esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials  
The equipment used in the measurements campaign was composed of a UAV and XBee modules for wireless communications.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Work** & **Frequency** & **UAV** & **Scenario** & **Height** & **Channel Statistics** \\ \hline
[10] & 2 GHz & Airship & Urban & 100–170 m & PDF, CDF, AFD, LCR, PSD, AF \\ \hline
[11] & 2 GHz & Airship & Urban & 150–300 m & PL \\ \hline
[12] & 5.76 GHz & Hexacopter & Suburban & 0–50 m & PL, SF, K, RMS, CDF \\ \hline
[13] & 4.3 GHz & Quadcopter & Open field, Suburban & 4–16 m & PL, SF, \(\mu\), \(\epsilon\), PDF, CDF, RMS, BC \\ \hline
[14] & 2.4 GHz & Hexacopter & Laboratory, Open air & 10–40 m & PL, PAS, K, PDF \\ \hline
[15] & 802.11a & Quadcopter & Open field & 15–110 m & PL, PAS, CDF \\ \hline
[16] & 802.11a & Quadcopter & Open field, Field area & 20–100 m & PL \\ \hline
[17] & 802.11a & Fixed Wing & Aerodrome & 46 m & PL \\ \hline
[18] & 802.11a/g, 900 MHz & Fixed Wing & Aerodrome, Rural & 46 m, 107–274 m & PL \\ \hline
[19] & GSM, UMTS & Fixed Wing, Capture balloon & Urban, Rural & 0–500 m & PL \\ \hline
[20] & GSM, UMTS, LTE & Weather balloon & Urban & 11–18 m & PL \\ \hline
[21] & LTE (800 MHz) & Hexacopter & Rural & 15–100 m & PL, SF \\ \hline
[22] & LTE (850 MHz) & Quadcopter & Suburban & 15–120 m & PL, SF \\ \hline
[23] & 2 GHz & Airship & Urban, Wooded Region & 100–170 m & CDF, DG, AFD, LCR \\ \hline
[24] & 5.8 GHz & Octocopter & Residential & - & RMS, DS, CDF \\ \hline
[26] & 802.11b/g & Fixed Wing & Agricultural region & 75 m & AF, DG \\ \hline
[27] & PCS, AWS, 700 MHz & Quadcopter & Mix Suburban & 122 m & PL, CDF \\ \hline
[28] & EDGE, HSPA+, LTE & Hexacopter & - & 10–100 m & RTT, J \\ \hline
[29] & 909 MHz & Quadcopter & Open field, Simulated Village & 40–60 m & PL, PES \\ \hline
[30] & 2/3.5/5.5 GHz & HAP airship & Built-up areas & - & SF \\ \hline
[31] & 2.585 GHz & Hexacopter & Suburban & 15–300 m & PDP, RMS, DS \\ \hline
[32] & 3.4/3.8 GHz & Commercial UAV & Open area & 5–15 m & PDP, RMS \\ \hline This work & 915 MHz & Quadcopter & Lake, Caatinga & 8–80 m & PL, LCR, CDF, DS, SF, K, \(\mu\), \(\epsilon\), \(m\), \(\beta\), \(\sigma\) \\ \hline \hline \end{tabular}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline AF: Autocorrelation function, AFD: Average fade duration, BC: Coherence bandwidth; CDF: Cumulative distribution function, DG: Diversity gain, DS: Doppler spread, J: Jitter, K: Rician factor; LCR: Level crossing rate, PAS: Power azimuth spectrum, PDF: Probability density function; PDP: Power delay profile, PES: Power elevation spectrum, PL: Path loss, RMS: RMS delay spread; RTT: Round trip time, SF: Shadow fading, \(\mu\), \(\varepsilon\): Mean and standard deviation of Nakagami \(m\) factor; \(\beta\): Weibull shape parameter, \(\sigma\): Rayleigh parameter.  
\end{table}
Table 1: Characterization of channels with UAVs.
# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 4 Results
### Model Performance Metrics  
The models (GPT-3, BERT, T5, and SAAS) were assessed using conventional criteria including Accuracy, Precision, Sensitivity, Specificity, F1 Score, and Runtime Performance. The findings are succinctly presented in Table 3 below.  
