esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Problem Formulation
### Interference Model
#### 3.4.1 Co-Channel Interference between LEO Beams  
The schematic diagram of co-channel interference between beams is shown as Figure 3.  
In Figure 3, beam \(b\) and beam \(i\) are lit in the same time slot and their allocated frequency bands overlap with each other, then the interference from beam \(i\) received by terminal \(u\) in beam \(b\) is calculated as follows.  
\[I_{i,u}^{CO}=P_{i}G_{i}(\theta_{i,u})G_{u}(\theta)/L \tag{8}\]  
where \(P_{i}\) is the transmitting power of beam \(i\), \(G_{i}(\theta_{i,u})\) is the gain of beam \(i\) in the angular direction \(\theta_{i,u}\) deviating from the main axis, \(G_{u}(\theta)\) is the receiving gain of terminal \(u\), and \(L\) is the transmission loss.  
In this paper, in order to simplify the model without losing generality, we assume that the terminals covered by the beam are all located at the center of the beam, that is, in the above formula, \(G_{u}(\theta)\) is a constant equal to peak gain of the receiving antenna, and in actual calculation, \(\theta_{i,u}\) should be substituted into \(\theta_{i,u}+\theta\) as shown in the figure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Notation** & **Value** \\ \hline LEO satellite orbit height & \(H_{l}\) & 1050 km \\ LEO satellite beam radius & \(R_{l}\) & 40 km \\ GEO terminal receiving antenna half power angle & \(\theta_{\rm 3 dB}^{g,g}\) & 1.58\({}^{\circ}\) \\ LEO terminal receiving antenna half power angle & \(\theta_{\rm 3 dB}^{g}\) & 2.06\({}^{\circ}\) \\ antenna efficiency & \(\eta\) & 0.7 \\ constant related to the field distribution & \(N\) & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Antenna model simulation parameters.  
Figure 3: Schematic diagram of co-channel interference between beams.  
The total interference to the terminal \(u\) is the sum of the interference from all the beams illuminated at the same time slot, which is denoted as  
\[I_{tot,u}=\sum_{i}\gamma_{u,i}I_{u,i}^{CO} \tag{9}\]  
where \(i\) represents all surrounding cells illuminated by co-frequency and co-polarized beams, and \(\gamma_{u,i}\) is the interference coefficient of the two cells, which is defined as the ratio of the overlapped sub-bands of the two cells to the bandwidth of the total used frequency band as sketched in Figure 4.  
As shown in Figure 4, beam \(i\) and beam \(j\) occupied four and three sub-bands, respectively, and the overlapping part contains two chunks. For beam \(i\), the interference coefficient is \(1/2\), and for beam \(j\), the interference coefficient is \(2/3\).
## 5 Summary and Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Slab ID**} & \multirow{2}{*}{
\begin{tabular}{c} **Center** \\ **Frequency** \\ \end{tabular} } & \multirow{2}{*}{**Bar 1**} & \multirow{2}{*}{**Bar 2**} & \multirow{2}{*}{**Bar 3**} & \multirow{2}{*}{**Bar 4**} & \multirow{2}{*}{**Bar 5**} & \multirow{2}{*}{**Bar 6**} & \multirow{2}{*}{**Bar 7**} & \multirow{2}{*}{**Bar 8**} & \multirow{2}{*}{**Bar 9**} & \multirow{2}{*}{**Bar 10**} \\ \hline \multirow{4}{*}{C} & 1 GHz & X & ✓ & X & X & X & & & & & \\ \cline{2-11}  & 2 GHz & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & - & - & - & - \\ \cline{2-11}  & 0.2 to 6 GHz & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & - & - & - & - \\ \hline \multirow{4}{*}{J} & 1 GHz & ✓ & ✓ & ✓ & ✓ & ✓ & & & & & \\ \cline{2-11}  & 2 GHz & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \cline{2-11}  & 0.2 to 6 GHz & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \multirow{4}{*}{L} & 1 GHz & X & X & X & ✓ & ✓ & ✓ & & & & \\ \cline{2-11}  & 2 GHz & X & ✓ & X & X & ✓ & ✓ & ✓ & & & \\ \cline{2-11}  & 0.2 to 6 GHz & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & - \\ \hline \hline \end{tabular} Note: ✓ = detectable, X = not detectable.  
\end{table} 
Table 7: Bar detectability in depth slices performed using devices with different center frequency.
