esa-sceva/satcom-chunk-collection huggingface dataset:
# Method for Theoretical Assessment of Safety against Derailment of New Freight Wagons
## 3 Results from the Theoretical Derailment Safety Assessment  
This study was conducted only for the first bogie axle of a Sggmrss series wagon (90 feet) in an unloaded condition equipped with a standard Y25 bogie. The reason for this is that theoretical analyses categorically state that the first wheel axle of an unloaded wagon is most at risk of derailment. This conclusion is also confirmed in the test results of all wagons from the reference list given in UIC Leaflet 530-2 [29]. This was also found during field tests of the same wagon [11,30]. The initial data used in the theoretical study are as follows:  
* Tare weight of the wagon, 27.5 t;
* Curve radius, \(R\) = 150 m;
* Clearance between flanges and rail threads in a straight section of the track, equal to \(\delta\) = 0.01 m;  
Figure 9: Forces acting on the standard Y25 bogie with distances used for calculations.  
* Additional tracks widening in a curved section, \(\delta=0.002\) m (in accordance with the test data of the wagon [30]);
* Coefficient of friction between the rail and the wheel, \(\mu=0.4\);
* Wheel axle distance, a\({}^{+}=1.8\) m;
* Pivot distance (for one wagon section only), a\({}^{*}=11.995\) m;
* Speed of passing through the curve, \(v=7\) km/h (in accordance with the test data of the wagon [30]);
* Wind pressure, \(W=0\) N/m\({}^{2}\) (in accordance with the test data of the wagon [30]);
* Distance between the rolling circles of the two wheels of the same axle, \(2b_{0}=1.5\) m;
* Transverse distance between the axle journals, \(2b_{jF}=2.0\) m;
* Distance between the side supports on the bogie, \(2b_{s}=1.7\) m;
* Overhang of the outer rail, \(h=0.15\) m;
* Earth acceleration, \(g=9.81\) m/s\({}^{2}\).  
For the theoretical determination of the safety criterion against derailment of a Sggmrss wagon (90 feet), the methods described in detail in Section 2 of this paper were applied. The results from the calculations conducted with the mentioned methodology are given in Table 2.  
With the data from Table 2, the final assessment of safety against derailment can be conducted. This is performed using Equation (1), and the calculated value is equal to 1.017. According to [9], when using the theoretical assessment methods, the limit value of 1.2 is reduced by 10%, which means that the limit value of [PERSON]'s criterion should be set to 1.08 and compared with the calculated value, as shown in Equation (29).  
\[\left(\frac{Y}{Q}\right)_{ju}=1.017;\qquad\qquad 1.017\leq\ 1.08 \tag{29}\]  
The obtained value of the safety criterion, 1.017, is lower than the limit value, 1.08, meaning that, for wagon series Sggmrss, the requirement for safety against derailment is fulfilled.
## 1 Introduction
### Active Power Ramp Rate Control/Power Smoothing  
#### 3.1.1 Proposed Definition and Its Justification  
During the last few years, the increased penetration of variable-output converter-interfaced DRES has led to significant net load variations and has modified the existing load pattern at system level. These variations, mostly appearing in minute-to-hour timeframes [29; 30], can no longer be neglected by the TSOs and need to be properly compensated in order to maintain the power balance in the transmission system by increasing the required FCR and FRR. To mitigate this requirement, a growing number of grid codes, over the last years, are posing specific limits to RES generation so as to avoid extreme active power ramp rates, especially in weak grids, e.g., the TSOs in Ireland, [31; 32] and Puerto Rico [13], pose specific ramp-rate limitations for PV systems and wind farms. For example, the Puerto Rico Electric Power Authority (PREPA) [13] requires the RES PP fluctuations to be less than 10% (of nominal power)/min. In other countries, e.g., Mexico, the regulations target greater restrictions, of around 1-5%/min [33]. It is noted that these specifications concern variable-output DRES connected in the transmission system. To address the high ramp rates originating from DRES connected in the distribution system, PREPA suggests the use of utility scale ESS at the POI of the distribution grids  
Figure 3: DRES and FSS based on supercapacitor bank for inertial response, FRT and ramp rate limitation in PV plants.  
Figure 2: Proposed AS and their categorization [18; 27; 28].  
with the transmission system [34]. It is noted that the aforementioned requirements have the form of system-support functions, therefore they are not remunerated [6]. On the contrary, their absence is penalized.  
