esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Influence Factors
### Influence of Stratum Conditions
#### 5.1.1 Influence of Elastic Modulus  
As can be seen from Figure 21, the change of the elastic modulus has a certain degree of influence on the horizontal displacement of the pile, ground settlement and axial force, but the curve forms all stay the same under each case. When the elastic modulus is 0.5 E, 0.75 E, 1.0 E, 1.5 E, and 2.0 E, maximum horizontal displacements of piles are 33.86 mm, 24.27 mm, 18.44 mm, 12.53 mm, and 9.87 mm, respectively. When the elastic modulus increases from 0.5 E to 1.0 E and then from 1.0 E to 2.0 E, the maximum displacement of the pile was reduced by 83.6% and 53.8%, respectively. The change in elastic modulus has little effect on the extent of ground settlement around the foundation pit. When the elastic modulus increases from 1.0 E to 1.5 E and then from 1.5 E to 2.0 E, the maximum settlement decreases by 46.8% and 21.2%, respectively. When the elastic modulus increases from 1.0 E to 2.0 E, the maximum axial force decreases from 142.43 kN to 120.65 kN, decreasing by 15.3%.  
Figure 20: Ground settlement.
## 4 COME-UP: Computation Offloading in Mobile Edge Computing with User Direction Prediction  
This section presents the proposed location and direction-aware task offloading framework based on LSTM. The proposed framework (COME-UP) consists of the Location and direction prediction model (LDPM) and task placement model (TPM). In order to forecast future location and direction of mobile users, the LDPM learns the mobile trajectories of mobile users from mobility traces and applies these learnings to predict their future location and direction, as explained in Section 4.1. Using features such as energy consumption, latency, and server utilization, the TPM chooses an optimum edge server residing in the expected cell locations, and then offloads the user tasks to the selected server, as described in Section 4.2. A mobile user \(M_{U1}\) positioned in \(cell_{1}\) delegates a task with higher computation requirements to the serving MEC server \(EdgS_{1}\). Nevertheless, due to frequent and quick mobility, the \(M_{U1}\) initiates a handoff to \(cell_{2}\) after submitting a job to the previously associated edge server, as shown in Figure 3.  
When the mobile user moves out of the range of \(EdgS_{1}\), the delivery of results gets delayed. Consequently, the response time of mobile applications is increased, leading to additional energy consumption and underutilization of computing resources. Further, the fitness function uses the weighted sum method to determine the priority weights of the evaluation parameters that helps to discover an optimal edge server with minimal latency and energy consumption, and higher utilization, as detailed in Section 4.3.
## 3 The Basic of GNSS
### Reliability, Availability, Maintainability, Safety (RAMS), and Evaluation Criteria in Aeronautics  
In the nomenclature and GNSS environment, system performance is defined by the criteria accuracy, availability, continuity, and reliability. These criteria, are based mainly on aeronautical applications and have been defined by the International Civil Aviation Organization (ICAO). They are not directly related to railway requirement criteria, usually qualified in terms of RAMS. A common evaluation criterion in aeronautical systems for RAMS analysis is the concept of reliability. The meaning of the above concept in relation to railway applications can be defined in terms of all possible failures, the probability of failure, and the impact of failure on the operation of the system. In both cases, reliability refers to the impact on the level of safety of the assessed application area. In addition, in comparing the ICAO evaluation criteria and the RAMS analysis, integrity is also a converging concept. For satellite navigation users, integrity is a quantitative measure characterizing confidence in the reported position. In railway transport, users speak of safety integrity to encompass a larger number of safety lifecycle requirements. The term integrity itself refers to the integrity of the train, that is, the fact that the train has not split and in the further implementation of positioning and unification systems may characterize such functions as diagnostics and reporting of rolling stock failures [23; 24; 25; 26].