## 3 Network Scenario and QoT Model  
### General Assumptions  
The spatially-spectrally flexible optical network that we consider in this study makes use of weakly coupled multi-core fiber links that carry optical signals transmitted as spectral super-channels. Each spectral SCh is formed by a group of optical carriers (OCs), which occupy an appropriately allocated frequency slot within a flexible frequency grid on the assigned MCF core. A frequency slot represents a subset of adjacent frequency slices on the frequency grid. We assume that guard bands are introduced between adjacent SChs for filtering and switching purposes.  
The transmission and reception of OCs is realized using coherent transceivers. The transceivers operate at fixed band rates and generate OCs that occupy a fixed-width spectrum channel. The transceivers may use various modulation formats (MFs), and therefore, they can support different bit rates that depend on the spectral efficiency of the MFs. The MFs are selected based on the optical link properties. The OCs of a SCh use the same MF. Each SCh makes use of the required number of transceivers, according to the number of OCs it is composed of. In particular, if the bit rate of a traffic demand is above the transceiver capacity, assuming the selected MF, this demand is realized in the network using several transceivers.  
The lightpaths established in the network satisfy the spectrum and spatial continuity constraints. Namely, the spectrum continuity constraint imposes that the frequency slot assigned to the Sch carried by a lightpath does not change its routing path. The spatial continuity constraint means that a lightpath has assigned the same core in each MCF on the routing path. Spatial continuity simplifies the SDM switch architecture [27], and such an assumption is frequently used in the literature [8].
# Evaluation of the Possibilities of Using Out Malt in Wheat Beadmaking
## 3 Results and Discussion
### Characteristics of Nutritional Value and Antioxidant Potential of Bread  
The results of nutritional and energy value as well as the antioxidant potential of the tested bread are shown in Table 5.  
The control bread (WB) was characterized by statistically lower protein, fat, and TDF contents (average 12.82, 0.16, and 3.29% d.m., respectively), as well as statistically higher carbohydrate content (average 82.18% d.m.) compared to the other tested bread samples (Table 5). Meanwhile, the total dietary fiber (TDF) content ranged on average from 3.29 to 3.88% d.m. (for WB and OB, respectively). Considering that the dietary fiber content in 100 g of bread supplemented with 10% non-bread flour ranged from 2.18 to 2.28 g (for OMB and SMB, respectively) (Table 5), in light of EU legislation [71], these products could not be included in the criteria for using a nutrition and health claim. The data obtained for the bread samples seem to be consistent with the results of the TDF content in the flour samples used in this study (Table 2). The statistically higher contents of insoluble dietary fiber (IN-DF) were found in SMB (average 2.91% d.m. ), and the lowest in the control wheat bread (average 2.49% d.m.). The share of the soluble dietary fiber fraction (S-DF) in the bread samples (Table 5) constituted approx. 1% of total dietary fiber content (TDF). The highest soluble dietary fiber (S-DF) was found in bread samples supplemented with oat flour, i.e., OB and OMB (about 1.0% d.m.). Oat flour and other oat products are a rich source of beta-glucan, which constitutes the soluble fiber fraction (S-DF) and thus substances with health-promoting properties [72; 22; 73; 23]. Research by [PERSON] et al. [64] proved that replacing part of wheat flour in the recipe with a 30% addition of oat flour resulted in a significant increase in the beta-glucan content (almost 3-fold) and the TDF content (over 4-fold) compared to wheat bread (control sample). [PERSON] et al. [75] reported that the total beta-glucan content in the assessed European oat genotypes ranged from 2.85 to 6.77% d.m., and the soluble fraction from 2.05 to 5.29% d.m., which constituted 50.7-87.0% of their total content. During the malting process, the beta-glucan present in the grain is broken
## 6 Conclusions  
This study systematically investigated landslide time prediction through both single-point and multi-point group joint predicting approaches, addressing three operational scenarios: simple data exclusion, velocity data fitting, and out-of-range data elimination. The principal findings are summarized as follows:  
1. A novel multi-point joint inverse velocity method for landslide time prediction is developed, which achieves prediction errors consistently constrained within 1 h. Minimal discrepancies (<2 h) were observed based on strict data exclusion, with enhanced stability and reliability emerging proportionally to the number of displacement monitoring points incorporated.
2. Point groups distributed across different landslide sectors can all be utilized for inverse velocity method-based failure time prediction, with variations in the slope of the inverse velocity fitting line reflecting spatial heterogeneity.
3. Single-point deformation velocity monitoring data yield inverse velocity method predictions with stochastic error distributions and limited stability.  
