esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Time-Resolved Serial Femtosecond Crystallography  
The advancement of the hard X-ray free electron lasers technology with its unique ultrashort and highly brilliant pulses at various XFEL sources has revolutionized the field of structural biology by bringing about a significant development in time-resolved crystallography (TRX), thereby leading to the emergence of the TR-SFX [4; 5; 9; 14; 15; 20]. The TR-SFX made possible the direct visualization of biomacromolecules, e.g., proteins and nucleic acids, in action inside the crystals and under their near-physiological conditions with high spatial and temporal resolutions [9; 14; 15]. Almost all biochemical reactions are controlled by the actions of series of proteins or protein-nucleic acids that undergo essential conformational changes as a function of time following the triggering of the reaction by a specific factor that enables a specific set of reactions to subsequently occur [25; 26; 51; 52; 53]. The trigger of a biochemical reaction can be any chemical or physical factor that elicits the biomacromolecule to react and realize the specific biological function [14; 15; 51]. The TR-SFX enables the probing of these conformational changes at different delay time points and thus, significantly aids the understanding of the mechanisms of these processes at near-atomic levels [15]. Based on the triggering system, the TR-SFX can be divided into two major categories: (i) the pump-probe TR-SFX, which uses light excitation (physical factor) to trigger the biochemical reaction of the biomacromolecule [23], and (ii) mix-and-inject TR-SFX, which uses a chemical trigger or substrate (chemical factor) to trigger the reaction [26]. Using the SPB/SFX instrument and high-repetition rate X-rays, the standard pulse duration available for users is nominally 25 fs (potentially down to less than 10 fs upon request) with a jitter of \(\sim\)12 fs [54], corresponding to the shortest time delay available for time-resolved studies with laser excitation. There is no limitation on the longest time delay, as the trigger for a pump-probe or the \"start\" of a mixing experiment can be an almost arbitrary amount of time prior to the X-ray's interaction with the sample. In the following subsections, we highlight some of the recent advancements in the pump-probe TR-SFX approach with particular a focus on experiments that took place using the SPB/SFX instrument of the European XFEL facility, as well as on mix-and-inject TR-SFX and the development and use of cage compounds.
# Influence of Open Differential Design on the Mass Reduction Function
## 3 Design of the ODT
### Sun and Planetary Bevel Gears Calculation  
The differential mechanism, which enables a different number of revolutions of the output shafts (axle shafts), is made using two sun and four planetary bevel gears (Figure 4). These six gears are made with straight teeth from steel 15 CrNi6.  
The calculation of the bevel gears is similar to the calculation of the spur gears. With this calculation, the intermediate auxiliary gear has the same width as the bevel gear [41]. For this reason, the load on the bevel gears is realized according to the mean values of the auxiliary gears. The auxiliary gears are the virtual spur gears.  
Module calculations were carried out with regard to the loadability of the tooth root and with regard to the loadability of the tooth flank.  
Module considering load of tooth root is  
\[m_{\mathrm{r}}\geq 2\cdot\sqrt[3]{\frac{T_{\mathrm{max}}\cdot\cos\delta_{3}}{z_ {3}\cdot\lambda\cdot\sigma_{\mathrm{FP3}}}}.Y_{\mathrm{F}}. \tag{33}\]  
Module considering load of tooth face flank is  
\[m_{\mathrm{f}}\geq 2\cdot\sqrt[3]{\frac{u_{\mathrm{V}}+1}{u_{\mathrm{V}}}\cdot \frac{T_{\mathrm{max}}\cdot\cos^{2}\delta_{3}}{z_{3}^{2}\cdot\lambda\cdot \sigma_{\mathrm{FP3}}^{2}}.Z_{\mathrm{M}}^{2}\cdot Z_{\mathrm{HV}}^{2}\cdot Z_{ \mathrm{\epsilon V}}^{2}}. \tag{34}\]  
According to expressions (33) and (34), the following modulus values were obtained: \(m_{\mathrm{r}}=6.637\) mm and \(m_{\mathrm{f}}=8.49\) mm. Between these two modules, for the further calculation of the bevel gears, was adopted a module \(m=m_{\mathrm{f}}=8.5\) mm.
