are required for the breakdown of various P forms, hydrolyzing phosphate from compounds such as orthophosphate monoesters, phosphodiesers, and phytate may represent different levels of investment. Just as certain non-mycorrhizal plant species may specialize in accessing P from one or more of these forms [28], it is likely that EMF partners have different strategies for acquiring P and may target one source over another. For example, EMF species vary in their capacity to produce phytase [27]; however, compared with the rate of P release from _para_-nitrophenylphosphate, the rate of Pi release from phytate has been shown to be notably less [29]. The objective of this study was to understand the potential for _A. tenuifolia_ and its associated EMF partners to mobilize P across spatial (mineral and organic soil horizons) and temporal (chronosequence of successional stages) scales. We hypothesized that: (1) _Alnus_-associated EMF differ in their capacities to mobilize various sources of P, and (2) the variability in EMF enzymatic potential, the community composition of EMF associated with _Alnus_, and the total P-mobilizing potential of the _Alnus_-EMF community are coordinated with spatial and temporal variation in soil resources associated with different stages of boreal forest stand development.