Conceptualization, Y.R., Y.Z., Z.Y., and H.M.; Methodology, Y.Z., and Y.R.; Software, M.M.; Writing--Original Draft Preparation, Y.R.; Writing--Review and Editing, Y.R., S.H. Z.Y., and H.M.; funding acquisition, H.M. All authors have read and agreed to the published version of the manuscript.  
This article was funded by the Research, Development, and Demonstration Application of Key Technologies for Precise Monitoring and Early Warning of Slip on High and Steep Slopes (2024 EMST080802), and the Fundamental Science Research Foundation of China Academy of Safety Science and Technology [2024]BKY20].  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
All authors was employed by the company Cathay Safety Technology Co., Ltd. All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
## 2 Autonomous DDboats
### Modeling of the DDboats  
The state and disturbance estimation procedures investigated in Section 3 make use of the following Dubins car model [5] (also often referred to as a simple kinematic car model):  
\[\begin{bmatrix}\dot{x}_{1}^{(i)}(t)\\ \dot{x}_{2}^{(i)}(t)\\ \dot{v}^{(i)}(t)\\ \dot{\phi}^{(i)}(t)\\ \dot{z}_{1}^{(i)}(t)\\ z_{2}^{(i)}(t)\end{bmatrix}=\begin{bmatrix}\cos\left(\phi^{(i)}(t)\right) \cdot v^{(i)}(t)\\ \sin\left(\phi^{(i)}(t)\right)\cdot v^{(i)}(t)\\ u_{v}^{(i)}(t)+z_{2}^{(i)}(t)\\ 0\\ 0\end{bmatrix} \tag{10}\]  
with the state vector  
\[\mathbf{x}^{(i)}(t)=\!\left[x_{1}^{(i)}(t)\quad x_{2}^{(i)}(t)\quad v^{(i)}(t )\quad\phi^{(i)}(t)\quad z_{1}^{(i)}(t)\quad z_{2}^{(i)}(t)\right]^{T}\;, \tag{11}\]  
and the effective system inputs \(u_{\phi}^{(i)}(t)\) and \(u_{v}^{(i)}(t)\), as well as the external disturbances \(z_{1}^{(i)}(t)\) and \(z_{2}^{(i)}(t)\), that are included in (10) by means of two independent integrator disturbance models \(z_{1}^{(i)}(t)=0\) and \(z_{2}^{(i)}(t)=0\).
## 2 Materials and Methods  
### Area Description  
The YP is located between latitudes 17\({}^{\circ}\) N and 22\({}^{\circ}\) N comprising the Mexican states of Quintana Roo, Yucatan, and Campeche, and northern Belize. The Mexican portion of the YP has 1941 km of shoreline representing 17% of the nation's length, which is unevenly partitioned between the GOM (765 km) and the CS (1176 km); Belize's shoreline in the YP extends only 386 km. In 2015, the population of the YP was ~4.9 million (Yucatan: 43%; Quintana Roo: 31%; Belize: 18%). The five largest cities in decreasing order of total population are Merida, Cancun, Campeche, Chetumal, and Playa del Carmen (5) (Figure 1B); coastal cities in Quintana Roo account for 45% of Mexico's tourist revenue [52; 53].  
Figure 1: (**A**) Yucatan Peninsula (YP) topography. Shaded gray colors indicate relative elevations above 15 m; 50–250 m contours lines are shown in white color; plus signs are locations of 166 meteorological stations used in the computation of rainfall accumulation during tropical cyclone development over the YP; inset shows the YP location in Mexico, the Gulf of Mexico, and Caribbean Sea. (**B**) Red dots mark landfall positions for a selection of tropical cyclones from 1980–2007 and blue squares/circle are urban locations mentioned in the text.  
The average annual rainfall accumulation (1981-2010) was observed to be variable across the YP, with the highest values ranging from 1283-1333 mm (e.g., Merida, Cancun, Chetumal, Playa del Carmen, Canasayab) and lowest from 697-747 mm (e.g., Chicxulub, Dzilam, Celestun) [54; 55]. The rainy season represents 70% of the annual total and occurs from May through October [56]. There are spatial differences in the precipitation distribution, with significant accumulations (800-1600 mm/year) in the southern and central peninsula while drier conditions (\(<\) 800 mm/year) are registered in the northern Yucatan state [57; 58].  
