esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions
\begin{tabular}{l l}
**Symbols** & **Description** \\ \(A_{n}\) & Total number of user appliances \\ \(A_{d}\) & Deferrable appliances \\ \(A_{b}\) & Base load appliances \\ \(A_{md}\) & Non-deferrable appliances \\ \(H\) & Total 24-h time horizon \\ \(h\) & Time in hours \\ \(E_{b}\) & Total electricity energy utilized by base appliances in a day \\ \(P_{b}\) & Power rating of each appliance belonging to the category of base load appliances \\ \(S_{b}\) & ON/OFF state of base load appliance \\ \(E_{d}\) & Total electricity energy utilized by deferrable appliances in a day \\ \(P_{d}\) & Power rating of each appliance belonging to the category of deferrable appliances \\ \(S_{d}\) & ON/OFF state of deferrable appliance \\ \(E_{md}\) & Total electricity energy utilized by non-deferrable appliances in a day \\ \(P_{md}\) & Power rating of each appliance belonging to the category of \\  & non-deferrable appliances \\ \(S_{md}\) & ON/OFF state of non-deferrable appliance \\ \(k_{a}\) & The required time for an appliance to complete its operation \\ \(\gamma^{\prime}a\) & The required number of time intervals for each appliance to complete its operation \\ \(\alpha\) & Earlier starting time of any appliance \\ \(\beta\) & Least finishing time of any appliance \\ \(E_{hu}(h)\) & Output energy of battery in time interval \(h\) \\ \(E_{bat}^{ch}(h)\) & Charging energy of battery in time interval \(h\) \\ \(E_{hu}^{cl}(h)\) & Discharging energy of battery in time interval \(h\) \\ \(\eta_{cl}\) & Charging efficiency of battery \\ \(\eta_{clh}\) & Discharging efficiency of battery \\ \(E_{ch}^{max}\) & Maximum allowable charging energy of battery in time interval \(h\) \\ \(E_{ddh}^{max}\) & Maximum allowable discharging energy of battery in time interval \(h\) \\ \(SOC(h)\) & State of charge of battery in time interval \(h\) \\ \(SOC(h-1)\) & State of charge of battery in time interval \(h-1\) \\ \(C^{\text{{th}}tery}\) & Capacity of battery \\ \(SOC_{max}\) & Maximum limit of SOC \\ \(SOC_{min}\) & Minimum limit of SOC \\ \(P_{ac}\) & Output power of PV panel \\ \(P_{n}\) & Rated power of PV system \\ \(\mathbf{G}(h)\) & Solar irradiance in time slot \(h\) \\ \(\eta_{DC-AC}\) & Efficiency of inverter \\ \(\gamma\) & Coefficient of power temperature \\ \(\alpha,\beta\) & Parameters of Beta distribution function \\ \(\sigma\) & Standard deviation of the random variable \(\mathbf{G}\) \\ \(\mu\) & Mean of the random variable \(\mathbf{G}^{\prime}\) \\ \(E_{a}(h)\) & Total energy consumed by consumer appliances in time interval \(h\) \\ \(E_{b}(h)\) & Energy consumed by consumer base load appliances in time interval \(h\) \\ \(E_{d}(h)\) & Energy consumed by consumer deferrable appliances in time interval \(h\) \\ \(E_{md}(h)\) & Energy consumed by consumer Non-deferrable appliances in time interval \(h\) \\ \(E_{T}\) & Total energy import from grid of export to the grid \\ \(E^{PV}(h)\) & Energy generated from PV in each time slot \\ \end{tabular}
# Study on Vibration Effects and Optimal Delay Time for Tunnel Cut-Blasting Beneath Existing Railways
## 3 The Mechanism and Effect of Vibration Reduction Through Waveform Interference
### The Phase Division of Seismic Waveform by Blasting  
Blasting seismic waves are primarily composed of compressional waves (P-waves), shear waves (S-waves), and surface waves [31; 32]. For waves generated by the same blasting source, the differences in their propagation velocities result in distinct arrival times at a given monitoring point, eventually leading to the phenomenon of wave separation in  
Figure 4: Time-history curves of the monitoring points: (**a**) Point A, (**b**) Point B\({}_{1}\), (**c**) Point B\({}_{2}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Point**} & \multicolumn{3}{c}{**PPV (cm/s)**} \\ \cline{2-4}  & **X** & **Y** & **Z** \\ \hline A & 6.42 \(\times\) 10\({}^{-1}\) & 2.62 & 1.51 \\ B\({}_{1}\) & 5.78 \(\times\) 10\({}^{-2}\) & 5.27 \(\times\) 10\({}^{-1}\) & 2.78 \(\times\) 10\({}^{-1}\) \\ B\({}_{2}\) & 6.89 \(\times\) 10\({}^{-2}\) & 2.22 \(\times\) 10\({}^{-1}\) & 9.71 \(\times\) 10\({}^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: PPV at each point.  
