esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimentations
### Clustering Performance Evaluation
#### 4.3.1 Rand Index  
Therefore, we utilize the Rand Index [55], which calculates a similarity measure between two clusters by considering all pairs of samples. It counts the pairs that are assigned to the same or different clusters in both the predicted and true clusters. The Rand Index is a function that quantifies the similarity between two assignments while disregarding permutations.  
Consider \(C\) as the ground truth class assignment and \(K\) as the clustering.  
* \(a\) represents the count of pairs of elements that belong to the same set in both \(C\) and \(K\).
* \(b\) denotes the number of pairs of elements that are in different sets within both \(C\) and \(K\).
* \(c_{2}^{n_{samples}}\) represents the total number of potential pairs within the dataset, where \(n_{samples}\) denotes the number of samples.  
\[\text{Rand Index }=\frac{a+b}{c_{2}^{n_{samples}}} \tag{8}\]
# Optimized DM-RS Configuration for Improved 5G New Radio Network Capacity and Performance.
## 5 DM-RS Optimization, Achieved Network Capacity and Performance Improvements
### DM-RS Optimization for MBB Use Case and Improved Peak User Experienced Downlink Throughput
The impact of DM-RS optimization on spectral efficiency performance in different radio conditions was analyzed through correlation with the used modulation coding scheme (MCS), which can take a value between 0 and 28, where a higher value is used for better radio conditions. The results presented in Figure 11 show that spectral efficiency  
Figure 9: DM-RS optimization for MBB use case: (**a**) user-experienced downlink throughput measured by drive test; (**b**) spectral efficiency improvements measured by counters from 5G NR _performance management_ (PM) system. improvement may be observed regardless of radio conditions, with comparable gains on both carriers. A deep-dive analysis of the impact of DM-RS reduction on link adaptation performance was conducted to assess potential degradation due to channel estimation inaccuracy. The analysis of modulation distribution in two drives presented in Figure 12a did not show major differences. A slight redistribution may be noticed, where usage of best-performing modulation from a spectral efficiency perspective, 256 QAM, decreased from 47% to 44% with the implementation of new settings. On the other hand, a decrease in usage of worst performing QPSK modulation is observed in parallel from 8.7% to 8%. The analysis of _block error rate_ (BLER) performance presented in Figure 12b shows only a minor degradation of 2% that may be neglected. The improvement of achieved spectral efficiency brings lower PRB utilization in the system and a positive impact on user experience through the allocation of additional RBs and peak throughput increase. The comparative analysis of PRB allocation during two drives presented in Figure 13 shows higher PRB allocation on both primary and secondary cells in the second drive after DM-RS optimization. Higher gains are seen in situations when the load is higher and fewer PRBs are allocated (11% gain on 20 th percentile vs. 2% gain on median). Figure 11: Correlation of achieved spectral efficiency and used MCS: (**a**) primary cell—Fc = 3.5 GHz; (**b**) secondary cell—Fc = 3.4 GHz. Figure 10: Spectral efficiency improvements measured from drive tests: (**a**) primary cell—Fc = 3.5 GHz; (**b**) secondary cell—Fc = 3.4 GHz.
## 2 Materials and Methods
### Media Preparation and pH Determination  
The organics, acetic acid (AA-CH\({}_{3}\)COOH) and formic acid (FA-HCOOH), and the inorganics, hydrochloric acid (HA-HCl) and sulfuric acid (SA-H\({}_{2}\)SO\({}_{4}\)), were purchased from Sigma-Aldrich (St. Louis, MO, USA) and used to obtain a set of Mueller Hinton Broth (MHB, Fluka, Merck KGaA, Darmstadt, Germany) mediums with pH ranging from 3.0 to 7.0 (36 different conditions). pH was measured using a pH electrode (Mettler Toledo, Schwerzenback, Switzerland) and adjusted dropwise. The final broth media was then sterilized by filtration using 0.22 um PTFE syringe filters (BGB, Rheinfelden, Germany). pH adjustment of the MHB mediums was made just before the incubation with the bacterial suspension in the 96-well plates (Greiner, Merck KGaA, Darmstadt, Germany, see below). The stability of the pH all over the experiments was observed by measuring, at specific time points over a 24 h period, the pH of an aliquot of each condition, kept at 36 \({}^{\circ}\)C. No significant variations were observed during the 24 h period.