The regional geology consists of almost pure carbonate rocks and evaporites that define an unconfined flat lying karst landscape [37]. A distinct physiognomic structure is the Chicxulub basin, located in the northeast (Figure 2A). This basin is delimited by karstic sinkholes (\"cenotes\") that route groundwater flow towards the central and northern YP [59]. Freshwater discharges occur mostly on the north coast supporting freshwater, brackish, and saline habitats, including productive wetlands and seagrasses in coastal and estuarine waters [40; 60]. Relative ground elevations in the south range between sea level to \(\sim\)300 m (Figure 1). The only significant topographic relief close to the coastline is in the southern region of the YP with a maximum elevation of 210 m. The northern coast of the peninsula is delineated by a series of lagoons where discharge is regulated by underground caves due to high rock permeability and gentle slopes [40; 61]. Groundwater discharge along the Quintana Roo shoreline (e.g., Tulum; Figure 1), is distributed throughout a network of caves extending 8-12 km inland [62].
## 2 Methods
### Data Collection
#### 2.1.1 Welding under External Strain  
The welding experiments were carried out with the Trudisk 16,002 disk laser from Trumpf, with a maximum output power of 16 kW, a wavelength of 1030 nm, and a beam parameter product of 8 mm x mrad. The welding experiments were carried out on sheets of austenitic steel grade 1.4404 (AISI 316 L) with a thickness of 2 mm. The chemical composition of the material, determined using the spectral analysis, is summarized in the Table 1. The welding parameters applied were 2 kW laser power and a constant welding speed of 1.2 m/min, at a focus position of +5 mm. Argon with a flow rate of 20 l/min was used as shielding gas. In these experiments, all welds were bead-on-plate welds and the seam length was 100 mm.  
The solidification cracks were generated during laser beam welding using the hot cracking test under external loading. This type of hot cracking test or so-called externally loaded hot cracking test was developed to induce cracking in the specimen by external load. The controlled tensile weldability test was used in these experiments, where the weld specimen was subjected to a predefined strain and strain rate perpendicular to the welding direction while welding (see Figure 1). The strain was not controlled during the welding process; only a predefined transverse displacement corresponding to the targeted strain was applied.  
The welded specimens were subjected during welding to a strain of 7% at three different strain rates 4 s \({}^{-1}\), 6 s\({}^{-1}\) and 8 s\({}^{-1}\). The strain was applied after 30 mm after the start of welding with the corresponding (strain rates). Afterwards, the applied strain was kept until the end of welding. Figure 2 shows, schematically, the CTW test procedure during the welding process.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**C** & **Cr** & **Ni** & **Mn** & **Mo** & **Si** & **P** & **S** & **N** & **Fe** \\ \hline
0.03 & 16.95 & 10.57 & 1.36 & 2.28 & 0.39 & 0.04 & 0.004 & 0.019 & Bal. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition (in wt-%) of the investigated material.  
Figure 1: The experimental setup of the welding under external strain using CTW test.
## 2 An Overview of the Deployable Reflector Antenna Based on CFRP Honeycomb Sandwich Structure  
Figure 1 shows the general development process of the satellite/payload. First of all, in the development model (DM) phase, the function and performance tests are performed on the partially simplified models from a mechanical perspective. For example, the DM phase includes the deployment of solar panels in orbit, verification of assembly, and specimen testing. In the structural-thermal model (STM) phase, the environmental tests are performed to evaluate the structural stability against the acoustic and vibrational loads. In the STM phase, the verification for the deployment mechanism is not required, because the satellite/payload should maintain the stowed condition in the launch environment. However, the center of gravity (CoG) of the STM needs to be aligned to mimic the mass properties of the real model. Therefore, in the STM stage, the hinge, synchronization, and belt mechanisms are designed by dummy structure without the functionality of the deployment. The qualification model (QM) is designed/manufactured reflecting the environmental results performed in the STM phase. The QM has the function/performance same with the flight model. The experimental verification in QM phase includes not only the several dynamic structural tests same with STM phase but also the thermal vacuum test for the orbital environment.  
Figure 2 shows the deployable reflector antenna model under development. In previous papers of this series, we dealt with the design [5] and fabrication [13] of the main reflector composed of YSH70A [11], which is a space-grade carbon composite, and aramid core [12], which is a lightweight material. In this study, we manufacture the reflector antenna components including the main reflector, main structure, base plate and the three types of hold-and-release mechanism and integrate STM of the reflector antenna under the stowed condition. Finally, we performed the environmental test for the launch environment. The deployment mechanism structure (deployment, synchronizing, belt mechanism) of STM was developed as the dummy structure using Aluminum (AL6061) material, which does not provide a deployment functionality. The experimental results are used in the verification of structural stability for the vibration/acoustic load and the model correlation of the thermal analysis model of the antenna.  
Figure 1: General flow chart for the development of the satellite/payload.