the recorded waveform [33]. In the intermediate-to-far field of a blast, the first to arrive is the P-wave, characterized by a relatively small amplitude and high frequency, which forms the initial phase of the blasting seismic wave. This is followed by the S-wave, which exhibits lower frequency. Surface waves, typically containing the highest energy, constitute the main period of the blasting seismic wave. The attenuation of ground particle vibration then forms the coda phase.  
Figure 5 illustrates the blasting vibration velocity curve over time. In this analysis, the main vibration phase is defined as the interval where the amplitude of any peak or trough is at least 50% of the Peak Particle Velocity (PPV). The duration of this phase is termed the main vibration duration (\(\Delta\)t\({}_{\text{m}}\)). The PPV, recognized as a key index for blasting vibration safety control [34], occurs during a specific period designated as the main period (T). Both T and \(\Delta\)t\({}_{\text{m}}\) are schematically represented in Figure 5. Taking Figure 5 as an example, select the interval exceeding 50% of PPV from the vibration velocity time-history curve (2.7 ms\(\sim\)6.2 ms). At this time, \(\Delta\)t\({}_{\text{m}}\) is 3.4 ms. Select the range of 3.8 ms\(\sim\)6.2 ms where the PPV is located. At this time, T is 2.4 ms.
## 3 Results and Discussion
For comparison, the bond lengths obtained from the crystal structure (PDB ID: 5]SH from [PERSON] et al. [1]) are listed in bold print. red-shifted (2022 cm\({}^{-1}\), 2038 cm\({}^{-1}\), and 2018 cm\({}^{-1}\)). Nonetheless, the antisymmetric CN-stretching frequency for conformer II (2038 cm\({}^{-1}\)) is closest to the experimental values. The discrepancy between computed and measured CN-antisymmetric stretching frequencies indicates that, although conformer II from [PERSON] et al. may be present in the mixture of the oxidized species of the WT native enzyme measured in the experiment of [PERSON] et al. [10], the antisymmetric stretching vibration may be sensitive to the protein environment in the model. A similar systematic red-shift in the computed CN-antisymmetric stretching frequency has been observed for membrane-bound [NiFe] hydrogenase from _Ralstonia eutropha_[13]. Based on the intense band that they observe at 1940 cm\({}^{-1}\), [PERSON] et al. believe their as-isolated sample is more than 80% in the Ni-IS\({}_{\rm II}\) state [1]; the computed vibrational frequencies for conformers I and III for Fe\({}^{3+}\) (1952 cm\({}^{-1}\) and 1945 cm\({}^{-1}\), respectively) are in best agreement with this frequency. Interestingly, based on electron density occupancy fitting, [PERSON] et al. believe conformer I is dominant (51%), followed by conformer III (29%) and conformer II (20%) [1]. The isolated species of [PERSON] et al. are believed to be diamagnetic Ni\({}^{2+}\) states with one, or more than one, oxygen species in the active site [10]. An interesting suggestion has been made that the side chains of either cysteine or selenocysteine residues can be oxidized to sulfenates; this phenomenon has been used to explain data from X-ray absorption experiments [27]. In their experiments involving the Ni-Fe cofactor of the NAD-reducing soluble hydrogenase, [PERSON] et al. attribute the long measured Ni-S distance (3.6 A) to thiol groups that may be in an oxidized form (Cys-SOH) [30]. Similar chemical modifications of