esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Stations  
The layout of the SwissFEL experimental hutches is shown in Figure 12. The Athos soft X-ray experimental area is contained within one large hutch with a floor space of 692 m\({}^{2}\). Space within this hutch is allocated for the optical laser. The Aramis hard X-ray experimental hutches have a combined floor space of 523 m\({}^{2}\) in three separate experimental hutches. The three experimental stations are called Alvra, Bernina, and Cristallina. Both Alvra and Bernina will be ready for users in 2018, with Cristallina to be installed in Phase II concurrently with the Athos installation. The optical laser room is located directly above the first experimental hutch and delivered to the experiment using vacuum transfer-lines (see Figure 10). Further details on the experimental lasers can be found in Section 3.4. In addition to the experimental areas there is lab space allocated in the SwissFEL building for both biological and chemical sample preparation, and a large work area for experimental testing and assembly. Users will also have access to the lab facilities at PSI, including the sample crystallization and characterization facilities at the Swiss Light Source for protein crystallography experiments [129]. In the following sections we will provide details on the two hard X-ray experimental stations, Alvra and Bernina, which will begin user operation in 2018.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operation Mode** & **Wavelength Range** & **Output Energy** & **Output Pulse Duration** \\ \hline
2b (NirUVis) & 240–295 nm & \textgreater{}26 \(\upmu\)J at peak & \textless{}3 \(\times\) pump pulse width \\
2b (NirUVis) & 290–480 nm & \textgreater{}40 \(\upmu\)J at peak & 1.2–2 \(\times\) pump pulse width \\
2b (NirUVis) & 475–533 nm & \textgreater{}466 \(\upmu\)J at peak & 1–1.5 \(\times\) pump pulse width \\
2b (NirUVis) & 533–600 nm & \textgreater{}306 \(\upmu\)J at peak & 1–1.5 \(\times\) pump pulse width \\
2b (NirUVis) & 600–1160 nm & \textgreater{}320 \(\upmu\)J at peak & 1–1.5 \(\times\) pump pulse width \\
2a & 1160–2600 nm & \textgreater{}2000 \(\upmu\)J at peak & 1.2–1.5 \(\times\) pump pulse width \(\leq\)1550 nm \\  & & & \textless{}2 \(\times\) pump pulse width \textgreater{}1550 nm \\
2b (NDFG) & 2.6–9 \(\upmu\)m & \textgreater{}22 \(\upmu\)J @ \(\upmu\)m & \textless{}3\(\times\) pump pulse width \\
2b (NDFG) & 9–15 \(\upmu\)m & \textgreater{}10 \(\upmu\)J & n.a. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Wavelength-dependent laser performance using the optical parametric amplifier (OPA) (mode 2a and 2b), typically pumped with 30 fs, 8 mJ pulses.
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 4 Discussion
### The Spatial Distribution, Attribution, and Policy Recommendations for Cropland  
As shown in Table 3, the rainfed cropland occupied around 94% of the abandoned cropland, while the irrigated cropland accounted for only a small portion. Furthermore, most of the abandoned irrigated cropland fields were small, and there was no large-scale abandonment, whilst the abandoned rainfed cropland exhibited a certain degree of aggregation. The intermittent cropland was often distributed adjacent to abandoned cropland, with the possibility of abandonment. Intermittent cropland and abandoned cropland aggravated the spatial distribution of agglomeration, which posed a hidden danger to food security. Active cropland had the highest concentration, usually located around rivers and near settlements (Figure 5).  
Our study found that the causes of cropland abandonment were influenced by both the impact of human activities and the natural environment. Firstly, rural-urban migration led to a shortage of labor for agricultural activities, especially in economically underdeveloped areas, which was the main reason for cropland abandonment. Secondly, the abandonment rate of rainfed cropland was much higher than that of irrigated cropland, which indicated that some arid areas were not suitable for cultivation. Lastly, the fragmentation of arable plots inhibited large-scale cultivation; thus, the agricultural input-output ratio was low.  
Given the distribution of abandoned cropland in the study area, the government should take targeted measures to improve the use of cropland. Firstly, the government should further increase the subsidies for rural farming and narrow the income gap between urban migrant workers and agricultural laborers. Secondly, appropriate land-use conversion should be carried out for the rainfed cropland unsuitable for cultivation. Lastly, the government should establish a corresponding system of cropland transfer to reduce the fragmentation of cropland caused by human factors, strengthen the agglomeration of scattered cropland, and improve the farming input-output ratio.