The SAAS model demonstrated exceptional performance in producing relevant and accurate shopping suggestions, as evidenced by its greatest accuracy (0.96), precision (0.93), sensitivity (0.95), and specificity (0.94). Although GPT-3, BERT, and T5 had good performance, SAAS surpassed them in most parameters. However, SAAS had a slightly longer runtime of 32 milliseconds, as depicted in Figure 6.
###### Abstract  
Accurate landslide time prediction holds critical significance for ensuring safety and efficient production in open-pit mining operations. While the inverse velocity method serves as a prevalent data-driven forecasting approach, conventional single-point monitoring implementations frequently yield substantial deviations. This study proposes a multi-point collaborative inverse velocity landslide time prediction methodology using nonlinear least squares, which is based on slope radar multi-point group displacement monitoring data. Systematic stability evaluations were conducted for both single-point predictions and multi-point ensemble forecasts. Experimental results demonstrate that single-point-based predictions generally confine errors within 5 h, including the case of traditional smoothing treatments of velocity curves. The developed multi-point collaborative methodology achieves prediction errors below 1 h, with temporal forecast position variations and spatial point quantity adjustments inducing marginal error fluctuations under 2 h based on strict data exclusion. Enhanced data volume implementation significantly improves prediction accuracy and stability. These findings will provide substantive technical references and methodological guidance for advancing landslide temporal prediction research in open-pit mining engineering.  
landside prediction; synthetic aperture radar; inverse velocity method; nonlinear least squares; optimal fitting 2020 IEEEexample:BSTcontrol  
1  
[PERSON]  
1 China Academy of Safety Science and Technology, Beijing 100012, China; [EMAIL_ADDRESS] (Y.R.); [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (M.M.); [EMAIL_ADDRESS] (S.H.)  
2  
Cathay Safety Technology Co., Ltd., Beijing 102209, China  
* [2]Correspondence: [EMAIL_ADDRESS]
## 4 Analysis of the Measured Fluctuating Wind Speed Data
### Turbulence Integral Scale  
The turbulent integral length scale statistical analysis of the data of mean wind speed \(U\geq 10\) m/s was performed as shown in Figures 11 and 12. Table 3 presents the mean value of the turbulent integral scale for the two cycles. The along-wind turbulent integral scale distribution area of building A is (115 m, 636 m), that of building B is (10 m, 524 m), and that of building C is (102 m, 403 m). All of them show a large dispersion. No relevant provisions in the Chinese specification can be used to predict the turbulence integral length scales, but the values estimated by ASCE-7 and AIJ-2004 are 277.9 m (Sea, Lu = 198.12 x (150/10)\({}^{1/8}\) = 277.9 m) and 223.6 m (Lu = 100 x (150/30)\({}^{0.5}\) = 223.6 m), respectively. These values are comparable to the measured values of buildings A and C. The left side of Figure 12 shows that the mean wind direction angle of building B is \(\theta\in(39^{\circ}\), \(51^{\circ})\), and no interference in the upstream of building B exists within this wind direction angle. According to [PERSON] and [PERSON], when the plane size of the building is similar, aerodynamic interference occurs between buildings, and the plane size of the three buildings are the same [18; 19]. Building B is in the middle position, which explains why its turbulence integral scale is smaller than those of the other two buildings.  
Figure 11: Variation in turbulence integral length scales with 10 min mean wind speed. **(a)** Longitudinal turbulence intensities; **(b)** Lateral turbulence intensities.
## 3 Conventional Detection Schemes  
In OFDM, the IFO is generally detected exploiting redundant information present in the OFDM symbol [12; 13; 14; 15], which is called blind estimation. Meanwhile, the IFO estimation method can be developed by performing pilot-based correlation in the frequency domain [15; 16; 17]. The former has the problem of high computational load when compared to the latter. Although the blind estimation strategy is too computationally intensive, it can be considered as a possible candidate because the number of explicit pilot symbols is limited, and some sort of pilot configuration is available only after TMCC decoding in the ISDB-T system.