Cys residues have been detected in a range of proteins [31]. [PERSON] et al. also speculate that oxidation of their Ni-IS species leads to a Ni-OX species in which selenocysteine or cysteine ligands are oxidized [10]. In the case of Fe\({}^{3+}\) (Table 3 and Figure 7), the computed CO and CN-symmetric stretching frequencies for conformers I (1952 cm\({}^{-1}\) and 2087 cm\({}^{-1}\)) and III (1945 cm\({}^{-1}\) and 2098 cm\({}^{-1}\)) agree well with the frequencies measured for both WT (1939 cm\({}^{-1}\) and 2094 cm\({}^{-1}\)) and recombinant enzymes (1940 cm\({}^{-1}\), 2085 cm\({}^{-1}\), and 2094 cm\({}^{-1}\)), respectively. The computed CN-antisymmetric stretching frequencies for conformers I and III (2058 cm\({}^{-1}\) and 2060 cm\({}^{-1}\), respectively) are red-shifted compared to the experimental frequencies, behavior that was also observed in the case of Fe\({}^{2+}\).
# Mechanical Performance Characterization of Lignin-Modified Asphal Mixture
## 4 Results and Discussion
### Fatigue Performance  
Four-point beam bending tests were conducted to evaluate the fatigue resistance of asphalt mixture. The chosen fatigue resistance criterion was a 50% loss of the initial stiffness modulus. Figure 10 presents the fatigue life values at five different strain levels of three different mixtures. It can be seen that at a certain strain level, SMA-LP had a significantly longer fatigue life than SMA-C. By contrast, SMA-LF exhibited a lesser fatigue life than SMA-C. The following fatigue law based on the phenomenological method was used to predict the fatigue life at a strain level of interest.  
\[N_{f}=a\varepsilon^{-b} \tag{5}\]  
where \(N_{f}\) is the fatigue life (loading cycles when the specimen fails), \(\varepsilon\) is the applied strain level, and \(a\) and \(b\) are the fitting parameters related to mixture property. For comparison, the \(\varepsilon_{6}\) parameter (the strain at which specimen can endure 1 million loading cycles before failure) of different mixtures was also calculated using Equation (5). The parameters of the fatigue law and the \(\varepsilon_{6}\). parameter are summarized in Table 3. The \(\varepsilon_{6}\) parameter is an indicator of the fatigue resistance of asphalt mixture. The \(\varepsilon_{6}\) parameter of SMA-C, SMA-LP, and SMA-LF was 251, 293, and 209, respectively. From the binder test results, it is known that lignin powder-modified asphalt binder increased the fatigue resistance [7]. It is anticipated that lignin-modified asphalt increases the adhesion between aggregates and delays the development of fatigue cracking. However, it is unexpected that the addition of lignin fiber decreases the fatigue resistance of asphalt mixture. Normally, fiber-reinforced asphalt mixture has a higher cracking resistance than normal mixture because of the bridging effect of fibers among cracks, which delays the propagation of cracking [16]. The reason for this may be due to the poor compatibility between lignin fiber and asphalt, hence, influencing the bonding between aggregates.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mixture Type** & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\varepsilon_{6}\) \\ \hline SMA-C & 2.45E + 17 & 4.745 & 251 \\ SMA-LP & 1.14E + 19 & 5.293 & 293 \\ SMA-LF & 8.53E + 15 & 4.281 & 209 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fatigue law parameters and \(\varepsilon_{6}\).  