## 2 Experimental Technique
### Experimental Facility
The experimental irradiation device (ID) is made considering study task and character of technical maintenance, associated with these tasks. For that, the ID shall provide the possibility of experimental cells placement, with the surface source of charged particles \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parts** & **Type (Grade)** & **Parameters** \\ \hline \multirow{3}{*}{Optical fiber} & BIF600-VIS + NIR & spectral range: 400–2500 nm \\  & P-600-10-UV + VIS & spectral range: 300–1100 nm \\  & P-600-10-VIS + NIR & spectral range: 400–2100 nm \\ \hline Optical spectrometer & QE-Pro-abs & spectral range: 200–950 nm \\ \hline \multirow{3}{*}{High-Speed Fiber-Coupled Detectors} & silicon detector (DET100A) & Rise time 35 ns, \\  & silicon detector (DET100A) & spectral range: 320 nm–1100 nm; \\  & silicon detector (DET25A/M) & Rise time 1 ns \\  & Indium–gallium detector & Bandwidth 5 GHz, rise time 70 ps \\  & (DET08 CFC/M–InGaAs) & spectral range: 800 nm up to 1700 nm \\ \hline PMT module & PDM02-9113W-CN & spectral range: 280–850 nm; \\ \hline MDR-204 monochromator & & spectral range: 190–5000 nm \\ \hline \multirow{3}{*}{oscilloscope} & Tektronix TBS 2204B & 200 MHz Bandwidth \\  & Tektronix TPS 2012 & 100 MHz Bandwidth \\ \hline \multirow{3}{*}{quadrupole mass-spectrometer} & RGA-100 & measured mass range is from 1 a.m.u. \\  & & up to 100 a.m.u. \\ \hline \multirow{2}{*}{pressure sensor} & VSR53D & working range: 1:10\({}^{-4}\)–750 Torr \\  & MTM 9D & working range: 1:10\({}^{-9}\)–750 Torr \\ \hline \multirow{3}{*}{pressure sensor controller} & \multirow{3}{*}{VD10} & Power supply: alternating current, voltage from \\  & & 95 V to 265 V, 50/60 Hz \\ \cline{1-1}  & & Consumed power–25 W \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parts of the experimental facility IMS. Figure 3: Optical system of facility: (**a**) scheme of optical recording system; (**b**) equipment for optical signal registration during calibration: 1—QE-Pro-abs optical spectrometer, 2—PMT module, 3—MDR-204 monochromator, 4—TBS2204B oscilloscope, 5—TPS2012 oscilloscope, 6—DET100A photo detector, 7—DET25A/M photo detector, 8—InGaAs photo detector, 9—PC for experimental data recording, PXI1, PXI2—system lines, technological control of IGR reactor. (lithium CPS) at the level of the IGR reactor core center, adjustment, and the maintaining of the temperature regime under the survey of ID experimental cell housing. The next subsection presents a detailed description of the developed ID.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### Fuzzy Logic
#### 3.1.8 Sif  
SIF is a Swarm Intelligence protocol based on FL routing and considers the residual energy [39], the distance to the sink, and the distance from the cluster center to select appropriate CHs. SIF uses a Fuzzy C-Means (FCM) clustering algorithm to cluster all sensor nodes into balanced clusters, the appropriate CHs are selected via the Mamdani fuzzy inference system. SIF integrates a hybrid swarm intelligence called FA-SA. FA-SA is based on the firefly behaviors algorithm (FA) and the powerful local search algorithm SA, both are used to optimize the fuzzy rule base table of the fuzzy system. Authors haveshown that SIF is energy efficient in terms of forming balanced clusters, minimizing the intra-cluster distances, prolonging the network lifetime, and maximizing the total number of data packets received in the sink. The comparison of SIF against protocols LEACH, LEACH-DT, ASLPR, and LEACH-FL shows that the proposed protocol significantly increases the network lifetime (by increasing the first node dies FND, the half node die HND, and last node dies LND). The results obtained also show that the amount of data received by the sink node on SIF is better than the others protocols according to the FND, the HND, and the LND.