## 5 Vibration Control of an Isolated Structure
### Reaching Law-Based Sliding Mode Control  
Sliding mode control is particularly suitable for semi-active control of both linear and nonlinear variable-stiffness structural systems [53]. In general, the dynamic system can be considered as:  
\[\dot{z}(t)=Az(t)+Bu(t)+d(z,t) \tag{25}\]  
where \(d(z,t)\) represents the external system disturbances regarding \(Ew(t)\). \(\delta(z,t)\) is the upper bound of the disturbance satisfying \(\|d(z,t)\|\leq\delta(z,t)\). The tracking error of the system is:  
\[e=z-z_{d} \tag{26}\]  
The goal of vibration control is to meet \(z_{d}=0\) and then \(e=z-0=z\).  
The basic design of the sliding mode control includes two stages. In the first step, a hyperplane \(s(e)\) is chosen to describe the dynamic behavior of the control system. The second step includes designing a controller such that the trajectory of the system converges to, and always remains on, the sliding surface. \(m\) switching functions are chosen as follow  
\[s(z)=rz \tag{27}\]  
\[s_{i}(z)=r_{i}z=r_{i1}z_{1}+r_{i2}z_{2}+\cdots r_{2n+2}z_{2n+2}\ \ \ \ i=1,2,\cdots m \tag{28}\]  
where \(s\) is a \(m\times 1\) vector, and \(r=[r_{1},r_{2},\cdots,r_{2n}]^{T}\) is a \(m\times(2n+2)\) vector, and \(r_{i}=[r_{i1},r_{i2},\cdots,r_{i2n+2}]\), \(r_{i}\) is a sliding vector. In the sliding mode control, the system satisfies \(s(z)=0\) and \(\dot{s}(z)=0\), and the control force is called the equivalent control force, i.e.,  
\[u_{eq}=-(rB)^{-1}(rAz+rEw) \tag{29}\]  
Thus, the control law can be chosen as:  
\[u=u_{eq}+u_{h} \tag{30}\]  
External excitation term \(d(z,t)\) can be dropped from Equation (29). Therefore, the equations governing the system dynamics may be acquired from the following design.  
\[u_{eq}=-(rB)^{-1}rAz \tag{31}\]The exponential reaching law can be expressed as:  
\[u_{h}=-(rB)^{-1}[\eta sgn(s)+gs]\ \ \ \ \ \eta>0,\ g>0 \tag{32}\]
Remote Sens._**2013**, \(5\), 3637-3661; doi:10.3390/rs5083637  
_Remote Sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
Evaluation of Land Surface Models in Reproducing Satellite Derived Leaf Area Index over the High-Latitude Northern Hemisphere. Part II: Earth System Models  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 3,4 and [PERSON] 5**  
Footnote 1: College of Engineering, Mathematics & Physical Sciences, Harrison Building, North Park Road, Exeter EX4 4 QF, UK; E-Mails: [EMAIL_ADDRESS] (G.M.-T.); [EMAIL_ADDRESS] (P.F.)  
Footnote 2: College of Life and Environmental Sciences, University of Exeter, Amory Building, Rennes Drive, Exeter EX4 4 RJ, UK; E-Mail: [EMAIL_ADDRESS]  
Footnote 3: Department of Ecology, Peking University, Beijing 100871, China; E-Mail: [EMAIL_ADDRESS]  
Footnote 4: Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100085, China  
Footnote 5: Department of Earth and Environment, Boston University, 675 Commonwealth Avenue, Boston, MA 02215, USA; E-Mail: [EMAIL_ADDRESS]  
Footnote *: Author to whom correspondence should be addressed; E-Mail: [PERSON]_ADDRESS].  