Figure 9: Flexibility index from SCB tests.
## 2 Materials and Methods
### The Hyperspectral Flight Campaign and Imagery Processing  
A hyperspectral flight campaign was organized in July 2011. Three flights (Figure 1) were carried out in cloud-free conditions on 8-9 July 2011 at different times of the day using an AisaEAGLE hyperspectral sensor (VIS-NIR, 130 bands; SPECIM, Finland). Hyperspectral images (0.9 m spatial resolution; 400-1000 nm; spectral resolution: 4.3-4.8 nm FWHM) were ortho-rectified, atmospherically corrected, and mosaicked using the ATCOR-4 package of the PARGE software (ReSe applications, Zurich, Switzerland).  
According to the preliminary EC footprint analysis, a circular area with a radius of 30 m (region of interest: ROI) was considered, and data were exported and averaged using ENVI 4.8. In order to calculate the SVIs, S2 bands (Table 1; as in [PERSON] et al. [35]) were considered, and a simulation was obtained by calculating the average reflectance over the bandwidth of the respective MSI bands following [PERSON] et al. [36]. Both traditional VIS-NIR and NIR-based (NIDI; Normalized Infrared Difference Index) SVIs were investigated (Table 2).  
Figure 1: The Viote del Monte Bondone RGB image derived from AisaEAGLE hyperspectral data including the five Eddy Covariance towers positions (indicated by letters from A–E) and the thirty 60 \(\times\) 60 m regions of interest (ROIs) used in this study for optical diversity assessment. Reflectance values for each 0.9 m pixel (in grey) and the ROI mean reflectance value (in red) are indicated as an example of spectral variability within a single ROI. Optical diversity and productivity relationships were analyzed at the thirty 60 \(\times\) 60 m ROIs at an increasing pixel size window (from 0.9-20 m; see the upper right corner). At the bottom, a list of the three flights carried out during the campaign including the adapted solar time window for averaging Net Ecosystem Exchange (NEE) and photosynthetically active radiation (PAR) data.
## 3 Related Work in the Area of the Data Quality Dimension Completeness
The utilization of the feature space is described by the data set completeness. The paper hypothesizes that the data set completeness metric and its visualization reveal the structure of a multi-dimensional data set in more detail than other commonly used statistics and visualizations in data preparation. Furthermore, it will be shown that a reduction of data set completeness harms model accuracy of data-driven models for condition-based maintenance. Thereby, model accuracy is measured by the coefficient of determination \(R^{2}\) and the root mean squared error (RMSE) of the prediction model. The coefficient of determination (1) measures the variance of the model output that can be explained by the model. A high coefficient of determination indicates a good predictability of the model output based on the model input. The RMSE (2) measures the deviation of the predicted and the actual model output providing information about the precision of the model. Due to the impact on model accuracy, the importance of assessing data set completeness is revealed. The example of condition monitoring in control valves will show that a reduced metric for Record [37], Data File [37], Tuple [34] Completeness and Empty Records in a Data File [37] (cf. Table 1), which are the only metrics on record level in the related literature, and does not reduce the data set completeness and decrease the model accuracy necessarily. However, a reduced data set completeness will decrease model accuracy. \[R^{2}=\frac{\sum(y_{i}-\hat{y}_{i})^{2}}{\sum(y_{i}-\overline{y}_{i})^{2}} \tag{1}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum(\hat{y}_{i}-y_{i})^{2}} \tag{2}\]  
with \(\hat{y}_{i}=\) predicted output, \(y_{i}=\) actual output, \(\overline{y}_{i}=\) mean of actual output and \(n=\) number of observations. To develop an efficient metric for data set completeness, four requirements have to be fulfilled [42]. First, the metric requires a defined minimum and maximum value (R1). For data quality, the common definitions set the minimum to 0--perfectly poor data quality--and the maximum to 1--perfectly good data quality. Furthermore, the metric should be interval-scaled (R2), enabling the interpretation of intervals, e.g., completeness of 0.2 is half as complete as completeness of 0.4. Third, the use and the calculation of the metric should be independent of hyper-parameters (R3) to receive an objective measure. It is essential that two different users of the metric obtain the same numerical result. Last, an appropriate aggregation (R4) is required to include the metric in high-level key indicators for data quality. A fifth requirement, economic efficiency, is not considered in this paper. For a visualization, requirements concerning the usability should be considered. However, this paper leaves the evaluation of the visualization for future work, due to the greater extent. In this paper, the visualization is used to support the plausibility check of the proposed metric.