# Optimisation of Cable Dome Structure Design for Progressive Collapse Resistance
## 4 Optimised Design
### Locally Optimised Design
First, the structural index \(\beta_{10}\) decreased as the number of iterations increased, and it remained constant after the 82 nd iteration. Second, the index \(\beta_{10}\) decreased by 60.26% from 0.5083 to 0.2020 in the optimisation process. In the optimised design, the parameters were as follows: \(\mathrm{S}_{1}=2.712\), \(\mathrm{S}_{2}=4.745\), \(\mathrm{H}_{1}=8.096\), \(\mathrm{H}_{2}=6.878\), \(\mathrm{R}_{1}=29.080\), and \(\mathrm{R}_{2}=13.575\). Figure 11 depicts the model parameters before and after optimisation. Third, when optimisation was conducted separately with each of the three parameter combinations, the optimisation effectiveness (measured by the percentage of \(\beta_{10}\) reduction) was greatest when the hoop cable radii were changed (53.73%), followed by when the strut lengths (10.35%) and then the height differences of the strut nodes (1.01%) were changed.
## 3 Verification of Accuracy of FODNOPP
### Example Verifications for Program Accuracy
#### 3.2.3 Subsonic Counter-Rotating Propfans, Using Code a2  
The three-dimensional unsteady flow field of the counter-rotating propfan model CRPFt, under take-off conditions at an altitude \(H\) = 300 m and a flight Mach number \(M\) = 0.24, is accurately simulated using the NLH solver of NUMECA FINET(tm)/Turbo. Utilizing Aeroacoustics [16], an optional module also based on Formu 1A, integrated within the solver for the discrete frequency noise calculation, the noises at the observer point \(O_{1}\) (0, 0.4665 m, 0), positioned directly above the front propfan with a distance 1.5\(R\) from the rotor axis, including the overall noise and its thickness and loading noise components, all generated by the front, the rear, and both propfans of CRPFt, respectively, are calculated accurately. Through the above two processes, the acoustic pressure time history and spectrum of the overall noise generated collectively by the front and rear propfans of CRPFt are obtained. The results are then compared with those obtained using code a2, as depicted in Figure 5. This figure, along with its detailed description [17], indicates a high degree of congruence of the acoustic pressure time histories. Since the negative sound pressure levels are not practically meaningful, the sound pressure levels of the tones, particularly for the frequencies up to 32 times the rotor shaft frequency, show a considerable degree of similarity across both methods. Thereby, the accuracy of code a2, in predicting the noise of counter-rotating propfans with subsonic helical tip speeds, is effectively demonstrated.  
Figure 4: Overall noise of propeller PROP at sea level \(H\) = 0 m, flight Mach number \(M\) = 0.12, at observer point \(O_{1}\) (0, 7.28 m, 0) separately by using Formu 1-RTE, Formu 1-Sph, and Formu 3, compared with that by using Formu 1A: (a) Acoustic pressure time histories; (b) Spectra (BPF = 107.25 Hz).
## 2 State-of-the-Art: IAR Applications for Training and Assistance
### IAR Assistance Systems  
Training and assistance are two of the most common IAR applications. Assistance systems aim to provide operators and supervisors with visual or auditory information to perform a task [24]. Such information is rendered ubiquitously and seamlessly, to be perceived in a context-aware manner.  
In the literature, IAR has already proven to be useful in a number of previous assistance tasks like the interaction with robots or in guidance systems. For instance, the authors of [25] used AR as an enhanced user interface designed to use visual cues to improve users' spatial perception and provide more accurate dimensions and distances when guiding a telepresence robot. Other recent works have also presented AR-based tools for controlling robot movements [26]. The interested reader can find a systematic review of AR-based remote guidance systems in [27].  
With respect to common Industry 4.0 processes, an example was presented in [28], where the researchers presented a simple use case of an assembly simulator. A similar work with a smartphone-based application that uses Vuforia and Unity software was described in [29]. Such an application was aimed at training and assistance when handling industrial equipment (e.g., PLCs), and its usability was evaluated through a questionnaire.  
Teleassistance is also important when monitoring and repairing machines. Thus, teleassistance systems can ease remote collaboration between workers [30] and provide the same level of information that would be shared when being physically together. Such a type of augmented communication can also be used for collaborative visualization in the different engineering processes [31]. In addition, IAR systems are also able to give quick access to documentation like manuals, 3D models, or historic data [32]. Moreover, IAR is helpful when supporting decision making in real scenarios by combining the physical experience with displayed information that is extracted in real time [33].  