## 2 Materials and Methods  
### Instrumentation  
A MP-AES instrument (Model: 4100, Agilent Inc., Santa Clara, CA, USA) was used in all trace element determinations. The sample introduction system consisted of solvent-resistant tubing, a double-pass cyclonic chamber, and an inert flow blurring nebulizer (OneNeb). The operating parameters are presented in Table 1. Details of the instrumentation have been described in previous publications [19; 20]. Nitrogen gas was obtained from a generator connected to a simple air compressor. Instrumental parameters such as the nebulizer gas pressure, the viewing position (i.e., the analytical observation zone in the plasma), and the background correction were optimized automatically and individually for each element using the instrument software (MP Expert, Agilent Technologies, Santa Clara, CA, USA). The microwave-assisted acid digestion technique was utilized for the preparation of samples using the microwave assisted acid digestion system, CEM MARS 5. No further sample preparation was required and no modifiers or ionization buffers were added. For the purpose of comparison, Inductively Coupled Plasma-Optical Emission Spectrometer (Model: 720 ES, ICP-OES, Agilent Inc., Santa Clara, CA, USA) with an ICP Expert II software (Agilent Technologies, Mulgrave, Victoria, Australia) and Fast Sequential-Atomic Absorption Spectrometer (Model: 280 FS, FS-AAS, Agilent Inc., Santa Clara, CA, USA) with SpectrAA Pro software (Mulgrave, Victoria, Australia) were used for the determination of B, P, and Mo content in biosludge samples.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{l}{**Plasma Conditions**} \\ \hline Plasma gas & Nitrogen (by using an Air Compressor and a \\  & Nitrogen Generator) \\ \hline Power of Magnetron out put & 1 kW \\ \hline
**Gas Flows** & \\ \hline Plasma Gas flow-Nitrogen & 20 L/min \\ \hline Intermediate flow-Nitrogen & 1.1 L/min \\ \hline Pre-optics protection (POP) gas—Air & 25 L/min \\ \hline Nebulizer pressure & 140-240 kPa (Optimize for each group of elements) \\ \hline Nebulizer & OneNeb inert concentric for HF and high Total \\ \hline Spray chamber & Double pass glass cyclonic \\ \hline Solution uptake & 1.4 mL/min (pumped) \\ \hline Pump tubing & 1.02 mm i.d. PVC (Polyvinyl Chloride) \\ \hline Plasma Torch & Quartz torch \\ \hline Plasma Viewing & Axial \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operating parameters of microwave plasma atomic emission spectrometry (MP-AES), inductively coupled plasma optical emission spectrometry (ICP-OES), and flame atomic absorption spectrometry (FAAS).
# Stochastic Natural Vibration Analyses of Free-Form Shells
## 4 Results and Discussion
### Shell Model 2: A Free-Form Shell Consisting of Two Connected Positive--Gaussian Dones
#### 4.2.2 Stochastic and Sensitivity Analysis  
The initial, 100 th, and 1000 th optimized shape of Model 2 in Figure 13 are selected to investigate the influence of uncertainty factors on free frequencies. The samples are generated by LHS. The sample size is also determined as 400, which has been checked for the current model by Equation (3), with \(\xi_{1},\xi_{2}\) as 0.01 and 0.001, respectively.  
Firstly, the first natural frequency is studied, which is depicted in Figure 15. The mean values of the initial guess, 100 th-step shape, 1000 th-step shape is 11.91 Hz, 11.58 Hz, and 10.58 Hz, respectively. Their standard deviation is 0.28 Hz, 0.32 Hz, and 0.28 Hz, respectively. The stochastic analysis shows that the first frequency of this shell model always follows the normal distribution during the optimization process. Their CDFs are depicted in Figure 16 and fitted, which are similar to Model 1.  