## 4 Design and Stability Analysis of Surrounding Rock-Supporting Structure of Shaft  
### Engineering Example  
The Niaoshan coal mine is located in Heilongjiang Province, China. The geology comprises, from the bottom to the top of the mining area, the Upper Chengzie Formation  
Figure 8: Model curves and test curves ofuffaceous mudstone under different confining pressures: (**a**) \(\sigma_{3}=0\) MPa; (**b**) \(\sigma_{3}=1\) MPa; (**c**) \(\sigma_{3}=3\) MPa; (**d**) \(\sigma_{3}=7\) MPa.  
(K1C), Muling Formation (K1m), Dongshan Formation (K1 ds), Houshigou Formation (K1-2h), and tertiary and quaternary strata. The ground elevation of the mining area is +350 m, the design and production final level of the auxiliary shaft is \(-\)700 m, the cross-section is circular, and the excavation radius is 4.5 m. When an auxiliary shaft is constructed in the \(-\)600-700 m horizontal section, it passes through the fault fracture zone. According to the geological survey data, there are 23 faults with a drop of more than 30 m and 13 faults with a drop of more than 100 m in the middle and deep parts of the area. The rock mass in this section is mainly tufaceous mudstone and thin-bedded argillaceous sandstone. The rock mass is low in strength, easily weathers and peels off, and swells and softens in contact with water, showing the mechanical properties of soft rock with well-developed rock mass joints and fissures. It is prone to significant deformation. The geological survey data show that the average cohesion of the rock mass \(c\) = 2.2 MPa and the average internal friction angle \(\varphi\) = 26\({}^{\circ}\). Groundwater is not developed in this section and the rock mass is dry. In situ stress testing was carried out on the horizontal section ranging from \(-\)400 m to \(-\)700 m in the mining area. The test results showed that the distribution of in situ stress was mainly horizontal principal stress. The average maximum and minimum horizontal principal stresses were 23 MPa and 22 MPa, respectively. The results from the horizontal ground stress test at \(-\)600 m are shown in Table 4, where \(\alpha_{\rm H}\) is the maximum horizontal principal stress azimuth angle.
## 3 Simulation  
The simulation environment is MATLAB/Simulink 2018b, and (12) is solved by using the fmincon function in MATLAB. In order to simulate real-time performance, the desktop real-time module is also enabled. The processor used in the simulation is Intel(R) Core(TM) i5-8500. The simulation system includes a computer model of mining vehicles and the path tracking controller proposed in this paper.  
The parameters of the model are taken from a real mining vehicle, as shown in Table 2. Since the computer model outputs states directly, we omitted the positioning and sensing systems.  
In the simulation system, the position error is decomposed into \(X\)-direction error and \(Y\)-direction error. In order to facilitate reading, \(e_{d}\) is defined as displacement error, as shown in Figure 4. \(e_{h}\) is heading error.  
Like the switching-MPC controller proposed by [PERSON] et al., the NMPC controller is a variable longitudinal velocity controller. However, in order to verify the hypothesis shown in Figure 2, simulations were divided into three groups according to three fixed longitudinal velocities. In each group of simulations, the performance of the NMPC path tracking controller was compared to that of the switching-MPC controller. In all simulations, the parameters of these two controllers were fixed, as shown in Table 3. Each group simulation had the same computer model of mining vehicles and the same given path. The given path consists of the straight line and arc, where the radius of the arc is 15 m.
# Seismic Velocity Anomalies Detection Based on a Modified U-Net Framework
## 4 Discussions and Conclusions  
We generated simulated seismic data based on the finite difference method and used the modified U-Net to successfully predict the underground velocity field and the location of anomalies from seismic waveforms, and then used CNN to post-process the generated images. Experimental results show that the effect of multi-shot-point prediction is not necessarily better than that of single shot point. After numerical verification, the predicted velocity field and abnormal point position have very little error with the ground truth. It should be noted that traditional VMB normally needs weeks or months to reconstruct the velocity model; however, our algorithm only needs days, which is shown as Table 1. Next, we will use real seismic data to verify and refine our model.  
There are still some problems with our model. First of all, as shown in Figures 10, 12 and 14, as the depth increases, the error of the velocity prediction will gradually increase. In addition, the data used in each of our experiments correspond to 1000 sets of artificially generated information. It is necessary to increase the amount of information and use real seismic data to improve the applicability of the model to the actual situation. Finally, although U-Net can extract image features well and achieve the required training effects, this processing is based on images and not directly obtained from seismic data training. The image resolution will seriously affect the training effect. We will continue to find ways to overcome these problems.  