_Received: 4 June 2013; in revised form: 17 July 2013 / Accepted: 17 July 2013 / Published: 25 July 2013_  
**Abstract**: Leaf Area Index (LAI) is a key parameter in the Earth System Models (ESMs) since it strongly affects land-surface boundary conditions and the exchange of matter and energy with the atmosphere. Observations and data products derived from satellite remote sensing are important for the validation and evaluation of ESMs from regional to global scales. Several decades' worth of satellite data products are now available at global scale which represents a unique opportunity to contrast observations against model results. The objective of this study is to assess whether ESMs correctly reproduce the spatial variability of LAI when compared with satellite data and to compare the length of the growing season in the different models with the satellite data. To achieve this goal we analyse outputs from 11 coupled carbon-climate models that are based on the set of new global model simulations planned in support of the IPCC Fifth Assessment Report. We focus on the average LAI and the length of the growing season on Northern Hemisphere over the period 1986-2005. Additionally we compare the results with previous analyses (Part I) of uncoupled land surface models (LSMs) to assess the relative contribution of vegetation and climatic drivers on the correct representation of LAI. Our results show that models tend to overestimate the average values of LAI and have a longer growing season due to the later dormancy. The similarities with the uncoupled models suggest that representing the correct vegetation fraction with the associated parameterizations; is more important in controlling the distribution and value of LAI than the climatic variables.  
LAI; CMIP; Earth System Models; leaf phenology; remote sensing of vegetation
# Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Systems
## 2 System Model and Problem Formulation
### Problem Formulation  
For the downlink NOMA network, this paper considers the case that any subchannel only allocates two users. Let the multiplexing user on \(SC_{n}\) be \(U_{1,n}\) with good channel condition and \(U_{2,n}\) with poor channel condition respectively, and \(|H_{1,n}|\geq|H_{2,n}|\). In this case, the throughput of \(U_{1,n}\) and \(U_{2,n}\) can be expressed as  
\[R_{1,n}=B_{s}\text{log}_{2}(1+\alpha_{n}p_{n}H_{1,n}) \tag{5}\]  
\[R_{2,n}=B_{s}\text{log}_{2}\bigg{(}1+\frac{(1-\alpha_{n})p_{n}H_{2,n}}{1+ \alpha_{n}p_{n}H_{2,n}}\bigg{)}=B_{s}\text{log}_{2}\bigg{(}\frac{1+p_{n}H_{2,n }}{1+\alpha_{n}p_{n}H_{2,n}}\bigg{)} \tag{6}\]  
where \(\alpha_{n}\in(0,1)\) is the power distribution scaling factor of \(U_{1,n}\). Then, the throughput of subchannel \(SC_{n}\) is expressed as  
\[R_{n}=R_{1,n}+R_{2,n} \tag{7}\]  
Therefore, we formulate the energy efficiency optimization problem of the NOMA downlink system as follows:  
\[\eta:\max\limits_{p_{n}>0}\sum\limits_{n=1}^{N}\bigg{(}\frac{R_{1,n}+R_{2,n}} {p_{n}+p_{c}}\bigg{)} \tag{8}\]  
\[s.t.\quad C_{1}:R_{m,n}\geq R_{\min},\forall m\in M_{n},n\in N\]  
\[C_{2}:\sum\limits_{n=1}^{N}p_{n}\leq P_{tot},\forall n\in N \tag{9}\]  
Among them, \(p_{c}\) represents the power loss of the subchannel, and \(P_{tot}\) represents the total transmit power of the base station. The constraint \(C_{1}\) indicates the minimum data rate for each user to meet the quality of service (QoS) requirements, and \(C_{2}\) guarantees the maximum transmission power limit of the base station. It can be seen that the problem is non-convex and NP-hard, so it is difficult to find the optimal global solution directly. In order to solve this problem efficiently, we decouple it into two subproblems: subchannel allocation and power allocation. We use a stepwise approach to find the suboptimal solution of the problem.
# Sound Localization Framework for Construction Site Monitoring
## 4 Construction Sound Localization Framework (CSLF)
### Key Components of the Sound-Based Localization Approach
#### 4.2.3 Location Estimation Approach  
The location estimation approach is an iterative process that uses the coordinates and relative distances of microphones in an array to calculate the approximate locations of detected sound sources.  