Through the sensitivity analysis, the ranking of the three factors with respect to the first frequency is obtained, as shown in Figure 17. It can be seen that the elastic modulus deviation is the most important uncertainty, with a weight of around 60%; the shell thickness deviation has relatively small influence with a high weight of around 30%; and, the initial geometric imperfection imposes negligible effects with a weight of less than 10%. Generally, the ranking is similar to Example 1, except for that the sensitivity value of elastic modulus deviation is much higher. This could be attributed to the change of the mass, as discussed above.  
Figure 14: Evolution of the first three natural frequencies of Model 2 during the optimization process.  
Figure 15: First natural frequency of Model 2.
# Wearable Haptic Device for Stiffness Rendering of Virtual Objects in Augmented Reality
## 3 Experimental Study: Stiffness Rendering of Virtual Objects
### Experimental Setup  
#### 3.1.1 Hardware Setup  
* **Experimental Environment:** An overall snapshot is presented in Figure 4A. A subject is instructed to sit in front of the table surrounded by the MOCAP cameras. The plane of the table is slightly sloped for the subject to look at the virtual object as vertical as possible, where the average vertical distance from the eyes to the plane is about \(30\,\mathrm{cm}\).
* **Stereo Camera:** To capture stereoscopic scenes of a real environment for AR scenes, we utilize Stereolabs(r) ZED Mini [34], which is designed particularly for AR usage and can be equipped with commercial HMDs. The resolution/sampling rate of the captured scenes is \(1280\times 720\) and \(60\,\mathrm{Hz}\), respectively, for each stereo image.
* **Head Mounted Display (HMD):** To display an interactive AR environment, we employ Oculus(r) Rift S [35] as the HMD. We compose AR scenes, where the real environment and virtual objects are co-presented, using the cross-platform game engine Unity(r). We also utilize the existing functionality of adopted hardware for rendering realistic AR scenes (e.g., mixing virtual/real scenes, depth-sensing).
* **Motion Capture (MOCAP) System:** For dexterous finger-based haptic interaction, one of the key prerequisites is to track the motions of finger-tips and the HMD in real time. In this study, we utilize an Optitrack(r) MOCAP system, which consists of a set of IR reflective markers and surrounding IR cameras. The MOCAP system provides 3D positions of markers with an accuracy less than \(1\,\mathrm{mm}\) at \(200\,\mathrm{Hz}\). We attach two MOCAP markers on the index/thumb finger-tips of the developed haptic glove and four MOCAP markers on the HMD. Note that, for real-world AR manipulation, the MOCAP system can be replaced with an affordable tracking algorithm (e.g., [20]), yet, the MOCAP system is employed here to minimize the effect of tracking error on the stiffness perception.
# Mobile Robot for Security Applications in Remotely Operated Advanced Reactors
## 9 Introduction to Mobile Dog Robots
### Sensory Capabilities
#### 9.2.2 Navigation and Object Detection  
The SPOT robot has an integrated radiometric thermal camera, a pan-tilt-zoom (PTZ) camera with 30\(\times\) optical zoom, an infrared (IR) camera, a 360\({}^{\circ}\) camera, and audio with a microphone. In contrast, the Ghost Robotics robot also has a PTZ camera along with a LIDAR and thermal (IR) surround sensor, and the ANYmal Robotics robot has a zoom camera, a depth camera along with a LIDAR sensor, and a thermal camera. Overall, for navigation, the ANYmal Robotics robot has better navigation and object detection capabilities.