Variable-output DRES can limit the ramp rate of their electrical power by using suitable control algorithms. Some wind generators equipped with advanced controllers, such as the rotor inertia control [35] and pitch angle control [36] can control the ramp rate (also referred to as power smoothing) without involving any complementary ESS. However, this is associated with a higher stress of their mechanical system. On the contrary, using a complementary FSS, both wind generators and PV systems can provide power smoothing without curtailing power or increasing the mechanical stress [37; 38]. The use of FSS enables the smoothing of the power in timeframes from milliseconds up to few seconds. The use of slower ESS, such as BESS, is more suitable for power smoothing in time frames from some seconds up to several minutes. The degree of power smoothing depends on the algorithm implemented and the size of the ESS. The most prevalent power smoothing algorithms are based on moving average [39] or ramp-rate control [40].
# Carrier Phase Dual One-Way Ranging Method Based on a Frequency Hopping Signal
## 3 The FH Dual One-Way Ranging System
### The Tracking Algorithm of the FH DOWR  
The tracking loop adopts a frequency locked loop (FLL) and a phase locked loop (PLL) into track the carrier phase. In addition, a delay locked loop (DLL) is adopted for PN code tracking [28]. In carrier phase measurements, it is necessary to output the carrier phase from the carrier tracking loop [29; 30]. However, for the TDD mode in the ISL, the DOWR needs to convert two time measurements into equivalent instantaneous observations; that is, the influence of propagation delay on the measurement must be corrected [31]. Because each satellite needs to transmit signals from multiple frequency points over time, the tracking loop of the satellite segment for multiple frequency points will frequently lose lock and reconnect, resulting in unstable carrier phase tracking and ultimately leading to an incorrect calculation of the integer ambiguity. Therefore, extrapolation methods are adopted for PLL and DLL to ensure the stability of carrier phase tracking. The structure is shown in Figure 6.  
The extrapolation yields two values: the radial velocity \(v(t)\) and the distance \(\rho(t)\). The calculation methods are shown in (13) and (14):  
\[v(t_{2})=v(t_{1})+\int_{t_{1}}^{t_{2}}a(t)dt, \tag{13}\]  
\[\rho(t_{2})=\rho(t_{1})+\int_{t_{1}}^{t_{2}}\biggl{[}v(t_{1})+\int_{t_{1}}^{t_ {2}}a(t)dt\biggr{]}dt \tag{14}\]  
where \(v(t_{1})\) and \(\rho(t_{1})\) are the velocity and distance of the measurement time, respectively. \(v(t_{2})\) and \(\rho(t_{1})\) are the velocity and distance of the extrapolation time, respectively. \(a(t)\) is the radial acceleration, which is calculated based on orbital elements.  
Considering that the switching time of the ISLs is \(T_{\text{SW}}\), the radial acceleration error obtained through orbital elements is \(\Delta a\), and the radial velocity error obtained through orbital elements is \(\Delta v\). The radial velocity correction error obtained by integrating (11) is \(\Delta a\times T_{\text{SW}}\), and the radial distance measurement correction error obtained via (12) is \((\Delta v+\Delta a\times T_{\text{SW}}/2)\times T_{\text{SW}}\). Assuming \(T_{\text{SW}}=2\) s, \(\Delta v\leq 0.1\) m/s, and \(\Delta a\leq 1\times 10^{-5}\) g, the velocity correction error is less than \(1.96\times 10^{-4}\) m/s, and the Doppler correction error in the Ka frequency band is less than \(0.02\) Hz. The distance correction error is less than \(0.1\) m. Therefore, the distance correct error, CNR, and systematic error determine the ranging error totally.
## 2 Materials and Methods
### Fitting and Evaluation  
After the hearing devices were created, they were fitted to the ears and their audio-logical benefits were examined. The adjustment of the device was first performed using the impression-based transducer. The benefits were evaluated via subjective impression, aided thresholds, functional gains, and speech audiometry. The audiometric results were judged according to the Japanese guidelines for the evaluation of hearing aid fitting [23]. If the benefits were insufficient, the device was adjusted again. After the completion of the adjustment, the CT-based transducer was connected to the body of the hearing aid instead of the impression-based transducer. The hearing aid gains were maintained without adjustments to evaluate the difference in the benefits between the transducers. During the fitting and trial, the patients were not informed which transducer was designed based on the ear impression or CT images.  
After the evaluation, the hearing device was tried in daily life. Patients 1 and 2 tried both transducers for 4 weeks. During the trial, the patients could interchange between the two transducers to compare their benefits. For Patient 3, the completion of the CT-based transducer was delayed. Therefore, the impression-based transducer was first tried for 4 weeks, and after the CT-based transducer was created, both transducers were alternately tried for 2 weeks.  