The brute force method is the simplest form of the location estimation approach, with all variables entered manually to calculate the location of the sound source. Furthermore,there is a method that uses both the TDOA and brute force methods to estimate the location of sound sources. However, this method requires significant preprocessing and computing capacity, considering that information must be generated on every possible variable. Therefore, this method is only suitable for permanently installed systems at specific locations.  
[PERSON] et al. and [PERSON] consider sound-source reverberations in line-of-sight and non-line-of-sight environments [34; 35] in terms of linear and nonlinear least-square calculation methods (LSMs). In a line-of-sight environment, the sound source is within the direct view of the sound receiver, whereas in a non-line-of-sight environment, the path that the sound signal travels to reach the sound receiver is blocked by certain objects. However, because these methods must consider background noise discrimination and reverberations, high-order equations need to be solved, requiring a high computation capacity and computing power.  
This study selected a calculation method based on the linear LSM which does not consider reverberations and reflected noise, and hence, has a low computational capacity. LSM is an iterative formula that determines the approximate solution of an equation to find the squares of the error between the actual and calculated location values based on the initially expected location value. Then, it determines the location estimation value that minimizes the sum of the squares. There are three factors that must be defined beforehand: the initial estimation value, the maximum number of repetitions, and the allowable error value. LSM calculates the first estimated location \(S_{n}\) based on the predefined initial estimated coordinates \(S_{0}\). If the squared difference between \(S_{n}\) and \(S_{0}\) is greater than the predefined allowable error, the LSM will calculate the new estimated location based on Ln (previously estimated location), i.e., \(S_{n+1}\). This process is repeated until the squared difference between the current and previously estimated value is within the error limit or until the predefined maximum repetition count is achieved (Figure 3).  
Figure 3: CSLF Process.
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 3 Comparative Analysis of IEEE 802.11 ah with Previous IEEE 802.11 Amendments
### Comparison of IEEE 802.11 Amendments Based on MAC Features
#### 3.1.7 Frame Aggregation:  
Mechanism to combine multiple data frames into one larger aggregated data frame for transmission.  
* _For IEEE 802.11n:_ It employs two steps of accumulation to increase the size of the data frame to be transmitted. The first, which is at the top of the MAC, assembles MAC service data units (MSDU) and is calledA-MSDU. Another, at the bottom of the MAC, adds MAC Protocol Data Units (MPDUs) and is called A-MPDU.
* _For IEEE 802.11 ac/11 ah:_ Enhanced frame aggregation methods are used. All frames follow the A-MPDU format; the maximum size of A-MPDU is increased for IEEE 802.11 ac.
## 6 Test Results
Although the results of the proposed method show the best positioning accuracy when compared with the other two stochastic models \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**RMS Error [mm]** &
\begin{tabular}{c} **Standard Least** \\ **Square Solution** \\ \end{tabular} & **Elevation Model** & **SIGMA-\(\varepsilon\) Model** & **Proposed Method** \\ \hline Horizontal & 10.2 & 8.3 & 6.2 & 6.7 \\ Vertical & 27.0 & 28.8 & 21.5 & 18.9 \\
3D & 28.8 & 30.0 & 22.4 & 20.1 \\ \hline \multicolumn{4}{c}{\%improvement} \\ \hline Horizontal & - & 18.5 & 39.5 & 34.4 \\ Vertical & - & \(-6.8\) & 20.2 & 29.8 \\
3D & - & \(-3.9\) & 22.4 & 30.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: RMS errors in horizontal and height components and their percentage improvement (when comparing with the results of other stochastic models) of the tested stochastic models based on the proposed method in Dataset KTE1. Figure 11: The moving trajectory of antenna and positions of pegs in Dataset KTR2. in the two datasets, the proposed method does not significantly outperform the SIGMA-\(\varepsilon\) model in Dataset KTE1 and the simple elevation model in Dataset KTE3, respectively.