Methodology, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); Resources, P.Y.; Software, [PERSON] ([PERSON]); Validation, [PERSON] ([PERSON]); Writing--Original Draft, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The authors are supported by the Natural Science Foundation of Jilin Province (Grant No. 20210101481 JC), the Shanghai Municipal Science and Technology Major Project (Grant No. 2021 SHZDZX0103), and the Fundamental Research Funds for the Central Universities (Grant No. 93K172020K27).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 2 Modeling
### The Diode Equivalent Model of a Solar Cell  
The modeling of solar cells plays a crucial role in the evaluation of the reconfiguration technique's efficiency. Multiple modeling techniques were employed in the literature, including the one-diode, two-diode, and three-diode models [39; 40]. Considering simplicity and practicality, the single-diode model was adopted for modeling solar cells, as shown in Figure 3. According to Kirchhoff's Current Law (KCL), the equations for the output current and output voltage under the irradiance intensity G can be expressed as [41]  
\[I_{m,ij}=I_{ph,ij}-I_{0}\left[\exp\left(\frac{q(V_{PV,ij}+R_{s}I_{s})}{\sigma AT _{ij}}\right)-1\right]-\left[\frac{V_{PV,ij}+R_{s}I_{m,ij}}{R_{sh}}\right] \tag{30}\]  
where \(I_{m,ij}\), \(I_{ph,ij}\), and \(I_{0}\) are the output current, photoelectric current and saturation current, respectively. \(V_{PV,ij}\) is the operating voltage. \(R_{s}\), \(R_{sh}\), and \(C_{p}\) are the series resistance, parallel resistance, and parallel capacitor, respectively. \(q\) is the electric charge (\(1.6\times 10^{-23}\)C). \(A\) is the quality factor of the diode. The photoelectric current \(I_{ph,ij}\) is calculated as  
\[I_{ph,ij}=I_{ref}\left(\frac{G}{G_{ref}}\right)\left(1+\alpha_{1}\left(T_{ij} -T_{ref}\right)\right)\frac{\left(R_{s}+R_{sh}\right)}{R_{sh}} \tag{31}\]  
where \(I_{ref}\) is the short-circuit current under standard irradiance \(G_{ref}\) (1000 W/m\({}^{2}\)) and standard temperature \(T_{ref}\) (25 \({}^{\circ}\)C). \(\alpha_{1}\) is the module's temperature coefficient for current.
# Hub Airport End-Around Taxiway Construction Planning Development: A Review
## 4 The Construction Principles and Key Points of End-Around Taxiway Layout
### Limiting Requirements in End-Around Taxivany Design
#### 4.3.2 REAT, Runway End-Around Taxiway Mode
For KWE, the displaced threshold enables the unrestricted SAT to avoid impacting the approach lighting system. Thresholds at airports like SHA and DFW have more considerations, such as visual obstructions, which are detailed in Section 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CODE** & **Aircraft Type** & **Vertical Tail Height** & **Approach Surface** & **VSS** \\ \hline \multirow{2}{*}{C} & A320 & 12.08 & 664 & 428 \\  & B737 & 12.7 & 695 & 446.9 \\ \hline \multirow{2}{*}{D} & B757 & 13.74 & 747 & 478.6 \\  & B767 & 16.13 & 866.5 & 551.4 \\ \hline \multirow{2}{*}{E} & B747 & 19.6 & 1040 & 657.1 \\  & A340 & 17.93 & 956.5 & 606.2 \\ \hline \multirow{2}{*}{F} & A380 & 24.27 & 1273.5 & 799.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Distance from the runway threshold to the SAT (meters). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CODE** & **Aircraft Type** & **Vertical Tail Height** & **Approach Surface** & **VSS** \\ \hline \multirow{2}{*}{C} & A320 & 12.08 & 664 & 230.6 & 240.6 \\  & B737 & 12.7 & 695 & 235.3 & 245.3 \\ \hline \multirow{2}{*}{D} & B757 & 13.74 & 747 & 243.1 & 253.1 \\  & B767 & 16.13 & 866.5 & 261.0 & 271.0 \\ \hline \multirow{2}{*}{E} & B747 & 19.6 & 1040 & 287.0 & 297.0 \\  & A340 & 17.93 & 956.5 & 274.5 & 284.5 \\ \hline \multirow{2}{*}{F} & A330 & 18.3 & 975 & 277.3 & 287.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Half-widths of the SAT in the vertical direction of the runway (meters). SAT is termed an unrestricted EAT because it fulfills the REAT, BAT, and SAT modes, operating as a truly unconstrained bypass. Air traffic controllers can select the most proximate, optimal, and appropriate route per real-time runway flow to swiftly guide aircraft to the apron, potentially with no instructions required. The unrestricted EAT has limited international implementations. Following project completion and commissioning in 2022, KWE's application has received strong support from air traffic control. More airports are starting to evaluate the unrestricted end-around taxiway model.