Figure 2: Scheme of process of designing transducer based on ear impression and CT images. CT, computed tomography.  
After the trial, the subjective benefits of each CC hearing device were measured using questionnaires, which is consistent with the aforementioned Japanese guidelines for the evaluation of hearing aid fitting, and the evaluation was performed according to these guidelines [23]. In comparing the subjective benefits between both transducers, the subjective patient preferences in terms of hearing, wearing comfort, and appearance were recorded, and the patients determined the devices they wanted to purchase and use continuously.
# Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD
## 2 Feature Extraction Method Based on ICEEMD-Time-Frequency Information Entropy
### A Description of Time-Frequency Information Entropy  
Time-frequency distribution of the signal reflects its energy variation at each frequency. It can quantitatively describe the different degrees of planetary gearboxes under different operation conditions. Time-frequency information entropy can describe the time-frequency distribution complexity of signals, so it is especially suitable for fault feature extraction. In this paper, time-frequency information entropy is the feature vector for fault diagnosis. It can represent the fault information hidden in the signal, with high diagnostic accuracy and good robustness.  
The steps of time-frequency information entropy can be described as follows [24; 25]:  
(1) Short-time Fourier transform for each IMF component.  
\[F(t,\omega)=\int IMF(i)w(i-t)e^{-j\omega t}dt \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MSE (Mean Squared Error)** & **IO (Index of Orthogonality)** & **R (Coefficient of Determination)** \\ \hline
**ICEEMD** & 4.0286 & 0.9073 & 0.9925 \\
**CEEMD** & 6.2469 & 1.3127 & 0.9489 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison of ICEEMD and CEEMD.  
Figure 4: Seven sub-signals decomposed by complementary ensemble empirical mode decomposition (CEEMD).  
where \(w(\tau)\) is the window function.
2. Calculate the time-frequency energy spectrum of each IMF.  
The time-frequency energy spectrum is used to describe the time-frequency distribution of the signal. The expression is as follows [26]:  
\[S(\text{t},\omega)=\left|F(\text{t},\omega)\right|^{2} \tag{8}\]
3. Energy normalization.  
The difference of energy distribution of time-frequency block on time-frequency plane reflects the difference in signal time-frequency distribution. The time-frequency energy \(W_{i}\) of \(IMF_{i}\) is obtained by the Equations (6) and (7). The time-frequency energy of all IMFs is A.  
Energy normalization of \(IMF_{i}\):  
\[q_{i}=W_{i}/A(i=1,\ldots,M) \tag{9}\]
4. Calculate the entropy value of each time-frequency block:  
\[s(q)=-\sum_{i=1}^{M}q_{i}\ln q_{i} \tag{10}\]
# Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?
## 3 Robust Statistics for GNSS Positioning
```
Input :\(\mathbf{y}\in\mathbb{R}^{n},\mathbf{x}^{0}\in\mathbb{R}^{p}\) Output :\(\hat{\mathbf{x}}\in\mathbb{R}^{p}\) for\(k\gets 1\)to\(N\)do
1 Update residuals: \(\mathbf{r}^{k}\leftarrow\mathbf{y}-h\left(\mathbf{x}^{k-1}\right)\) Update scale: \(\delta^{k}\gets MAD(\mathbf{r}^{k})\) Update weights: \(\mathbf{W}\leftarrow\text{diag}\left(\psi(\mathbf{r}^{k}/\hat{\sigma}^{k})/ \left(\mathbf{r}^{k}/\hat{\sigma}^{k}\right)\right)\) WLS: \(\mathbf{x}^{k}\leftarrow\arg\min_{\mathbf{x}}\|\mathbf{y}-h\left(\mathbf{x}^{ k-1}\right)\|_{\mathbf{W}^{-1}}^{2}\)if\(\|\mathbf{x}^{k}-\mathbf{x}^{k-1}\|<\delta\)then
2 return\(\hat{\mathbf{x}}\leftarrow\mathbf{x}^{k}\)
```  
**Algorithm 1**IRLS procedure for robust GNSS SPP. Remarkably, there are certain specific challenges associated to the GNSS-based positioning problem that we point out in this paper. First, the observation model \(h\left(\cdot\right)\) is nonlinear. Thus, the IRLS procedure for finding the observations weights based on the M-estimator concatenates with the iterative LS used for dealing with the model nonlinearity. Secondly, the GNSS problem is characterized by presenting _fat_ data samples, namely, there is a low redundancy of observations. Since generally only around a dozen satellites are tracked and at least four parameters are to be estimated, GNSS SPP constitutes a severe case of low redundancy regression problem [56]. Lastly, the general assumption on robust statistics of independent and identically distributed noise is not met for the GNSS case. Not only are GNSS observations noise uniquely described using stochastic models, but the assumption of independent noise can be violated for satellites of similar direction-of-arrival (e.g., for multipathand none line of sight effects), or for all satellites (e.g., under the influence of a jamming attack or an ionospheric storm).