## 2 Materials and Methods
### Design of Film Removal Device  
As shown in Figure 2, the film removal device is mainly composed of a vane-type film removal roller 5 and diversion shell 4. The vane-type film removal roller 5 includes a roller 10, film stripping blade 15 (hereinafter referred to as blade), and blade connecting plate 11. The roller 10 material is a seamless steel pipe with a certain wall thickness. The blade connecting plate 11 is evenly welded on the surface of the roller 10. The main structure of the diversion shell 4 is composed of the top cover 3, the bottom plate 7 and the side plate 2. One end of the diversion shell 4 is provided with a film removal area 6, which is convenient for the interaction between the arc-shaped tooth and the blade 15. The other end is provided with a film conveying port 9 to guide the residual film into the residual film recovery box.  
The film removal device is the core part of an arc-toothed residual film recovery machine, and it is also the key to determining the film removal rate. As the residual film before sowing is mostly in the form of a \"broken\", soft texture and strong electrostatic adsorption force, to prevent tearing of the residual film during film removal, the rotation direction of the vane-type film removal roller is designed to be opposite to that of the film picking device [27, 28], as shown in Figure 1. The working principle of the film removal device is that the blade acts on the residual film on the same arc-shaped tooth many times.  
Figure 1: Structure diagram of the whole machine. 1. Three-point suspension device. 2. Gearbox. 3. Film picking device. 4. Chain drive mechanism. 5. Film stripping device. 6. Frame. 7. Suppressing soil cover device. 8. Residual film recovery box. 9. Arc-shaped tooth. 10. Spoke plate. 11. Depth limit wheel.  
Figure 2: Structural schematic diagram of the film removal device (**a**) and structure diagram of the vane-type film removal roller (**b**). 1. Air inlet. 2. Side plate. 3. Top cover. 4. Diversion shell. 5. Vane type film removal roller. 6. Film removal area. 7. Bottom plate. 8. Frame. 9. Film conveying port. 10. Roller. 11. Blade connecting plate. 12. Shaft. 13. Bolt. 14. Blade pressing plate. 15. Blade.  
After overcoming the winding and adsorption force of the residual film on the arc-shaped tooth, the residual film is removed, that is, mechanically stripped. When the vane-type film removal roller rotates at high speed in the diversion shell, a stable air flow field is generated inside the diversion shell. The removed residual film is blown into the residual film recovery box along the direction of the film conveying port by virtue of the air flow force, that is, airflow-type film removal, which reduces the winding phenomenon between the removed residual film and the blade.
## 4 Discussion  
### Classification Accuracy  
The results (Tables 1 and 2) showed that MSG-GCN outperformed the other models. MSG-GCN better extracted the features of minority classes. The IoU accuracy for NMX was 0.4374, which was higher than those of U-Net++ (0.3367) and U-Net (0.3440), and shows the utility of augmentation techniques, including the cropping, rotating, and flipping (of input images), as well as the Dice loss function [103]. The results are in line with previous studies; the classification accuracy for NMX was lower than those for CP or NBL [10]. [PERSON] et al. [32] provided a relatively reliable planted forest (OA = 84.93 %,  
Figure 10: The forest types misclassified by the various models.  
F1 = 0.85) classification at a national scale using multisource remote sensing data, while the poor data quality and complex terrain and vegetation conditions brought errors in planted and natural forest classification. In CP areas, seedlings are usually planted in lines, such that high classification accuracy is possible. However, if the lines are not evident because some trees have died or been harvested or broadleaved trees have naturally regenerated, there is a tendency toward error [2]. The borders of NMX areas are not sharp and the manual and automatic classification results varied. Although visual classification can be considered accurate, errors may exist due to inaccurate manual delineation and objective variance [3]. NMX areas with heterogeneous and irregular boundaries, diverse species, and multicanopy layers were the most difficult to map. The edges were not smooth and the canopy textural features were heterogeneous. This severely imbalanced the forest types in terms of the relative proportions of the NMX, NBL, and CP areas, which complicated classification because the dataset was biased toward the majority class; this was associated with errors among the minority classes [104]. For the SOTA experiments, FCN [89] can effectively capture the local feature information of the target, while it cannot obtain the global information due to the small receptive field. A shallow GCN [78] with a single structure cannot effectively spread label information in a large area. During the transmission of node information, it is easy to cause the node feature vector to be oversmoothed and result in similar characteristics of each node, which is not conducive to the final segmentation performance. ViT [90] regards each patch as a task by extracting and refining relational information through a multihead self-attention mechanism, while the model requires for heavy computation capacity and high-quality training data. In addition, the oversmoothing problem limited the number of stacking layers and prevented the model from encoding the position information, which ultimately degraded the segmentation performance. The computation complexity performance of MSG-GCN is not the best among the SOTA experiments, our main aim is to improve classification accuracy while ensuring moderate FLOPs. In the future, we will try to build concise efficient graph semantic models to improve the accuracy of forest type classification.