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 4 Application to Fingerprint-Based Indoor Localization System
### Implemented System  
Figure 3 illustrates the overview of _FILS15.4_. During location detections, the user needs to always wear the transmitter device. The transmitter will send data with the 500 ms interval. The receivers allocated in the field will receive the data with \(LQI\), and send them to the server through the USB-connected _Raspberry Pi_ with the 30 s interval, utilizing the _MQTT_ protocol. The server detects the user's current room by comparing the received \(LQI\) with every stored fingerprint.  
_FILS15.4_ adopts _Twelite 2525_ in _Mono Wireless_[2] as the transmitter conforming the _IEEE 802.15.4_ standard. The wireless signal is at the 2.4 GHz band, which can be interfered with _IEEE 802.11 Wi-Fi_. During detections, the user may wear it at the wrist.  
Figure 1: Experiment field layout.  
Figure 2: Measured LQI data for _D307_.  
Furthermore, _FILS15.4_ adopts _Mono Stick_ in the same company as the receiver. It is connected with _Raspberry Pi_ through the USB interface. When a packet from a transmitter is received, the _link quality indication (LQI)_ is also monitored. _Raspberry Pi_ sends the received and LQI data to the server through the _MQTT protocol_[17].  
In the _calibration phase_, the server stores the received data in the _SQLite_ database, calculates the average LQI during every 30 s, and combines the values from all the receivers into one vector to generate the fingerprint for the room. It is stored with the relevant location label. In the _detection phase_, the server calculates the Euclidean distance between the average measured LQI and every fingerprint to detect the current room at every 30 s.
# An Overview of Indoor Positioning and Mapping Technology Standards
## 2 Related Work
### Standards for Indoor Positioning, Mapping and Navigation
#### 2.3.4 IEEE MDR  
The requirements raised by robot navigation tasks in 2D indoor and outdoor environments may include: map quality in terms of metric accuracy, probabilistic behavior of robot navigation, and capability of handling metric and topological maps [91]. In order to satisfy robot navigation requirements, the IEEE Robotics & Automation Society (RAS) developed a standard for Robot Map Data Representation (MDR) for navigation, which specified data models and data formats for two-dimensional (2D) metric and topological maps.  
The IEEE 1873-2015 standard defines a global map concept, considered as a tree consisting of a collection of 2D local maps. The local map, which is either a metric or a topological map, is represented by nodes and arcs, where nodes represent local maps and arcs represent the relations (for example, transformation of coordinate system) between two local maps. A metric map may contain grid maps and geometric maps, where a grid map decomposes the representation of an environment into square cells that constitute atomic pieces of information, and a geometric map comprises a list of continuous geometric features. A topological map represents an environment in the form of a graph consisting of a set of nodes and edges connecting them [92]. This standard also specifies the XML implementation according to this data model. More information about the XML data format in practice can be found in [91, 92].
# Optimization of the Position and Stiffness of Passive Walking Assistance Devices
## 2 Methods
### Computational Model of Human Walking with Passive Assistive System  
In our previous study [21], a two-dimensional (2D) multibody dynamic model of human walking was created by using nine rigid bodies (2 feet, 2 shanks, 2 thighs, pelvis, trunk, and head) and eight revolute joints (2 ankles, 2 knees, 2 hips, pelvis-trunk, and neck joints). Because most of the power during walking is consumed in the sagittal plane, a 2D modeling approach was adopted. The multibody system had 8 degrees of freedom to define the intersegmental position by using joints and 3 degrees of freedom to determine the spatial coordination with respect to the global coordinate system. Ground reaction forces were predicted with a foot-ground contact model. The normal ground reaction force, \(F_{N}\), was obtained using the relationship given in Equation (1), where \(k\) is the contact stiffness, \(x\) is the amount of penetration, \(e\) is the stiffness exponent, and \(c_{max}\) is the contact damping at a specified penetration level. The tangential behavior of contact was modeled with Coulomb friction. The height and weight of the human model were determined as 170 cm and 70 kg, respectively. The masses and inertial properties of each rigid limb were estimated by using anthropometric distributions.  
\[F_{N}=k\ x^{\alpha}+c_{max}\ \dot{x} \tag{1}\]  
Figure 1: The workflow of the method.  