# Cold Atmospheric Pressure Plasma in Wound Healing and Cancer Treatment
## 5 Conclusions  
Cold atmospheric pressure plasma (CAP) is a promising innovation for a variety of different applications in medicine. The application of CAP for disinfection, wound healing, and cancer treatment are probably the most investigated areas of application. While clinical evidence is available for wound healing, preclinical studies dominate the novel field of plasma oncology. Efforts to elucidate the mode of action of CAP suggest that different components, such as UV radiation, electromagnetic fields, and reactive species may act synergistically, with the reactive species being regarded as the main effectors acting by targeting redox signaling pathways. While CAP induced oxidative eustress triggers pathways that promote proliferation, angiogenesis, migration, and re-epithelialization and hence, improves wound healing, oxidative distress triggers pathways inducing senescence, apoptosis, or necrosis, as well as an immune response and therefore may be beneficial for cancer treatment. Although much progress has been made in identifying the underlying molecular mechanisms, more research is necessary to fully understand the complex signaling processes involved in the biological responses to CAP treatment.  
Conceptualization, L.B.; methodology, L.B.; resources, S.E.; writing--original draft preparation, L.B.; writing--review and editing, S.B., T.B., S.E., T.F., B.M.-H., O.J., C.S., B.N., G.O., K.P., M.S., M.L.S.; visualization, L.B.; funding acquisition, S.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The joint research project \"ONKOTHER-H\" is supported by the European Social Fund (ESF), reference: ESF/14-BM-A55-0001/18, 02/18, 05/18 & 06/18 and the Ministry of Education, Science and Culture of Mecklenburg-West Pomerania, Germany. S.E. is supported by the Damp Stiftung. S.B. is supported by the German Federal Ministry of Education and Research (BMBF), grant numbers 03Z22 DN11, 03Z22 Di1, and 03Z22D511. Furthermore this work was supported by DFG EM 63/13-1 (S.E.), the TBI project \"AmbuPlas\" (S.E.) and the FORUN program to promote young scientists at the University Medical Center Rostock (L.B.).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Parameter Modeling and Error Analysis of the INS and GNSS Systems
### Inertial Navigation Error Analysis and Modeling
#### 3.1.2 Velocity Error Equation  
Since the acceleration and the specific force of the carrier correspond one-to-one, the velocity error equation can be obtained from the specific force error. The specific force equation in the navigation coordinate system is as follows:  
\[\dot{v}^{n}=f^{n}-(2\omega_{ie}^{n}+\delta\omega_{cn}^{n})\times V^{n}+g^{n}. \tag{19}\]  
By calculating the total differential on both sides of Equation (19), the velocity error propagation equation can be obtained as  
\[\delta\dot{v}^{n}=f^{n}\times\phi^{n}+\
abla^{p}-(2\delta\omega_{ie}^{n}+ \delta\omega_{cn}^{n})\times V^{n}-(2\omega_{ie}^{n}+\omega_{cn}^{n})\times \delta V^{n}+\delta g^{n}, \tag{20}\]  
where \(f^{n}\) represents the specific force of each axis in the local navigation coordinate system, \(g\) is the gravitational acceleration, and \(\
abla^{p}=[\
abla^{N},\
abla^{E},\
abla^{D}]\) is the equivalent bias error of the accelerometer in the NED direction. The other parameters are the same as in Equation (17). Expanding Equation (20), and ignoring the second-order infinitesimal expression, we can obtain \[\left\{\begin{array}{l}\delta\dot{V}_{N}=f_{D}\phi_{E}-f_{E}\phi_{D}-2(\omega_{ ie}\sin L+\frac{V_{E}}{R_{n}+h}\tan L)\delta V_{E}-\frac{V_{V_{D}}}{R_{n}+h}\delta V_{N}- \frac{V_{V_{D}}}{R_{n}+h}\delta V_{D}\\ \quad-(2\omega_{ie}\cos L+\frac{V_{E}}{R_{n}+h}\sec^{2}L)V_{E}\delta L+\
abla_{ N}\\ \delta V_{E}=f_{N}\phi_{D}-f_{D}\phi_{N}+(\frac{V_{E}}{R_{n}+h}\tan L-\frac{V_{D}}{R_{n}+h} )\delta V_{E}+(2\omega_{ie}\sin L+\frac{V_{E}}{R_{n}+h}\tan L)\delta V_{N}\\ \quad-(2\omega_{ie}\cos L+\frac{V_{E}}{R_{n}+h})\delta V_{D}+(2\omega_{ie}V_{N }\cos L+\frac{V_{E}V_{E}}{R_{n}+h}\sec^{2}L+2\omega_{ie}V_{D}\sin L)\delta L+ \
abla_{E}\\ \delta V_{D}=f_{E}\phi_{N}-f_{N}\phi_{E}+2(\omega_{ie}\cos L+\frac{V_{E}}{R_{n }+h})\delta V_{E}+2\frac{V_{E}}{R_{n}+h}\delta V_{N}-2\omega_{ie}V_{E}\delta L \sin L+\
abla_{D}\end{array}\right., \tag{21}\]  
where \(f_{N}\), \(f_{E}\), and \(f_{D}\) represent north, east, and down specific force components in the navigation coordinate system, respectively, and \(\
abla_{N}\), \(\
abla_{E}\), and \(\
abla_{D}\) represent the first-order Markov offsets of the accelerometer in the NED direction.