## 4 Design Guidelines and Loss Analysis
### Inverter Components Selection  
Based on the power switch voltage stresses mentioned in Section 3, the discrete devices of the inverter, \(S_{1}\) and \(S_{2}\), must be rated to withstand at least \(+2V_{dc}\), while \(S_{3}\) and \(S_{4}\) must be rated for \(+V_{dc}\) voltage. Neglecting the phase shift caused by the output filter inductor, the duty cycle \(d(t)\) and grid current \(i_{g}(t)\) can be described as follows [6]  
\[d(t)=D_{m}\sin(\omega t) \tag{11}\]  
\[\langle i_{g}(t)\rangle=I_{m}\sin(\omega t) \tag{12}\]  
where \(D_{m}\) is the maximum duty cycle, \(I_{m}\) is the amplitude of the output current, \(\langle\rangle\) denotes the average value over the grid switching period \(T_{g}\), and \(\omega\) is the grid angular frequency. Accordingly, the current stresses of the power switches can be averaged over the grid switching cycle as  
\[\langle i_{S1}(t)\rangle=d(t)\langle i_{g}(t)\rangle,\ \text{from}\ 0\ to\ \frac{T_{g}}{2} \tag{13}\]  
\[\langle i_{S2}(t)\rangle=d(t)\langle i_{g}(t)\rangle,\ \text{from}\ \frac{T_{g}}{2}\ to\ T_{g} \tag{14}\]  
\[\langle i_{S3}(t)\rangle=d(t)_{zero}\langle i_{g}(t)\rangle,\ \text{from}\ \frac{T_{g}}{2}\ to\ T_{g} \tag{15}\]  
\[\langle i_{S4}(t)\rangle=d(t)_{zero}\langle i_{g}(t)\rangle,\ \text{from}\ 0\ to\ \frac{T_{g}}{2} \tag{16}\]  
The duty cycle during the zero state denoted by \(d(t)_{zero}\) is complementary to \(d(t)\)  
\[d(t)_{z\sigma o}=1-d(t)=1-D_{m}\sin(\omega t) \tag{17}\]The input capacitor of the DC bus can be sized based on the method suggested in [30] as  
\[C_{in}=\frac{P}{2\omega~{}V_{dc}~{}\Delta V} \tag{18}\]  
Note that \(V_{dc}\) is the DC bus voltage, \(P\) the power of the inverter, and \(\Delta V\) is the maximum permissible voltage ripple. The final part of the proposed topology is the LCL stage filter, which can be designed based on the analytical method suggested in [31]  
\[L_{f}=\frac{V_{dc}}{8~{}f_{sw}~{}ripple\%_{l}~{}I_{m}} \tag{19}\]  
\[C_{f}=\frac{x~{}P}{\omega~{}V_{o\_rms}{}^{2}} \tag{20}\]  
where \(L_{f}\) is the filter inductor in the inverter side, \(f_{sw}\) is the switching frequency, \(ripple\%\) the inductor current ripple ratio required, \(x\) is the reactive power percentage absorbed by the filter capacitor, the typical value of which is 0.05, and \(V_{o\_rms}\) is the RMS output voltage of the inverter. Then, the grid side inductor \(L_{g}\) can be designed based on the methodologies recommended in [31; 32; 33], to limit the inverter output current harmonics according to IEEE 519-1992 and ensure system stability.  
\[r_{att}=\left|\frac{\frac{1}{1_{att}}-1}{1-L_{f}(2\pi f_{sw})^{2}C_{f}x}\right| \tag{21}\]  
\[L_{g}=r_{att}L_{f} \tag{22}\]  
where \(I_{att}\) is the attenuation factor to achieve the required LCL resonance and \(r_{att}\) is the ripple attenuation.