# A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output
## Appendix B Transfer Function for Moving Average  
Begin with the average of \(N\) samples,  
\[y_{k}=\frac{1}{N}\left(x_{k}+x_{k-1}+\cdots+x_{k-(N-1)}\right) \tag{1}\]  
Taking the Z-transform,  
\[\frac{y_{k}}{x_{k}} =\frac{1}{N}\left(1+z^{-1}+\cdots+z^{-(N-1)}\right)\] \[=\frac{1}{N}\sum_{p=0}^{N-1}z^{-p}\] \[=\frac{1}{N}\left(\frac{z^{N-1}}{z^{N-1}}\right)\sum_{p=0}^{N-1}z ^{-p}\] \[=\frac{1}{N}\left(\frac{1}{z^{N-1}}\right)\sum_{q=0}^{N-1}z^{q} \tag{2}\]This series has a solution of the form,  
\[\sum_{k=0}^{n}z^{k}=\frac{1-z^{n+1}}{1-z} \tag{10}\]  
Thus, we have,  
\[\frac{y_{k}}{x_{k}} =\frac{1}{N}\left(\frac{1}{z^{N-1}}\right)\frac{1-z^{N}}{1-z}\] \[=\frac{1}{N}\frac{z^{-(N-1)}-z}{1-z}\] \[=\frac{1}{N}\frac{z-z^{1-N}}{z-1}\] \[\frac{y_{k}}{x_{k}} =\frac{1}{N}\frac{1-z^{-N}}{1-z^{-1}} \tag{11}\]  
Now, to convert to continuous, we have \(z\to e^{sT}\), where \(T\) is the sampling period.  
\[C(s)=\frac{1}{N}\frac{1-e^{-sTN}}{1-e^{-sT}} \tag{12}\]  
Furthermore, we can write this equation using the coherent averaging time, \(T_{co}=NT\),  
\[C(j\omega)=\frac{T}{T_{co}}\frac{1-e^{-j\omega T_{\alpha}}}{1-e^{-j\omega T}} \tag{13}\]
# LED Current Balance Using a Variable Voltage Regulator with Low Dropout \(v_{DS}\) Control
## 4 Design and Experiment
### Experiment for the Multiple String 6-LEDs  
In this experiment, the master and the four slave LCRs are supplied by the same \(VV_{dc}\), and all of the controls are wired with the same dimmer signal. The \(VV_{dc}\) is controlled by the loop of the master LCR. With the \(VV_{dc}\), each slave LCR can self-regulate the MOSFET's \(v_{DS}\) to stay at the low dropout \(V_{Q}\), even at place higher than \(V_{Q}\) toward right-side, where the drain current can always mirror the master current during the dimmer control.  
The variations of the five \(v_{DS}\)'s, with respect to each string's LED current, are shown in Figure 10a, in which the highest \(v_{DS}\) is 1.78 V and the lowest \(v_{DS}\) is 1.37 V, and all are located on the flat \(v_{GS}\)-characteristic curves. With reference to \(v_{DS}=V_{Q}=1.5\) V, the deviation \(\Delta v_{DS}\) above \(V_{Q}\) is 0.28 V and below \(V_{Q}\) is 0.13 V, in which the low deviation is still on the flat \(v_{GS}\)-characteristic curve in the saturation region of MOSFET. The luminance's distribution of the 5-group LED strings during the LED current change is displayed in Figure 10b, in which the adjacent luminance values between 6-LED strings are almost the same during a wide dimmer range. In this measurement, the maximum luminance deviation, of about \(\pm\)0.45 kcd/m\({}^{2}\), occurs at the highest luminance of 62.7 kcd/m\({}^{2}\) (where \(i_{DS}\) = 357 mA) and the minimum deviation of about \(\pm\)0.01 kcd/m\({}^{2}\) at the lowest 2.65 kcd/m\({}^{2}\) (where \(i_{DS}\) = 10 mA). The measured luminance can be expected to be uniform if the proposed 5 \(\times\) 6 LED layout is equipped in a panel display. This experiment successfully demonstrates the current balance strategy for the multiple string LED; particularly, the control theme, which is easy to implement by using simple LCRs with a microchip control. The experimental setup of a scale-down 5 \(\times\) 6 LED display is shown in Figure 11.  
Figure 10: The measurements of the five string 6-LEDs connected in parallel during dimming process: (a) Drain-to-source voltage \(v_{DS}\)’s; (b) Luminance’s emitted from the five string 6-LED, all measures are group-displayed with respect to the string LED currents.