## 3 Results
### IR Camera  
A commonly used inspection sample in active thermography is a flat bottom-hole plate, as shown in Figure 5. The air trapped in the voids cools differently than the surrounding material. As heat is trapped, the air remains at a higher temperature and therefore shows up as a hotspot in the thermograms. Depending on the thermal properties of the sample material and the characteristics of the defects (depth, size, type of defect,...), the time needed to show a hotspot in the thermogram may vary. For this reason, the spatial image sequence, as shown in Figure 6, must be converted into a temporal image sequence, using the novel algorithm from Section 2.1. In the temporal data set, as shown in Figure 7, the sample under investigation remains at the same location and the cooling can be investigated. It is noticeable that the air pockets are smaller when the location from line source is further from the heating source. From Figure 6, it is noticeable that due to the angle of the camera, the edge of the object appears smaller than the same edge in image c. This effect only appears in the measured data. When the algorithm is applied to the measured data, a single location is used to reconstruct the object. Because the same location is used to reconstruct the image, there is no noticeable deformation of the object, as can be seen in the temporal matrix in Figure 7.  
Figure 5: RGB image of the inspected bottom wooden sample, so the defects are visible. The dimensions are the following: 160 \(\times\) 100 \(\times\) 5.8 mm.  
Figure 6: Three frames from the sequence captured by the IR camera while moving the object. (**a**) Frame 300; (**b**) frame 550; (**c**) frame 800. The frames correspond to an number of seconds after passing the heating source. (**a**) 10 s; (**b**) 18.3 s; (**c**) 26.67 s.  
Figure 7: Three frames from the temporal matrix, as created by the novel algorithm. (**a**) Frame 1; (**b**) frame 428; (**c**) frame 856. The ratio of width/height of the object matches the pixel dimensions of the reconstructed image, confirming the accuracy of the stitching algorithm. The air pockets are visible as circular hot spots. A less accurate stitching would result in elliptical hot spots.
# Research on Chinese Nested Entity Recognition Based on IDCNNLR and GlobalPointer
## 4 Experiment  
### Datasets  
This paper validates the effectiveness of the overall model and each module on two Chinese fine-grained entity recognition datasets: CMeEE dataset and CLUENER2020 dataset.  
The CMeEE dataset [36] is a nested entity recognition dataset for Chinese medical texts, which categorizes medical named entities into nine classes, including diseases (dis), symptoms (sym), drugs (dru), medical equipment (equ), medical procedures (pro), body parts (bod), medical examination items (ite), microorganisms (mic), and departments (dep). This dataset allows nested entities of different categories within an entity. For example, in the text \"pediatric pleural diseases\", there are two entities: \"pleural diseases\" and \"pediatric pleural diseases\". The entity \"pleural diseases\" is nested within the entity \"pediatric pleural diseases\". Compared to flat entity recognition datasets, the CMeEE dataset can evaluate the model's ability to recognize nested entities. The information statistics of the CMeEE dataset are listed in Table 1.  
The download link of CMeEE dataset is \"[[https://tianchi.aliyun.com/dataset/95414](https://tianchi.aliyun.com/dataset/95414)]([https://tianchi.aliyun.com/dataset/95414](https://tianchi.aliyun.com/dataset/95414))\". The date of access to the website is 30 December 2023. Under this link, the dataset is named \"CMeEE-V2\".  
CLUENER2020 [37] is a Chinese fine-grained entity recognition dataset, which is obtained by annotating part of the data of THUCTC open source text classification dataset of Tsinghua University. CLUENER2020 includes a total of 10 tag types, including positions, names, ads, companies, governments, titles, games, movies, organizations/institutions, attractions, and more. The entities of this dataset are labeled using 10 fine-grained entity categories. Each entity category is evenly distributed in the data set. The text in the data set is long text. The information statistics of the CLUENER2020 dataset are shown in Table 1. The information statistics of the CLUENER2020 dataset are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Train** & **Dev** & **Test** & **Classes** & **Total Number** \\ \hline CMeEE & 15,000 & 5000 & Null & 9 & 20,000 \\ CLUENER2020 & 10,748 & 1343 & Null & 10 & 12,091 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the datasets.  