# Study of Partial Discharge Inception Voltage in Inverter Fed Electric Motor Insulation Systems
## 4 Analysis of PDIV and Modelling Tool Performance for Variable Temperature
### Performance of PDIV Models for Variable Temperature  
#### 4.2.1 Dakin Model  
The Dakin model was proposed in [74] and defined as Equation (8) (see Section 2.2). In spite of the relative permittivity being adjusted to the specified temperature, the dependency of the breakdown voltage on the temperature is neglected, which can result in a less accurate PDIV estimation at high temperatures.  
In Figure 11, the comparison between the PDIV values estimated using the Dakin model (considering the evolution of the relative permittivity with temperature) and the experimental PDIV measurements for sample group B at different temperatures is illustrated. It is shown that the PDIV calculated using the Dakin can fit the experimental value at temperatures below 60 \({}^{\circ}\)C with a maximum error of around 10%. Above this temperature, the error keeps increasing with the increase in the temperature and can produce values up to around 27% at 180 \({}^{\circ}\)C.  
Figure 10: Comparison of the effect of temperature: (**a**) proportional evolution of air density and relative permittivity; (**b**) proportional evolution of the PDIV based on the Dakin and Lusuardi models (Relative Permittivity (RP)).  
It could be concluded that only considering the evolution of permittivity with temperature is not enough to accurately predict the PDIV. The effect of the temperature on air properties must be considered, especially at high temperatures. Thus, the Dakin model is not suitable for PDIV estimation during operating conditions for electric motors, where the winding temperature can reach 180 \({}^{\circ}\)C. Even so, the Dakin model was widely used in the literature [38, 59, 78] due to its accuracy and simplicity at ambient temperature.
## 8 Solving the Trajectory Planning Problem
\[\Gamma_{i+1}=\arg\min_{\Gamma}J(\Gamma_{i})+(J(\Gamma)-J(\Gamma_{i}))^{T} \bigtriangledown J(\Gamma_{i})+\frac{\eta}{2}\|\Gamma-\Gamma_{i}\|_{M}^{2}, \tag{64}\]  
where \(M\) is a weighting matrix and \(\eta\) is a regularization parameter. GTO is rather popular due to its ability to deform ineffability trajectory segments, low memory requirement, andhigh throughput. Despite having the listed advantages, GTO cannot avoid the problem of a local minimum. STOMP [54] is one of the early techniques proposed to address the local minimum problem. STOMP is based on the gradient-free technique. However, STOMP is unable to obtain real-time performance. Besides STOMP, the local minimum problem has been addressed by various recent works. However, this remains an open problem to be solved. [PERSON] [121] proposed a method, i.e., path-guided optimization (PGO), for overcoming local minima problems by generating topologically distinct paths and doing parallel optimization. Furthermore, various solvers can be utilized for solving optimization problems, including BOBYQA [122], L-BFGS [8; 123], ACADO [124], SLSQP [125], Proximal Operator Graph Solver (POGS) [126; 127], sequential quadratic programming (SQP), and MMA [128]. [PERSON] et al. [65] proposed a trajectory optimization technique in a distributed setup in which the researchers evaluated their formulation with several solvers. According to their observations, BOBYQA is faster compared to BFGS and SLSQP, while MMA yielded a similar performance to that of BOBYQA. In [129], L-BFGS was proposed for finding the shortest path in real-time; in this research effort, however, L-BFGS does not guarantee optimality, and only feasibility is enforced. Mathematical program with complementarity constraints (MPCC) [130] is yet another proposed method for fast trajectory optimization in real-time. Moreover, [PERSON] and [PERSON] [131] proposed a SQP-based trajectory generation approach for carrying augmented loads. The intuition behind selecting SQP over other solvers is due to its superlinear convergency and ability to handle nonlinear constraints within milliseconds.