Recently, more sophisticated approaches have been proposed. For instance, an olfactory-based AR system to help with the identification of maintenance issues was proposed [34]. In addition, in [35], the authors aimed to enhance a simulation-based assistance application by adding contextual awareness from the sensors. Another work that included Internet of Things (IoT) interactions was proposed in [36], where a framework was described for Microsoft HoloLens smart glasses that eases the integration between AR and IoT devices. Furthermore, IAR can contribute significantly to the definition of the properties of a digital twin by modeling logical objects within a virtualized space and then associating such a logic with physical entities [37].
## 2 System Description
### Determination of PV/T Panel Size  
The whole geographic area of China is divided into five zones in terms of weather conditions. Among the five zones, the hot summer and warm winter (HSWW) zone located in the south of China has a subtropical humid monsoon climate with strong solar radiation and a long summer. Therefore, sun shading has become a common way to improve the indoor thermal environment of the buildings in the HSWW zone.  
For a PV/T panel used as horizontal sunshade shown in Figure 2, its performance of shading the sunshine from above depends on its overhanging length (A). The minimum overhanging length can be calculated by:  
\[A=H\cot h\cos\alpha, \tag{1}\]  
where \(A\) is the overhanging length (m), \(H\) is the vertical distance between the bottom of the panel and windowsill (m), \(h\) is the solar altitude angle (\(0^{\circ}<h<90^{\circ}\)), \(\alpha\) is the solar azimuth angle (\(0^{\circ}<\alpha<90^{\circ}\)). Then, the panel width is determined by:  
\[W=\frac{A-E}{\cos\theta}, \tag{2}\]  
where W is the panel width (m), \(\theta\) is the tilt angle of the panel (\({}^{\circ}\)), \(E\) is the embedded depth of the windowpane (m). The performance of shading the sunshine from the left and right sides depends on the distances between the left/right sides of the window and the left/right endpoints of the panel (B), the minimum value of which is calculated by:  
\[B=H\cot h\sin\alpha. \tag{3}\]  
Therefore, the panel length can be determined by:  
\[L=D+2H\cot h\sin\alpha, \tag{4}\]  
where \(L\) is panel length (m), \(D\) is window width (m).  
The value of \(H\) is taken as the window height from the viewpoint of reducing overhanging length. In view of the fact that the solar altitude angle and the azimuth angle always vary, it is necessary to determine a design condition for exterior shading of windows. In the HSWW zone, the autumnal equinox (22, 23, or 24 September) is a transition from hot weather to cool weather. Thus, the solar altitude angle and azimuth angle at 11 a.m. or 1:00 p.m. on the autumnal equinox are determined  
Figure 1: Schematic of a BIPV/T solar water heating system for exterior shading.  
as the design condition because the strongest solar radiation during a day occurs around noon. A south-facing window (Orientation: 180\({}^{\circ}\) North) of a residence in Guangzhou, a city in the HSWW zone, is chosen as an application object of the PV/T panel in the presented system. The required hot water production is 120 L per day. The hot water temperature (HWT) is taken to be 45 \({}^{\circ}\)C, which is a little higher than the HWT for practical usage in households. According to the common device configuration of solar water heaters in Guangzhou, the irradiation-collection area of the panel is taken to be 1.6 m\({}^{2}\). The panel size and tilt angle, which make the panel just shade the sunshine at 11 a.m. or 1:00 p.m. on the autumnal equinox, were determined using Equations (1)-(4), and are listed in Table 1.
## 2 Testing Equipment and Methods  
### Bender Elements  
The BE technique is a non-destructive method that enables the determination of \(v_{s}\), which in turn allows the calculation of \(G_{0}\). Since its introduction to soil testing by [PERSON] and [PERSON] [38], the BE technique has become widely accepted and is used in various geotechnical testing devices (e.g., [39; 40]). The generation of shear waves is based on the principle of the piezoelectric effect. When a voltage is applied to a piezoelectric element, it deforms and generates mechanical stress and vice versa. The transmitted and received signals are recorded (Figure 1) to determine the arrival time of the shear waves \(t\), from which \(v_{s}\) can be calculated if the distance between the piezoelectric elements \(L_{tt}\) is known:  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multicolumn{2}{c}{**G/G\({}_{0}\)**} \\ \cline{2-3}  & **Presentation** & **Parameters** \\ \hline [PERSON] and [PERSON] (1991) [17] & graphically & \(PI\) \\ \hline EPRI (1993) [29] & graphically & \(PI\) \\ \hline [PERSON] (2001) [22] & equation & \(PI\), \(OCR\), \(p^{\prime}\) \\ \hline [PERSON] et al. (2005) [30] & equation & \(PI\), \(p^{\prime}\) \\ \hline [PERSON] and Bolton (2013) [28] & equation & \(PI\) \\ \hline [PERSON] et al. (2020) [31] & equation & \(PI\), \(p^{\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Regression models for \(G/G_{0}\) for cohesive soils.  