# Extending the Quality of Secure Service Model to Multi-Hop Networks
## 3 QoSS Applied to a Multi-Hop Network
### Calculation of Multi-Hop Network QoSSS
#### 3.4.2 Step 2  
For the next step, if the link between two nodes contains multiple channels, for example, between nodes **X** and **Y** in Figure 2, then the respective QoSS metrics for those two links are averaged. To be precise, a weighted average of the links is applied based on \(AL\) and \(DF\) as described in Section 3.3. If, on the other hand, the link between two nodes contains multiple hops, for example, link \(\langle\)**A2-B**, **B-C**, **C-D2**\(\rangle\) in Figure 2, then the respective QoSS metrics for those three links are multiplied together, as detailed by Equation (5). It is not possible to apply Equation (5) if there are multiple channels between the end-points, thus collapsing multi-channel connections into an equivalent single channel takes president, after which Equation (5) may be applied. This is the process for finding the QoSS equivalent for nodes \(\left[\textbf{A>X>Y>D}\right]\) in Figure 2.
## 3 Building the Virtual Astronaut
### Data Pre-Processing and Challenges  
Data pre-processing to put the multiple data sets into a common base is necessary for building a VA data model. The VA data model includes a texture model, animation paths, and a rover model. The elements of the data model are tied together by a set of WDFs stored as comma-separated value (CSV) files.  
The texture model includes a 3D terrain model with several image mosaics mapped onto the terrain to provide surface texture. The large amount of rover-based image data due to its high-resolution necessitated texture mapping of the images during the pre-processing step because projection on the fly required a large amount of computation power, which affected the overall system performance. Rover-based images are mapped to the terrain by a ray tracing technique. Ray tracing first determined the viewing ray direction of each pixel in the 2D images using camera model parameters, and then calculated the intersection of the viewing ray with the terrain. Mosaicking the textural information from multiple sources and overlaying the texture mosaic on a 3D terrain model were done using ESRI(r) ArcGIS. The texture model was then exported to a VRML (Virtual Reality Modeling Language [28]) file using ArcScene and was further transformed to a FBX (Autodesk FilmBox format, [29]) file by PolyTrans [30]. The FBX file was brought into the VA environment with customized code interfaced to Unity.  
The challenge for generating the texture model is integrating data from multiple sources taken at different resolutions and under different lighting and viewing conditions, and registered to different reference frames. Scale, shape, and location differences between the same features are caused by working with multiple sensors acquiring data under different conditions. Another challenge is registering rover position to features in the texture model. Rover positions are derived from an onboard inertial measurement unit (IMU) and from tracking the number of wheel turns. Rover position and orientation have uncertainties accumulated over time due to the wheel slippage and IMU drift. The rover attitude and position are periodically refined by the measurement of the Sun position [31] or by triangulation with surface features [32; 33; 34]. Various coordinate systems are associated with data from orbit and the surface. This requires a clear understanding of the sensor models and manual registration of multiple data sets into the same reference frame. In addition, Unity uses a left-handed Y up coordinate system. This is different from the GIS world where X/Y is the surface plane and Z is usually up. Finally, combining images from various cameras with different lighting conditions often involves manual operations of color adjustment as discussed in Section 4.3.