# Variance-Triggered Two-Step GPS Acquisition
## 5 Experiments and Results
### Time to Acquisition  
In the previous two Subsections, we have shown that the VTTS acquisition was equivalent or superior to the other four algorithms. In this Subsection, we present other important results of the proposed VTTS method. We show that additional to presenting better success and false-positive rates, it does so with much shorter processing time.  
We measured the average acquisition time of the three SA versions and the VTTS after 1, 5, 10 and 18 rounds. Figure 10 present those measurements for increasing C/N0 values.  
For C/N0 values lower than 42 dB-Hz, the time to acquisition remained constant for the VTTS, which is due to no peak being detected in the CS step. All SA versions had much larger processing times than VTTS because, although the signal is too weak regarding the noise, all samples are processed before skipping the probing satellite.  
Starting from a C/N0 of 42 dB-Hz, all algorithms begin to improve average acquisition time. For VTTS, acquisition time reduces at a higher rate for higher quality signals.  
The performance gain of VTTS in comparison to SA-FTD in C/N0 rates lower than 44 dB-Hz varies from 49.4% to 96.9%, depending on the number of rounds used. In this case, the detection probability is very small for both algorithms. Spending less time in this situation is much desired to avoid waste of important resources, such as energy in battery powered devices. From C/N0 = 44 dB-Hz on, success rate starts increasing and the performance gain of the VTTS varies between 45.9% and 94.1%. Less time in this situation means faster Time To First Acquisition (TTFA). Table 1 presents average acquisition times depicted in Figure 10.  
Figure 9: False-positive rate between the three SA versions and the VTTS acquisition for 1, 5, 10 and 16 rounds.
# Robust Sales forecasting Using Deep Learning with Static and Dynamic Covariates
## 2 Autoregressive Neural Network Model
This distribution can be used to assess the forecasting accuracy of the model by providing a measure of the uncertainty associated with each prediction. The sampling mechanism also allows the model to be used to generate different forecasting scenarios. By sampling from the distribution of predictions, the model can be used to generate a range of possible outcomes, which can be used to inform decision making. Sales data often exhibit a zero-inflated distribution, meaning that there are a significant number of observations that equal zero [16]. This can pose a challenge for forecasting models, as they are typically not designed to handle zeros [17; 18; 19; 20]. To address this challenge, we used the negative log-likelihood of the Tweedie distribution as our loss function [21; 22]. The Tweedie distribution is a flexible distribution that can accommodate zero-inflated data, and the negative log-likelihood is a well-established loss function for Tweedie models. Thisapproach allowed us to develop a forecasting model that was able to accurately predict both zero and non-zero sales. \[f(y;\mu,\phi,p)=\frac{y^{p-1}\exp\left(\frac{y\mu^{1-p}}{\phi(1-p)}\right)}{\phi(1 -p)y^{p}\Gamma\left(\frac{1}{1-p}\right)},\quad y>0. \tag{4}\]  
Here, \(\Gamma\) represents the gamma function, and \(\mu\), \(\phi\), and \(p\) denote the mean, dispersion, and power parameters, respectively. When \(p\) lies between 1 and 2, the distribution takes on the form of a compound Poisson-gamma distribution, which is frequently used for datasets displaying positive skewness and a significant number of zeros. The dispersion parameter \(\phi\) regulates the level of diversity or heterogeneity in the data. A small value of \(\phi\) suggests high dispersion in the data, whereas a large \(\phi\) value indicates homogeneity.