## 4 New Experimental Data Set, Benchmark Data Sets and Software Measurement Data
The goal of the Equinox project is to be a first-class open service gateway initiative (OSGi) [48]. The second dataset is from the JIRA Defect prediction dataset (Apache Active MQ) [49] which was developed in Java language for the Java Message Service client infrastructure. In Table 5, general information about each project in the datasets are presented. The last dataset's name is SFP XP-TDD. This dataset \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric Type** & **Software Metrics** & **Description** \\ \hline \multirow{6}{*}{[PERSON]} & LOC & Line count of code \\  & v (g) & Cyclomatic complexity \\  & ev (g) & Essential complexity \\  & iv (g) & Design complexity \\  & Fan-In & Number of other classes that reference the class \\ OO Metrics & Fan-Out & Number of other classes referenced by the class \\  & IOCode & Line count \\  & IOCmoment & Comment count \\ Basic \& Derived Halstead & IOCode & Number of code and comment lines \\  & And Comment & \\  & L & Program length \\ Other & Average Depth & The highest block depth value \\ Basic Metrics & Max Depth & The biggest subroutine value \\  & Error & Describing whether a software module is \\  & & defective or not \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Description of SFP XP-TDD software metrics. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Number of Modules** & **Non-Defective** & **Defective** & **\% Defect** \\ \hline Eclipse & 1321 & 986 & 335 & 25.35 \\ Apache Active MQ & 3420 & 3214 & 206 & 6.02 \\ SFP XP-TDD & 1713 & 1606 & 107 & 6.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The general short descriptions of all data sets. has been made publicly available for researchers to conduct their own studies and to have reproducible experimental results. The Eclipse data set, the minimum data set in terms of the number of modules in the study, consists of 37 measurements. This data set includes Chidamber and Kemer (CK) metrics, Object-Oriented (OO) metrics, and entropy metrics. However, the last domain module in the data set refers to the 'bug' domain, which detects whether the module is faulty [9; 49]. The Apache Active MQ refers to the most comprehensive data set in the study, consisting of 3420 modules and 66 software metrics. Chidamber and Kemer (CK), object-oriented (OO) [9; 50] entropy, and exchange metrics were all applied to this data set. The 'Heubug', which indicates whether the module is faulty, was the latest domain in the data set. The final data set, the SFP XP-TDD, is the second most ample data set within the context of the number of modules. It consisted of 1713 modules and 14 software metrics, comprising of Object-Oriented (OO) entropy and exchange metrics. Similarly, 'Error' is the last domain in this data set, detecting whether or not the module is faulty. The three data sets were all created for software fault predictions, and each data set retained a variety of metrics that are unique to that data set. The general metrics type information of all datasets are presented in Table 6.
# Frequency Response of Optically Pumped Magnetometer with Nonlinear Zeeman Effect
## 1 Introduction  
Highly resolving measurements of weak signals in the Earth's magnetic field are very important for applications that involve measurement of faint magnetic-field signals, such as geophysical exploration [1; 2; 3; 4], bio-magnetic field detection in an unshielded geomagnetic environment [5; 6], and ultralow-field nuclear magnetic resonance [7; 8]. One way to measure the small amplitude signals within the Earth's magnetic field is using optically pumped magnetometers (OPMs) [9], which are based on detecting the Zeeman shifts of the atomic energy levels, such as those of the alkali ground states [10; 11; 12; 13; 14; 15] or the He\({}^{4}\) metastable state [16; 17; 18; 19]. An alkali atom-based OPM is attractive for its low noise which can reach around \(\mathrm{f}\Gamma/\sqrt{\mathrm{Hz}}\) (1 \(\mathrm{f}\Gamma=10^{-15}\) T) [20; 21; 22], and the cryogen-free operation. However, in the Earth's field, alkali atoms based OPMs suffer from the nonlinear Zeeman (NLZ) effect [23; 24; 25]. The NLZ effect is caused by the nonzero nuclear spin of the alkali atoms and its coupling to the electron spin and as a consequence makes the Zeeman shifts of the atomic energy levels of the ground states become nonlinear dependent of the strength of the magnetic field. The NLZeffect becomes non-negligible in the Earth's field range of about 20 \(\upmu\)T to 70 \(\upmu\)T (1 \(\upmu\)T = 10\({}^{-6}\) T). It broadens and distorts the magnetic resonance lineshape which degrades the magnetometer's noise specification [26] and generates a heading error [24; 27].  
We find that the frequency response of alkali atoms based OPM is also influenced by the NLZ effect. Full and precise knowledge about the OPM's frequency response is important for parameter adjusting in control systems that involve the OPM, such as the closed-loop operation of the OPM [28], or the magnetic-field stabilization with OPM [29]. In the case of a small amplitude of the magnetic field, the NLZ effect is much smaller than the magnetic resonance linewidth and the frequency response of the OPM is typically a first-order Butterworth low-pass filter [28; 30]. However, when the magnetic field amplitude increases and the NLZ effect is comparable with the magnetic resonance linewidth, the OPM's frequency response deviates from that of the first-order low-pass filter.  
In this work, we theoretically analyze the frequency response of the alkali atomic magnetometer for a non-negligible NLZ effect and experimentally verify the theoretical predictions with an amplitude-modulated Bell-Bloom cesium OPM. The frequency response of the Bell-Bloom cesium OPM is measured when the biasing magnetic field is 50 \(\upmu\)T or 70 \(\upmu\)T and the results are coincident with the theoretical predictions.
# Prediction of the Load-Bearing Behavior of SPSW with Rectangular Opening by RBF Network
## 2 Finite Element Modeling (FEM)
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Element** & **E (MPa)** & **F\({}_{y}\) (MPa)** & **F\({}_{u}\) (MPa)** & **\(\mathbf{\varepsilon_{y}}\) (\%)** & **\(\mathbf{\varepsilon_{u}}\) (\%)** \\ \hline HEB 160 (SPSW s4) & 2.06 \(\times\) 10\({}^{5}\) & 340 & 450 & 0.17 & 14.4 \\ HEB 160 (SPSW2) & 2.07 \(\times\) 10\({}^{5}\) & 400 & 450 & 0.19 & 13.2 \\ The stiffener plate (t = 5 mm) & 2.05 \(\times\) 10\({}^{5}\) & 340 & 470 & 0.17 & 20.5 \\ The infill plate (t = 1 mm) & 2.04 \(\times\) 10\({}^{5}\) & 280 & 500 & 0.14 & 21.6 \\ \hline \end{tabular}
\end{table} 
Table 2: Mechanical properties of steel material used in modeling [26]. Figure 1: Experimental specimens which are using to verify Abaqus software [26] (**a**) SPSW2, and (**b**) SPSW s4 models, (**c**) dimension details of the SPSW s4 model. to some assumptions such as material modeling, merging the boundary elements with the infill plate, and ideal boundary conditions. Figure 3 shows the comparison of experimental SPSW deformations with numerical models. During the experimental testing, the buckling of the plate occurred between the diagonal stiffeners at 0.56% drift and 353 kN base shear which lead to a reduction of the stiffness of the wall [26]. This can be seen in Figure 2. The residual deformation and local buckling of the experimental and numerical specimens are the same. Mesh sensitivity analysis revealed that the proper mesh size is about 50 mm where the model has 4824 elements (see Figure 4). The loading is static and pushover applied to the top beam of the finite element frames, and the load-displacement diagram for each model is derived. The maximum load, stiffness and absorbed energy were determined. These values are considered as neural network inputs in the next section. Figure 3: Comparison of finite element (FE) simulated models and tested specimens at ultimate displacement: (**a**) deformation in SPSW s4 specimen, (**b**) von Mises stress distribution and deformation in FE model of SPSW s4, (**c**) deformation in SPSW2 specimen, (**d**) von Mises stress distribution and deformation in FE model of SPSW2. Figure 2: The load–displacement curves of experimental and numerical models, (**a**) with opening, and (**b**) without opening.
## 3 Results
### Skeletal, Dental and Soft Tissue Changes after Treatment with ASSD Appliance  
Table 6 shows the difference in pretreatment (T1) and post-treatment (T2) of cephalometric measurements. The results showed that there was an improvement in skeletal, dental, and soft tissue measurements after treatment with the ASSD appliance. The skeletal measurements showed statistically significant changes in the maxilla and the mandible that favor correction of Class III malocclusion. The maxillary position became more forward as the SNA angle increased by 2.73\({}^{\circ}\) (_p_ < 0.001), maxillary length (Co-A) increased by 4.62 mm (_p_ < 0.001), and forward movement of A point (A-NB) by 3.69 mm (_p_ < 0.001). Statistically significant changes were also seen in the mandible. Backward rotation of the mandible manifested by a significant increase in mandibular plane angle (MP-SN) by 2.35\({}^{\circ}\) (_p_ < 0.001). In addition to that, the mandible showed a more backward position, as indicated by a significant decrease in SNB angle by 0.71\({}^{\circ}\) (_p_ = 0.029). The inter-arch relationship was also improved shown in Wits appraisal value (AO-BO) by 6.46 mm (_p_ < 0.001), ANB angle by 3.29\({}^{\circ}\) (_p_ < 0.001). Besides that, the lower anterior facial height (ANS-Gn) showed an increase of 1.16 mm (_p_ = 0.007). The dental relation was also improved where clinically the overjet was increased. However, the inclination of the upper incisors reduced by 0.19\({}^{\circ}\), and the lower incisors inclination increased by 2.67\({}^{\circ}\). The soft tissue profile was also showed an improvement where the upper lip thickness (Ls-NB) increased significantly by 2.08 mm (_p_ < 0.001), as well as increased protrusion of the upper lip (UL-EL) by 1.87 mm (_p_ < 0.001). On the contrary, a few unfavorable changes were also noticed, such as increase in mandibular length (Co-Gn) by 1.92 mm (_p_ = 0.004),
# Influence of Drift on Robot Repeatability and Its Compensation
###### Abstract  
This paper presents an approach to compensate for the effect of thermal expansion on the structure of an industrial robot and thus to reduce the repeatability difference of the robot in cold and warm conditions. In contrast to previous research in this area that deals with absolute accuracy, this article is focused on determining achievable repeatability. To unify and to increase the robot repeatability, the measurements with highly accurate sensors were performed under different conditions on an industrial robot ABB IRB1200, which was equipped with thermal sensors, mounted on a pre-defined position around joints. The performed measurements allowed to implement a temperature-based prediction model of the end effector positioning error. Subsequent tests have shown that the implemented model used for the error compensation proved to be highly effective. Using the methodology presented in this article, the impact of drift can be reduced by up to 89.9%. A robot upgraded with a compensation principle described in this article does not have to be warmed up as it works with the same low repeatability error in the entire range of the achievable temperatures.  
robot repeatability; robot precision; robot drift; robot warm-up +
Footnote †: journal: Applied Sciences
## 1 Introduction
In addition, in terms of the time delay, the scheduler-MES communication is in millisecond while scheduler-AIV communication takes seconds (to reach the destination and in terms of auto-docking). Timing details of each step of scheduler execution is given in Section 4 in Table 1.  
A lot of work has been published on robotic automation [16; 17; 18; 19; 20] and MES [21; 22; 23; 24; 25]. Relevant discussion to this work is published in Reference [26; 27; 28; 29], but, to the best of the authors' knowledge, the detailed practical solution for the integration of an MES and an AIVs is not previously published. Some manufacturers of commercially available AIVs [30; 31] offer high-end developed solutions, including MES integration, but with license restrictions. The integration solution provided here can help the manufacturing industry to develop its own high-end system using low-cost AIVs. An overview \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Step-1** & **Step-2** & **Step-3** & **Step-4** & **Step-5** & **Step-6** & **Step-7** & **Step-8** \\ \hline Time (ms) & 1570 & 29,222 & 69,407 & 1693 & 1332 & 30,308 & 83,271 & 1888 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Scheduler steps execution time. Figure 3: System architecture. of the previously published relevant work in Reference [26; 27; 28; 29] is included here. In Reference [26] authors have focused on the challenges associated with AIV integration with MES in a manufacturing environment. This work mentioned the need for an interface tool, which can communicate with AIV and MES webservices; however, there is no discussion on how to develop the interface tool. A review of low-cost AIVs for advanced manufacturing systems is provided in Reference [27]. This work recommends the off the shelf low-cost AIV to integrate it into intralogistics. The use of ROS is also recommended here to build a low-cost solution. This work highlights that low-cost AIVs like Robotnik has no communication support with an MES. In Reference [28] the authors focused on data mining methods to integrate them into an MES. The data analysis was made with a focus on the Baxter cobot. In Reference [29] the authors presented a specific EU project STAMINA, which involved the integration of a cognitive robot into the manufacturing process. Here, a logistic planner was used to link an Enterprise Information System (EIS) with a robot. The authors defined the logistic planner as a mechanism responsible for integrating the STAMINA system with the IES. The technical details of the logistic planner was not provided. The LabVIEW-based scheduler proposed in this work is unique. This work focuses on the scheduler development details, while other parts of the project like MES webservices development and lift system (mechanical part) to pick and drop the product were completed by other project partners and are out of scope for discussion here. The paper is organized as follows. Section 2 briefly described MES and AIVs, Section 3 presents the proposed scheduler, Section 4 provides testing details, the discussion is covered in Sections 5 and 6 concludes.