Assuming homogeneous linear elastic material with known density \(\rho\), the \(G_{0}\) can be calculated as:  
\[G_{0}=\rho\cdot v_{s}^{2} \tag{5}\]  
Although the BE technique is widely used, signal interpretation remains a challenge, especially in the determination of \(t\)[41; 42; 43]. There are several methods to determine \(t\), which can be categorized into two main groups: time domain methods (arrival-to-future, peak-to-peak and cross-correlation methods) and frequency domain methods (discrete and continuous methods) [41]. Each of these methods has its own advantages and limitations, and there is currently no method for interpreting BE test results that has been clearly shown to be better than others [44]. As there is no clear approach for determining \(t\), the results obtained with BE are subject to a considerable degree of uncertainty and subjectivity [42]. However, time-domain methods are generally simpler and more straightforward, as \(t\) can be determined directly by analyzing the time interval between specific points in the transmitted and received waveforms [14].  
In this study, the peak-to-peak method was used to determine \(t\), as it gave better agreement with the results of the RC test (Figure 2). In contrast, the start-to-start method consistently yielded slightly shorter \(t\) or higher \(v_{s}\), which is close to the results reported by [PERSON] et al. [45]. Given the larger specimen size (\(L_{tt}>80\) mm), we also investigated the possible effects of wave attenuation as discussed by [PERSON] et al. [43].  
Figure 1: Representation of the input and output signals in BE test.  
Figure 2: Schematic representation of the BE setup in the RC device.
# Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things
## 2 LEO Mega Constellation for the IoT
### IoT and LEO Mega Constellations
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Constellation Category** & **Orbital Height (km)** & **Number of Satellites** & **Ground Coverage** \\ \hline Iridium & 780 & 66 & Global \\ SpaceX & 1200 & 4425 & Global \\ Amazon & 590–630 & 3236 & Global \\ OneWeb & 1200 & 720 & Global \\ Hongyun & 1000 & 156 & Global \\ Hongyan & \textasci{}2000 & 300 & Global \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Technical parameters of the constellations.
## 3 Satellite-to-Ground QKD Simulator
### Satellite Orbits  
Our software tool can simulate a comprehensive range of orbits, including LEO, MEO, and GEO, thus ensuring a versatile application for diverse QKD-satellite mission analyses.  
LEO satellite orbits are characterized by their relatively low altitudes, typically within 2000 km above the Earth's surface, thus providing a better link budget for the QKD downlinks and enabling higher key rates. The low orbits enable satellites to complete one orbit around the Earth in approximately 90 to 120 min, thus resulting in frequent communication time windows as they pass overhead. Specifically, for Sun Synchronous Orbits (SSOs), the satellites are visible approximately every 12 h with the same elevation gained by an OGS. Finally, LEO satellites offer global coverage but require constellations of multiple satellites to provide continuous service. Therefore, it is of great importance to consider whether a single LEO satellite pass provides an adequate volume of exchanged bits for post processing when finite key size statistical fluctuations are being considered.  
MEO satellite orbit altitudes typically range between 2000 and 35,786 km. MEO satellites orbit the Earth less frequently than their LEO counterparts, typically completing one orbit every several hours, thus resulting in fewer communication opportunities per day. While MEO satellites provide broader coverage than LEO satellites, their elevation gain strongly depends on the OGS location since most MEO constellation fly across the equator. The trade-off between longer communication time windows and a lower link budget will be considered in the following section in terms of finite key size statistical fluctuations.  
Finally, GEO satellites are located 36,000 km above the Earth's surface. Since they move at the same angular speed as the Earth, they appear to not be moving in respect to an OGS, thus allowing for continuous communication.