Inverse dynamic analyses were conducted to find the joint moments. The input of the inverse dynamic analyses were joint motions, which were assumed to be periodic and perfectly symmetric between the legs. In this study, joint motions were generated by minimizing the total positive joint power on the basis of our previous study [21], as shown in Figure 2. The conditions of periodicity and perfect symmetry between legs were satisfied by applying joint angles to one leg with a phase shift by 50% of gait cycle with respect to the other leg.  
The two ends of the spring, which behaved as a walking assistance system by storing and releasing energy under tension forces, were attached to points on rigid bodies. The connection between the spring model and the corresponding rigid body was assumed to be rigid connection, and the effect of soft tissue on the position of the springs was neglected.
## 5 Numerical Experiments and Conclusions
The conclusion is that, generally, the parallel Broyden's method has similar characteristics with the considered parallel Newton method, convergence properties, attraction basin, _etc._ This similarity can  
Figure 3: The graphs of \(ln(\varepsilon_{n})\) generated by Algorithm 5 and parallel Newton method; (**a**) the graph corresponding to systems (1); (**b**) the graph corresponding to systems (2) given by Algorithm 5 modified in accordance with Sherman-Morrison lemma; (**c**) example of a system verifying the condition \(\|D(x_{n})^{-1}\|\|E_{n}\|<1\). be seen in the case of the first system (Figure 3a). The third system is an example for which \(F\) is bounded on \(\mathcal{R}^{3}\), as it is required in Section 3. Note that the condition \(\|D(x_{n})^{-1}\|\|E_{n}\|<1\) is also satisfied. **Experiment 3. **  
This experiment is devoted to compare Algorithm 3 with the parallel variants proposed in [1] and [2,3]. Three linear systems of low dimensions (m = 5) with condition numbers equal to \(8.598,\ 4.624,\ 3.701\) were considered as test systems. Every system was set into two partitions of dimensions 3 and 2 respectively. The results are presented in Figure 4. We can conclude that the behavior of Algorithm 3 is satisfactory, as in all cases the generated sequence converges to the solution of the system and the convergence is linear. It is worthwhile to note the very good behavior of Algorithm 3 in comparison with the other two variants for cases (b) and (c): in case (b) the Algorithms 1 and 2 appear to have a very slow convergence, and in case (c) these algorithms fail to converge.
# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
## 3 Methods
### Three Methods for Obtaining Annual Night-Light Images  
To synthesise a suitable annual night-light image for the Cambodian region, which will be heavily influenced by cloud cover, three methods were used: annual average, annual cloud-free average, and month-specific substitution. In a night sky without clouds, light emanating from the surface can be captured by satellites; if there are clouds in the atmosphere, satellites cannot capture nighttime lights beneath them. Daily data products based on VIIRS/DNB night-light imagery cannot be used to estimate electricity consumption, as they are heavily influenced by cloud cover. Monthly data products from VIIRS/DNB night-light imagery are synthesised from the daily data products and, when an area is obscured by clouds, that day's data are replaced by the non-obscured days for that month. With this process, unobstructed monthly night-light images are available for most of the world; however, in low latitudes like the Cambodian region, there are still many months of the year when unobstructed monthly images are not available. In this study, months that were unaffected by cloud cover after daily data synthesis are called _cloud-free months_.  
Annual average method  
Annual night-light images were obtained by averaging the night-light values of the twelve monthly images of the year, regardless of whether they were affected by cloud.  
Annual cloud-free average method  
Cambodia experiences distinct dry and rainy seasons, such that night-light images are often heavily influenced by cloud cover in the rainy season. Using the annual cloud-free average method, the cloud-influenced months in Table 2 were excluded and the night-light images from non-cloud-influenced months were averaged to obtain annual night-light images.  
Figure 2: Examples of changes in night-light image raster data for different years before and after denoising (years \(n-1\), \(n\) and \(n+1\)).  
(3) Month-specific substitution method  
December is part of the dry season in Cambodia and is the month least affected by cloud. As a result, the December night-light images for each year from 2012 to 2019 are not affected by cloud cover and are the most stable. Thus, this method used December night-light images as annual night-light images.