The download link of CLUENER2020 dataset is \"[[https://tianchi.aliyun.com/dataset/144362?spm=a2c22.28136470.0.0.d3554a0a9](https://tianchi.aliyun.com/dataset/144362?spm=a2c22.28136470.0.0.d3554a0a9) cI392&from=search-list]([https://tianchi.aliyun.com/dataset/144362?spm=a2c22.28136470.0.0.d3554a0a9](https://tianchi.aliyun.com/dataset/144362?spm=a2c22.28136470.0.0.d3554a0a9) cI392&from=search-list)\". The date of access to the website is 30 December 2023. Under this link, the dataset is named \"cluener_public\".
## 5 In-Line Application of LIBS in Industrial Production
### Steel Slags
The mass fraction of the major oxides was determined by calibration curves for approx. 50 slag ladles and compared to the XRF reference mass fraction (Figure 19b). Similar results were obtained for the liquid slag (R\({}^{2}\) = 0.992, Figure 19b) and solid pressed powder samples (R\({}^{2}\) = 0.997, data not shown). Stable operation during a three-month test run has demonstrated the potential of LIBS for in-line process analysis. Figure 18: LIBS in-line measurement of solid steel slag in secondary metallurgy at voestalpine steel plant. (**a**) Photograph of the installed LIBS system. (**b**) Concentration of major oxides obtained by calibration-free (CF) analysis vs. nominal concentration (dashed line: ideal match). Adapted from [372].
## 3 Onboard Train Localization Algorithm
### Map Matching Utilizing SINS/OD Dead-Reckoning Results  
First of all, in order to control the longitudinal error of the train, as shown in Figure 5, assuming that \(P\) and \(P^{\prime}\) are the positions of the train at moments \(t\) and \(t^{\prime}\), respectively, the position at \(P\) is a known position computed from the previous moment, and using the highly accurate heading angle of the digital map of the track as well as the speed output of the OD, we are able to compute the position of the train at the next moment using Equation (6).  
\[\delta x =\begin{pmatrix}t^{\prime}\\ \frac{t}{t}\Delta v_{od}\Delta t\\ \delta y&=\begin{pmatrix}t^{\prime}\\ \frac{t}{t}\Delta v_{od}\Delta t\\ \end{pmatrix}\sin\psi^{\prime}\end{pmatrix} \tag{10}\]  
where \(\Delta v_{od}\) is the OD speed, and \(\psi^{\prime}=\pi-\psi\) is the digital track map heading angle, because we consider clockwise to be the positive direction. By using the heading of the digital track map, together with the short-term high-precision speed information from the OD, we can control the longitudinal error because the short-term OD speed error will not accumulate.  
\[W\alpha=\cos(|\psi_{track}-\psi_{SINS/DR}|) \tag{11}\]  
First, the measurement of similarity between the heading values was obtained by the SINS/DR algorithm, and that obtained by the orbital digital map was calculated. The smaller the difference of \(|\psi_{track}-\psi_{SINS/DR}|\), the greater the similarity between the two and the greater the decreasing function \(\cos\) and vice versa.  
On the other hand, in order to control the lateral error of the train, we projected the estimated train position to the electronic map of the track as shown in Figure 6.  
Figure 5: Digital track map heading and OD speed to calculate position.  
The perpendicular distance from the estimation point to the center line of the track is shown in Equation (8). The vertical distance \(d\) can be used as a basis for weighting or as a basis for outlier rejection.  
\[Wd=D/d \tag{14}\]