esa-sceva/satcom-chunk-collection huggingface dataset:
# DAB Signal Preprocessing for Passive Coherent Location
## 2 Materials and Methods
### DAB Signal Structure  
There have been four modes of DAB transmission defined historically [14], but in practice, only Mode I has remained in the current specification (ver. 2.1.1) since January 2017 [15]. The transmitted signal is divided into frames and symbols in the time domain and subcarriers in the frequency domain. The parameters of the DAB signal are summarized in Table 1.  
The DAB transmission employs a Coded Orthogonal Frequency Division Multiplex (COFDM) modulation scheme with a signal bandwidth of 1.536 MHz [16]. The spectrum of the acquired DAB signal is shown in Figure 2.  
In the temporal domain, the signal consists of consecutive frames of constant duration (\(T_{\mathrm{F}}\)). Each frame begins with the Null symbol, which is a period with RF emission turned off that allows the coarse synchronization of the receiver. The occurrence of the Null symbol is clearly visible in the signal's envelope, as depicted in Figure 3.  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**Parameter**} & **Value** \\ \hline Number of transmitted subcarriers & 1536 \\ Subcarrier spacing & 1 kHz \\ Transmission frame duration (\(T_{\mathrm{F}}\)) & 96 ms \\ Symbol duration 1 & 1246 \(\upmu\)s \\ Null symbol duration & 1297 \(\upmu\)s \\ OFDM symbols in frame: 2 & 76 \\ \(\bullet\)  Symbols with PR signal & 1 \\ \(\bullet\)  Symbols with FIC data & 3 \\ \(\bullet\)  Symbols with MSC data & 72 \\ \hline \hline \end{tabular} 1 With guard interval. 2 Without Null symbol.  
\end{table}
Table 1: Main parameters of the DAB signal in Mode I.  
Figure 2: Frequency spectrum of the DAB signal.  
The first symbol transmitted in the frame with the RF carrier turned on is a Phase Reference (PR) symbol. It is intended for the synchronization of the time, frequency, and phase of the following data symbols. Since it consists of OFDM subcarriers transmitted with strictly defined phase offsets, it is a pure deterministic part of the signal, repeated in every DAB frame. The structure of a complete DAB frame is illustrated in Figure 4. After the PR symbol, three Fast Information Channel (FIC) OFDM symbols are transmitted with a significantly reduced data rate for system organization and service control purposes [15] (p. 16). Eventually, the remaining 72 OFDM symbols of the Main Service Channel (MSC) are transmitted with the full data rate containing the compressed digital audio streams and, occasionally, additional data service components [15] (p. 11).
# Design and Development of a Management System for Energy Microgrids Using Linear Programming
## 3 Results
### Impact of the Penalty on Unmet Demand  
Finally, for the analysis of changes in the price of unserved demand, it is necessary to study the percentage of unserved demand with respect to total demand in each of the scenarios previously proposed.  
The model considers untended demand because there are moments within the time horizon in which the generation units are not available for operation, or the installed capacity is not enough to fully meet the demand. In this unattended demand analysis, its cost is calculated based on what has been implemented in [31], multiplying the amount of unattended energy by the cost of the unattended load.  
Figure 5 shows that the objective function presents significant increases for certain levels of unsatisfied demand penalty cost as long as these are less than the cost of the most expensive generation source. However, there is a limit to this value of unattended demand cost, beyond which this quantity reaches its virtual limit and the overall changes in the objective function are smaller.  
It is important to highlight that when the value of unattended demand is equal to that of the most expensive generation source, the maximum demand is met without changing the load configuration in the microgrid, that is, the maximum possible demand is supplied without entailing cost overruns. This can be attributed to the fact that the model does not try to store energy and incur costs associated with this process to cover future demand when it is possible to just leave it unattended. In addition, it avoids the intensive use of the battery system due to the considerations of its self-discharge process.  
It is important to note that since the unmet demand penalty strategy applies in the same way over the entire time horizon, the model seeks to supply demand from all generation and storage sources in the periods immediately following the low energy production. This illustrates that at night time, when the solar generation is not available, the stored energy  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Battery Capacity** & **Demand Supply \%** & **Variation \%** & **Savings \%** \\ \hline \(0.1*d_{max}\) & 0.90\% & – & 36.04 \\ \(0.2*d_{max}\) & 1.54\% & 71.11\% & 36.51 \\ \(0.3*d_{max}\) & 1.97\% & 27.92\% & 36.73 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Impact of battery capacity.  
is used early, preventing the stored energy from dissipating in a self-discharge processes, thus causing unattended demand at times when it is perhaps more important to supply it. For this situation, as with the ability of the model to better cover unattended demand, a segmentation of demand by type of demand and by hours could be considered, thus making the model identify the best times to use stored energy and availability of renewable and non-renewable resources. Thus, a new possible unattended demand penalty strategy should consider different costs according to the priority of the type of demand and the period of time in which it is presented.
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
## 5 Discussion  
### Impact of Power Augmentation Battery on Reliability  
The turboelectric hybrid system includes a turbine engine with a mechanical response rate measured in seconds, whereas the battery and electrical power system have a response rate measured in microseconds. Therefore, an active throttle scheme was utilized that controlled the turbine engine based on the demands of the electrical system [16,17]. A controller reads the voltage of the system and throttles the turbine engine based on whether or not the system is under load. The controller automatically detects when the system requires more or less power and throttles the turbine engine accordingly. However, the disparity in response rates can result in a situation in which electrical load transients reach an equilibrium state before the turbine. This situation can be very hazardous, generating a voltage spike from an unloaded generator that can damage electronics like the ESC, which could result in loss of the aircraft. There are several different strategies to prevent this excess power situation, such as using resistors or some other component to dissipate excess power as heat. Such an approach was used to dissipate excess energy as heat during bench testing. Unfortunately, these resistors added more than two pounds to the system weight, along with additional wires that cluttered the fuselage, making it impractical for flight.  
To avoid a worst-case scenario, the battery for flight tests was sized to absorb a power surge, increasing the battery weight from 21 N (sized only for power required at take-off and landing) to about 34 N and increasing the battery capacity from a 7500 mAh battery to a 12,000 mAh battery [18]. This change increased system reliability at the expense of added weight. If the 7500 mAh minimum-sized battery had been used, the aircraft may have been recoverable in a worst-case scenario, but would have damaged batteries had turbine power been lost during a vertical climb. Based on flight data, the final battery level discharged from an initial 95% charge to around 25%, such that a smaller battery would have been insufficient for the flight. Based on power data, the rear electric motor drew between 5 kW and 6 kW in forward flight, often exceeding the turbine power produced, such that minimal power was directed to recharge the batteries, reinforcing the suggestion to use oversize batteries until the measured flight test performance is obtained.
## 5 Recent Technologies Used in Ammonia Production
### New Ammonia Generation Routes
#### 5.3.3 Nonthermal Plasma Process  
This process involves the use of ionized gas containing charged particles called plasma in order to activate the nitrogen and hydrogen and promote the formation of ammonia [106]. This technology has been shown to be a promising transition to lower temperatures and direct ammonia synthesis. The three main variables and factors that influence the nonthermal plasma-assisted ammonia synthesis process are reactor design, operating parameters, and the catalyst, which plays an important role in production yield. Many efforts have been made to develop the catalysts shown in Table 12 to simplify and improve the efficiency of this technology.  
Figure 23: Operational parameters in the photocatalysis process [105].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**T (\({}^{\circ}\)C)** & **Catalyst** & **Light Radiation** & **Ammonia Yield** & **Ref** \\ \hline
25 & TiO\({}_{2}\)/Au/a-TiO\({}_{2}\) & UV & 13.4 nmol cm\({}^{-2}\) h\({}^{-1}\) & [103] \\ \hline
360 & K/Ru/TiO\({}_{2}\)-xHx & UV & 450–495 & [104] \\ \hline \hline \end{tabular}
\end{table}
Table 11: _Cont._  
Figure 22: Photocatalytic ammonia synthesis [100].  
However, several limitations must still be addressed before these techniques may be deployed in large-scale applications. [PERSON] et al. [99] summarizes the advantages and disadvantages of several ammonia synthesis methods, despite the fact that all synthesis techniques meet considerable challenges. In comparison to the Haber-Bosh process, biotechnological ammonia is still inefficient for large-scale synthesis. Haber-Bosh technology has been developed during a century of industrial applications around the world. To reduce the current highly significant greenhouse gas emissions associated with the fertilizer industry, it should shift ammonia production to a carbon-neutral process that can be accomplished at any scale and in any part of the world.
## 2 Proposal of a Position-Dynamic Binaural Synthesis System
### BRIR Synthesis
#### 2.2.1 Constant Reverberation  
This first approach is based on the simple idea to keep the reverberation constant throughout the different positions. There have been several earlier studies, like that in [31], indicating that around 50 ms after the direct sound, at least in small rooms the reverberation can be kept constant for direction-dependent reproduction. It was shown that at least for an approaching motion towards a virtual sound source a simple adjustment of energy of the direct sound according the distance to the sound source was sufficient to achieve a plausible reproduction. Moreover, it was perceived as plausible as the original set of BRIRs measured along the walking line [17; 32].  
The basic idea to keep the reverberant part constant is not new, but due to its simplicity a very interesting one. However, it is likely that it has its limitations and will deliver perceptually satisfying results only under certain conditions. For this reason, we started to investigate this approach for the given application scenario of synthesizing BRIRs for an AAR-system with 6-DoF. So far, two studies have been conducted for the case of walking towards and away from a virtual loudspeaker in two different rooms with a similar size but quite different reverberation times (RT60 of 0.27 s and 1.0 s). In both cases, using the reverberation measured at one position of a considered walking line, keeping the reverberant part of the BRIRs constant over the different positions did not significantly reduce the plausibility. Two different cases of direct sound were taken into account. In one version, the direct sound originally measured at all the different positions was used. The other version was built on the measurements from one specific position in the room. BRIRs  
Figure 4: Examples of nonuniform grids for a quadratic room and frontal sound source (x); f.l.t.r.: Grid 1 \(\alpha=5^{\circ}\), \(d=0.25\); Grid 2 \(\alpha=10^{\circ}\), \(d=0.5\); Grid 3 \(\alpha=15^{\circ}\); \(d=0.75\); and Grid 4 \(\alpha=20^{\circ}\), \(d=1.0\).  
for other positions were created by simply adjusting the level of the direct sound. Both realizations were perceived as plausible as the original fully measured set of BRIRs. More details about the experiments are provided in Section 3.2.1.  
In both experiments, the translation line with a length of 2 m was located in front of the loudspeaker. Therefore, it remains an open question whether this very simple approach still creates convincing results for the cases of walking past and behind a virtual sound source. For these cases, the source directivity has to be considered when modeling the direct sound. Moreover, there is still a lack of knowledge about the perception of room acoustical details and their relative changes for the cases of low direct sound energy.
## 2 Material and Methods
### In Vitro Metabolic Stability Study Using Liver Microsomes  
In vitro metabolic stability study was performed to predict the in vivo metabolism of the drug [25]. Pooled HLMs are a suitable source of enzymes for examining in vitro human metabolism as they contain the major drug metabolizing enzymes was used to measure the metabolic activity of 5-AIQ. Microsomal incubation was performed in 0.1 M phosphate buffer solution (PBS) placed in a shaking water bath as described in our previous method [26; 27]. Briefly, 5 \(\upmu\)L of 5-AIQ (50 \(\upmu\)g/mL) solution was transferred to 1.5 mL Eppendorf tube. Then, 460 \(\upmu\)L of PBS (pH 7.6) was added. For NADPH generating process, 25 \(\upmu\)L of freshly prepared 20 mM NADPH was added into mixture. All the samples were placed for incubation in a shaking water bath maintained at 37 \({}^{\circ}\)C. After 5 min, 10 \(\upmu\)L of microsomes (0.5 mg/mL) was transferred into each tube for reaction initiation and the incubation was continued for up to 45 min. Reaction was terminated at different time intervals (0, 2.5, 5, 10, 20, 30 and 45 min) by addition of 250 \(\upmu\)L of cold ACN containing IS (500 \(\upmu\)g/mL). After incubation termination, samples were properly vortex-mixed followed by cold centrifugation at 8500\(\times\)\(g\) for 8 min and 5 \(\upmu\)L was injected for UPLC-MS/MS analysis. The 5 \(\upmu\)L of the sample was transferred to HPLC vials for analysis by using the developed assay. A graph was plotted between the percentage of the 5-AIQ percent remaining versus time. From the plot, time points in the linear range were chosen to plot the natural logarithm of percent parent compound remaining versus time. The slope of the linear part gives the rate constant for the disappearance of the 5-AIQ that was used for in vitro half-life (T\({}^{1}\)/2) and intrinsic clearance (Clint) calculation.
## 3 Valve Segmentation: Binary Thresholding  
There are several works about diatom detection mainly related to the above-mentioned ADIAC project. It is out of the scope of this paper to present all of the segmentation methods; for further details, the reader is referred to the reviews mentioned in Section 1[12; 13; 18]. In this work, we present an automatic method used to do an initial quick segmentation, which required visual supervision afterward to include only the correct segmented diatoms. Some of the images have been acquired with low contrast and background noise, which produces a poor segmentation in terms of valve overlapping with other structures. That means visual supervision is needed for discarding segmentation errors. The valve is the most significant region of the diatoms where structural differences can be distinguished. Therefore, the segmentation process should accurately extract such a region for extracting relevant features. A proper segmentation of the valve is expected to affect textural, frequential and statistical descriptors. This segmentation is done here by means of a binary thresholding where the segmented region is the binary masks where descriptors must be computed.  
The process to obtain the binary masks consists of four steps:  
1. Binary thresholding: automatic segmentation based on [PERSON]'s thresholding.
2. Maximum area: calculation of the largest region (area).
3. Hole filling: interior holes are filled if present, using mathematical morphology operators.
4. Segmentation: the ROI is cropped with the coordinates of the bounding-box of the largest area (Step 2).  
Due to the presence of debris and overlapping with different diatom taxa (or debris), the segmentation was afterward manually checked to discard bad segmented diatoms or finish correction; see some examples of the segmented diatoms and their masks in Figure 4.  
Figure 3: Different views and sizes of the same species: (**a**) _Gomphonema insigniforme_; (**b**) _Nitzschia fossilis_; and (**c**) _Rhoicosphenia abbreviata_.
## 5 Discussion and Conclusions
Neglecting changes in the center of gravity results in differences of 11.06% in velocity and 68.6% in altitude. Similarly, neglecting changes in the moment of inertia leads to differences of 12.47% in velocity and 20.9% in altitude. Assuming constant aerodynamic coefficients results in differences of 16.5% in velocity and 108.7% in altitude. In all these cases, the flight path angle deviates significantly from zero. Moreover, when all three cases are neglected, substantial differences of 37.87% in velocity and 226.2% in altitude are observed, with the flight path angle being notably different from zero. When conducting further analysis of the results for the original solid last stage, significant differences emerge when specific parameters are neglected. Neglecting changes in the center of gravity yields differences of 10.6% in velocity and 48.8% in altitude. Similarly, overlooking changes in moment of inertia results in differences of 15.8% in velocity and 23.2% in altitude. Assuming constant aerodynamic coefficients further produces differences of 19.5% in velocity and 78.9% in altitude. In all these cases, a noticeable trend is the significant deviation of the flight path angle from zero. Furthermore, neglecting all three cases leads to substantial differences of 43.7% in velocity and 206.2% in altitude, with the flight path angle notably diverging from zero. This highlights the critical impact of comprehensive modeling on the optimization process, whereas simulations without comprehensive modeling achieve the desired trajectory using different design parameters. Consequently, it is seen that the performance of the launch vehicle is enhanced by utilizing a hybrid propulsion system, which offers lower production and manufacturing costs compared to other propulsion systems. The major contribution of the present work is considered to be the study of HRMs through the application of subsystems. Our future goal is to apply different parameters to the HRM system to better understand the relationship between environmental changes and HRMs. Conceptualization, U.A. and A.R.A. ; Methodology, U.A. and U.D.G. ; Investigation, U.A., A.R.A. and U.D.G. ; Writing--original draft, U.A. ; Writing--review & editing, A.R.A. and U.D.G. ; Supervision, A.R.A. ; Project administration, A.R.A. All authors have read and agreed to the published version of the manuscript. This research has been supported by the ITU Coordinatorship of Scientific Research Projects, grant number MDK-2019-41839. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. **Conflicts of Interest:** