esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fault Signal Characteristics of Test Rig
### Acquisition of Abnormal Vibration Fault Signals Based on FFT and DWT
#### 2.2.1 Fast Fourier Transform (FFT)
The vibration characteristics and failure mechanism of the key contact pair of the roller and the inner ring in the rolling bearing are mainly studied, and there is little related literature with the  
The rotational frequency of the rotating shaft in this test condition is 37.5 Hz. From the empirical formula in reference [15]:  
\[\mathrm{f_{a}=f_{s}-f_{o}}. \tag{2}\]  
\(\mathrm{f_{a}}\) is the rotational frequency of the axis of rotation, \(\mathrm{f_{s}}\) is the rotational frequency of the inner ring, and \(\mathrm{f_{o}}\) is the rotational frequency of the outer ring. In general, the outer ring is fixed, and the inner ring is rotated, so \(\mathrm{f_{o}=0}\), \(\mathrm{f_{a}=f_{s}}\). In this study, \(\mathrm{f_{a}=37.5}\) Hz. Formula of fault characteristic frequency of the inner ring in rolling bearing is:  
\[\mathrm{BPFI=f_{i}=Zf_{ic}=\frac{1}{2}Z\bigg{(}1+\frac{d}{D}\cos\alpha\bigg{)}f _{a}}. \tag{3}\]  
\(\alpha\) represents the contact angle, \(d\) represents roller diameter, \(D\) represents bearing pitch diameter, and \(Z\) represents the number of rollers. According to the empirical formula, the theoretical fault characteristic frequency of the inner ring is 21.2 Hz, which is different from the actual fault characteristic frequency 16.44 Hz in the spectrum diagram. The reason is that it exists complex time-varying rolling-sliding compound motion between the contact pair of the roller and inner ring, so the fault characteristic frequency obtained by the empirical formula is not applicable in this study. In this study, the surface morphology of the inner ring is analyzed for the skidding damage region in Figure 7, and the results are shown in Figure 8. The surface roughness of the inner ring increases from 0.378 \(\upmu\)m to 0.54 \(\upmu\)m before and after the experiment. By comparing the morphology of the inner ring, it can be inferred that the inner ring has occurred skidding damage. The region of skidding damage is shown in Figure 8b.
# Towards NGGM: Laser Tracking Instrument for the Next Generation of Gravity Missions
## 3 LTI Units
### Laser Stabilization Unit  
The laser stabilization unit was based on an optical Fabry-Perot cavity. The baseline cavity for the NGGM was the 'optical stabilizing reference cavity' (OSRC), being developed within the ESA since 2017 within a multi-national European consortium led by Airbus. The OSRC activity aims for a cavity-based laser stabilization system based on an optical cubic cavity for the NGGM mission in the first project phase (1064 nm, cavity one) and for an optical lattice clock system in space (1397 nm, cavity one) in the second project phase.  
The OSRC project aims to raise the TRL of core units to TRL 5-6, setting the basis for the advancement towards a flight-ready design in future activities. The laser frequency stability requirement for the NGGM system noise spectral density was (40 Hz)/\(\surd\)Hz \(\surd\)(\(1+(10\) mHz)/f)\({}^{2}\)) for 0.1 mHz \(<\) f \(<\) 1 Hz, which was compatible with the only flight-ready cavity system currently available by Ball Aerospace that was used by the LRI on GRACE-FO.  
The core of the OSRC LSU system was a highly stable optical cavity (designed by NPL) with its longitudinal Eigen modes serving as frequency references. The feedback signal was generated via the Pound-Drever-Hall scheme with the cavity lock electronics of the ICU.  
The LSU system could be divided into the following building blocks:  
* The optical cavity composed of ultra-low expansion glass (ULE) with low-noise crystalline-coated mirrors and a titanium mechanical mounting structure; see Figure 6.
* The vacuum chamber with a low thermal conductivity design, passive vibration-isolation, active thermal stabilization and a vacuum pumping unit (either an ion getter pump or venting-to-space-system); see Figure 7.
* The opto-electronic locking setup (\"Optics Arm\") comprising mode-matching optics, EOM and all detectors required for Pound-Drever-Hall locking.  
Figure 5: TESAT RLU (Image credit: TESAT Spacecom).  
The high stability and narrow linewidth of the LHU were derived from a cubic ultra-stable optical spacer using an ULE spacer of 5 cm in length with a coefficient of thermal expansion below \(1\times 10^{-8}/\)K near room temperature. Mirrors with fused silica substrate and crystalline coatings were optically contacted to the spacer. Mono-crystalline AlxGa1-xAs multilayer hetero-structures used as mirror coatings showed a Brownian noise contribution roughly an order of magnitude below that of typical dielectric mirrors. The substantial reduction in thermal noise from the mirrors enabled a significant improvement in the overall frequency stability of the optical reference cavities, pushing the ultimate boundaries of linewidth and noise performance that can be achieved in stabilized laser systems.  
In terms of TRL, the LSU was assessed with six others for the cavity core and four for the optical arm that performs the connection between the optical fiber coming from the LHU and the optical cavity itself.
Entropy_**2013**, _15_, 606-623; doi:10.3390/e15020606  
_Entropy_  
**ISSN 1099-4300**  
www.mdpi.com/journal/entropy  
_Article_  
**Comparing Surface and Mid-Tropospheric CO\({}_{2}\) Concentrations from Central U.S. grasslands**  
**[PERSON] *, [PERSON] and [PERSON]**  
Department of Geography, University of Kansas, 1475 Jayhawk Blvd, Lawrence, KS 66045, USA;  
E-Mails: [PERSON]DDRESS] (N.A.B.); [EMAIL_ADDRESS] (D.B.M.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-785-864-2021; Fax: +1-785-864-5378.  
_Received: 1 January 2013; in revised form: 15 January 2013 / Accepted: 29 January 2013 / Published: 6 February 2013_  
**Abstract:** Comparisons of eddy covariance (EC) tower measurements of CO\({}_{2}\) concentration with mid-tropospheric observations from the Atmospheric Infrared Sounder (AIRS) allow for evaluation of the rising global signal of this greenhouse gas in relation to surface carbon dynamics. Using an information theory approach combining relative entropy and wavelet multi-resolution analysis, this study has explored correlations and divergences between mid-tropospheric and surface CO\({}_{2}\) concentrations in grasslands of northeastern Kansas. Results show that surface CO\({}_{2}\) measurements at the Kansas Field Station (KFS) and the Konza Prairie Biological Stations 1B (KZU) and 4B (K4B) with different land-cover types correlate well with mid-tropospheric CO\({}_{2}\) in this region at the 512-day timescale between 2007 and 2010. Relative entropy further reveals that AIRS observations are indicative of surface CO\({}_{2}\) concentrations for all land-cover types on monthly (32-day) and longer timescales. AIRS observations are also similar to CO\({}_{2}\) concentrations at shorter timescales at sites KFS and K4B experiencing woody encroachment, though these results require further investigation. Differences in species composition and microclimate add to the variability of surface concentrations compared with mid-tropospheric observations.  
**Keywords:** Atmospheric Infrared Sounder; eddy covariance; information theory; relative entropy; wavelets; Konza Prairie
# Vulnerability Analysis of the China Railway Express Network Under Emergency Scenarios
## 1 Introduction
However, most of this research focuses on the impact of node failures within the network, while overlooking the effects of edge failures caused by sudden events and the actual role of cargo flow allocation. In light of this, the present study applies complex network theory to analyze the topological characteristics of the CRN, selecting the node degree, strength, betweenness, and edge betweenness and weight as the key indicators for identifying significant nodes and edges within the network. Based on the characteristics of the CRN, we propose a Coupled Map Lattice (CML) model that considers the cargo flow, distributing flow based on distance and the remaining capacity of adjacent nodes. The model simulates cascading failures within the CRN under three scenarios: isolated node failure, isolated edge disruption, and simultaneous node and edge failure due to unexpected events. This study offers several innovative contributions:  
1. It examines the cascading failure processes triggered by sudden events in three distinct scenarios--node failure, edge disruption, and simultaneous node and edge failure--within the CRN. This comprehensive assessment of network vulnerability under sudden events not only encompasses more practical scenarios but also provides a crucial basis for identifying key nodes and edges within the network. 2. By simulating the CRN's response mechanisms under different types of attacks, including deliberate and random attacks, this study explores and validates effective strategies to enhance the network's resilience. These findings not only illustrate the network's dynamic behavior when subjected to attacks but also offer new technical approaches for improving overall network stability and security. 3. It analyzes the impact of cargo flow reallocation on cascading failure propagation, revealing the critical role of dynamic cargo flow adjustments in enhancing network resilience. The study demonstrates that optimizing cargo flow allocation strategies can effectively mitigate cascading failures caused by sudden events, offering a new approach to improve the CRN's resilience against risks. The remainder of this study is organized as follows: Section 2 reviews the literature on the CR Express and network vulnerability research. Section 3 introduces the primary indicators for ranking nodes and edges, as well as the CML model. Section 4 constructs a topological model of the CRN and analyzes its characteristics. Section 5 simulates cascading failures caused by sudden events in the CRN and provides policy recommendations. Finally, Section 6 presents the conclusions and outlines future research directions.
# Pose Estimation of Primitive-Shaped Objects from a Depth Image Using Superquadric Representation
## 7 Discussion  
Currently, the proposed method cannot run at real-time speeds, but instead around 30 FPS. Although geometric segmentation and label matching can run at a reasonable frame rate (over 30 FPS), pose estimation cannot. The computational time of the proposed method is summarized in Table 3. Note that the proposed system was implemented on a Windows 10 64-bit laptop with an Intel Core i7-6950X 3.00 GHz CPU, and 16 GB memory. We did not use any GPUs in the system. Further, we were able to confirm that the pose update process is crucial. Even if there is a single object in the scene, the pose update takes 97 ms and this is not in real-time.  
To achieve the same results in a real-time system, we can employ the fast parameter estimation method proposed by [PERSON] et al. [39]. They proposed a method to estimate the superquadric parameters in near real-time. They down-sampled the input point cloud recursively until the optimization failed. Although they achieved 40 ms, this is not still suitable for augmented reality or a robot grasping system. Another solution is implementing the Levenberg-Marquardt algorithm using GPU [40]. Currently, as our system does not use any GPU acceleration, it can be expected to estimate parameters in real-time.  
In this paper, three types of primitive shapes (cuboid, sphere, and cylinder) and four objects were used to construct the dataset. Even though the types of primitive shaped objects are limited, the dataset includes the representative primitive shapes that are used in the conventional primitive shape estimation methods [16; 19]. Also, unlike existing methods, our method uses superquadrics for the primitive shape representation, which enables us to estimate the shape and the pose regardless of the type of the primitive shape (sphere, cylinder, cuboid, etc.). Although only four objects were used in the experiments, Figures 6 and 7 show that the superquadrics (shape and scale) parameters were correctly estimated. As the shape and scale parameters are estimated at the initial frame, our method can be extended to other primitive shaped objects as long as the objects can be represented by superquadrics. The purpose of this paper is not object shape classification, so we did not conduct experiments with a variety of primitive shaped objects, but this can be an important subject for future study.
# Stochastic and Higher-Order Effects on Exploding Pulses
## 4 Conclusions and Discussion  
In summary, we have studied the influence of additive noise, multiplicative noise, and higher-order effects on exploding solitons in the framework of the prototype complex cubic-quintic Ginzburg-Landau equation.  
For the stochastic CQGLE with enough large additive noise, we report a transition from explosions to filling-in, that is, a noisy spatially finite solution. Under the influence of large multiplicative noise, homogeneous in space, a collapse occurs in the zero amplitude solution.  
We show that the phase diagrams for different outcomes under the influence of additive or multiplicative noise are rather insensitive to the choice of the run time. The transitions to filling-in and collapse follow an exponential law for the transition time as a function of the noise strength. This is a typical behavior for a transition between different potential barriers triggered by noise.  
For short pulses, we deduced a dimensionless complex CQGLE including three higher-order effects: third-order dispersion, self-steepening, and intrapulse Raman scattering. Periodic exploding dissipative solitons are reported. We notice that a long time ago, but in the context of envelope equations, the effect of nonlinear gradient terms on localized solutions was studied by [PERSON] and [PERSON] [39].  
**Acknowledgments:** We wish to thank the support of FONDECYT through Project No. 1170728.  
**Author Contributions:** [PERSON] deduced the equations and has partially written the article. [PERSON] has carried out the numerical simulations, data analysis and has partially written the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
Figure 5: \(\tau-z\) plot of \(|A|\) following the dimensionless complex CQGLE (8) for \(\epsilon=1.02\), (**a**) without including higher-order effects, showing chaotic explosions, and (**b**) including higher-order effects, showing periodic explosions.
# A High-Sensitivity Cesium Atomic Magnetometer Based on A Cesium Spectral Lamp
## 4 Optimization Processes for Improving the Magnetometer's Sensitivity
### Optimization of Cesium Spectral Lamp
A commercial spectrum analyzer (HF2 LI) was used and the resolution bandwidth was set to 1 Hz, where the Larmor signal value was distorted in this mode. N \(\approx\) 90 nV/\(\sqrt{\rm Hz}\) is the system noise floor and is used to estimate the magnetometer's sensitivity in the last section. The photon shot-noise level around the Larmor frequency was estimated to be N\({}_{\rm sn}\)\(\approx\) 76 nV/\(\sqrt{\rm Hz}\) according to the DC photon current and feedback circuits. The reason that the system noise floor is slightly higher than the photon shot-noise level could be the power fluctuations of the lamp light, and the possibly associated frequency jump due to the light shift effect, which is similar to the case in atomic clocks [33]. Optimization of the sensor operating parameters was conducted in order to improve the sensitivity of the atomic magnetometer, with a focus on the impact of the input optical power, feedback magnetic field strength, and cell heating temperature. In fact, the tuning range of the output optical power from the cesium spectral lamp is relatively narrow when investigating the variations in magnetic resonance linewidth and signal amplitude. Moreover, based on a separate check, attenuating the optical power before the vapor cell deteriorates the sensitivity performance for all temperatures and feedback magnetic field strengths. Thus, we set the output power of the lamp after the improvement as the optimal optical power, a parameter that remained constant throughout the subsequent optimization process. Considering that the effect of the operating temperature on the magnetic resonance linewidth and signal amplitude is in opposite directions, let \(\beta=\Delta\
u/S\), where \(\Delta\
u\) represents the magnetic resonance linewidth and \(S\) represents the signal amplitude. The magnitude of \(\beta\) was used in this study to assess the sensitivity of the cesium atomic magnetometers. A greater \(\beta\) value implies that the sensor is less sensitive, while a smaller value suggests that the sensor is more sensitive. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**State** & **RF Frequency** & **RF Output Power** & **Optical Power** \\ \hline Before Improvement & 0.80\% & 61.97\% & 42.92\% \\ After Improvement & 0.07\% & 2.05\% & 14.68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the fractional fluctuations (standard deviation/mean value \(\times\)100%) over the temperature range [\(-\)50 \({}^{\circ}\)C, +70 \({}^{\circ}\)C] before and after the improvement of the cesium lamp. Figure 9: Comparison of optical output variation of cesium lamp.
## 4 Measurements Validation
### Measured \(30^{\circ}\) Off-Broadside Radiation Pattern  
For the \(30^{\circ}\) off-broadside radiation pattern, the four patches in Figure 13c are excited with the unit cell and cascaded unit cells P-CRLH phase shifters discussed in Section 2.2. This will ensure phase excitations of the patch elements A\({}_{1}\), A\({}_{2}\), A\({}_{3}\) and A\({}_{4}\) with the phases of \(-92.3^{\circ}\), \(-184.6^{\circ}\), \(-276.9^{\circ}\) and \(-369.2^{\circ}\), respectively. The resulting measured radiation pattern for the \(30^{\circ}\) scan angle is shown in Figure 15. The measured peak gain at the \(30^{\circ}\) scan angle is 9.9 dB, and the value of the minimum sidelobe level is 10 dB.  
The comparison data for peak gain and sidelobe level for the three prototypes discussed are given in Table 4. The differences between simulated and measured results are due to non-ideal fabrications, unavailability of exact phases with P-CRLH prototypes and a lack of perfect symmetry in the anechoic chamber.
# Visual Object Detection with DETR to Support Video-Diagnosis Using Conference Tools
## 1 Introduction
Challenging topics are solved currently with human interpreters, but this kind of setup is expensive and requires extra arrangements. To substitute the human factor with a highly reliable solution [13; 14], several research works have been conducted [15] providing various approaches for different setups. This paper proposes a ('DEtection TRansformer') DETR [16; 17; 18; 19] based solution design and procedure for Textual (Visual) Object Detection and will search the threshold where we can achieve feasible accuracy to support remote multilingual video diagnostics. For this research on the validity and feasibility checking side, deep learning based model structures has been applied, such as YOLO4 ('You Only Look Once') [20; 21; 22], the DETR ('DEtection TRansformer') [16; 17; 23] and the Detectron2 [24; 25; 26; 27; 28]. We conducted the experiments in two phases: the first round contains the technical minimum training data set for reasonable statistical learning models and analyses different model architectures' hyperparameters. Further on, we updated the data collection and annotation protocol based on these experiments. Finally, we doubled the initial training and validation data sets to improve the experiments based on the lessons learned from the first phase. We analysed other approaches on the market (such as in the case of [17]) where the experiments were they performed on COCO 2017 detections with 118 k training images and 5 k validation images. Our scope was to find a feasible solution on minimal required training data and accessible environment for healthcare sector. Our research provides solutions where the model requires a minimum training data set [29]. The research goal of both phases was to run limited validation-type experiment rounds--on each candidate group--and to summarize their results, and simultaneously to measure, compare, and analyze the outcomes, as well as to bring specific requirements about input data and preparation process efficacy. The main scope of the experiments is to prove that the detection and localization of textual and other visual element types are possible with high accuracy (even on limited training data sets), by application of screenshot images. Furthermore, using AI models already established in other sectors, we can provide outcomes with a high degree of precision to detect the textual bounding boxes for videoconference tools. These models might be part of a further cooperative communication layer between video diagnosis [5] attendees. The novelty and innovative content of this research is the adaptation of already known AI solutions and Machine Learning algorithms from other fields to healthcare to save time, save human resources, improve the efficacy of multilingual collaboration tools, break multilingual limits and save lives. In this way, the outcomes and results can quickly cover other use cases with minimal effort.
# Virtual Belt Algorithm for the Management of Isolated Autonomous Intersection
## 6 Virtual Belt Algorithm
### Offline Algorithm
#### 6.1.1 Virtual Belts Modeling  
There are several steps to construct the VBs for an intersection:  
1. Design the length of a VG. To ensure the safety of CAVs, a VG should be larger than CAVs with certain safe margin. Thus, the length of a VG should be longer than that of CAVs. The length of a VG is calculated as \[l_{g}=l_{veh}\cdot l_{s},\] (3) where \(l_{s}>1\) is a safe factor. \(l_{veh}\) is the maximum length of the considered CAVs.
2. Construct VPs based on the path of CAVs, which can be done by selecting the start points and end points of VPs. There are several aspects should be considered: * The start points of VPs should be selected before the collision zones in the considered intersection. The end points of VPs should be selected after the collision zones in the considered intersection. Thus, the VBs algorithm can be applied to avoid collision. * The lengths of these VPs should be equal. * If all CAVs have past the collision zone, there is no collision during the routes they drive away from the intersection with the same constant speed. Thus, it is unnecessary to place the end points far away from the intersection. * The VPs before the cross points should be long enough to provide sufficient VGs for the approaching CAVs. In fact this is also limited by the length of road.
3. Construct VBs based on the VPs designed previous. Each VP should be utilized to construct a VB with the same lane width. The constructed VGs in all VBs should be uniform as well.  
Consider the paths of CAVs shown in Figure 3. The VPs of the intersection are designed and plotted in Figure 17. Based on these VPs, the VBs at road S can be constructed, which are illustrated in Figure 18. The VBs at roads W, E and N can be designed with the same method.  
Figure 17: VPs at intersection.