## 1 Introduction
### Related Work
In our previous studies [23; 24], we investigated area optimization algorithms using a genetic algorithm (GA) as a first step to achieve HAPS area optimization. The GA is a common search method and a heuristic algorithm in which a population consisting of solution candidates performs operations such as crossover and mutation, which imitates evolutionary processes, to achieve an optimal solution. Despite its simplicity, GA can not only find solutions to complex problems, but it can also solve nondeterministic problems. In [23], assuming a uniform user distribution, the beam directions and beamwidths are optimized so that the spectral efficiency is improved as the objective function. In [24], assuming a nonuniform user distribution, each cell's beam direction and beamwidth are optimized so that the throughput indicator is improved while reducing the number of combinations for GA. However, as mentioned above, search methods such as GA cannot respond to sudden distribution changes, such as those resulting from natural disasters, due to the high computational cost. Furthermore, these methods cannot leverage the results from previous calculations, requiring calculations each time.
## 2 Observations and Methods  
### Angle Observations  
Usually, an observing device can only track a single object at the same time, and this mode of operation limits the ability to catalog space objects. So, a large field-of-view (FOV) multiobject optical telescope array observation mode was created. The equipment used in this paper is described below (shown in Figure 2 and Table 1).  
Figure 2: Physical pictures of Changchun LEO EA ((**a**,**b**), [14]) and Changchun GEO EA and Focus-GEO (c).  
The Changchun Observatory set up an electrical-optical array (EA) telescope for observing LEO objects and GEO objects at the astronomical observation base in Jilin Province in 2017. The \"mini\" EA for observing LEO objects consists of eight small telescopes, each with an aperture of 15 cm and a focal length of 15 cm. The system also includes 8 cameras with 3 \(\times\) 3 K resolution, 8 image processing computers, 1 GPS clock, an electronic control system, an image acquisition and processing system, etc. The monitored sky area is up to 1590 square degrees, mainly observing space debris with elevation angles from 18 to 32 degrees in the sky area with diameters ranging from 0.5 to 1 m.  
The optical telescopes used for observing space debris in GEO orbit are part of an all-sky rotatable array, which is designed for observing space debris. The array consists of four 280 mm optical telescopes and two T-frame structure equatorial instruments, with an average error of 0.9\" in the right ascension direction and 1\" in the declination direction in terms of tracking measurement accuracy. With the same design parameters, the four telescopes can significantly expand the sky area, covering 160 square degrees, and detect objects with brightnesses between 16 and 20 magnitude.  
At the end of 2017, SAO developed the FocusGEO, an EA with a large FOV dedicated to detecting GEO objects, which consists of three 0.18/0.22 m refracting optical cylinders, forming a large rectangular FOV of 9.5\({}^{\circ}\)\(\times\) 28.5\({}^{\circ}\), and can scan the GEO belt of about 3200 square degrees above the observatory in 15 min. The angle observations by the FocusGEO telescope at the Lijiang Observatory of the Yunnan Astronomical Observatory show that the telescope can observe nearly 380 GEO space objects over the station during non-full-moon clear nights. The observational capabilities of FocusGEO surpass 90% for the objects in GEO orbit that are cataloged in the NORAD catalog. With an exposure of 5 s, the telescope can detect GEO objects of 15 magnitude with a precision of about 3\({}^{\prime\prime}\) in azimuth and pitch.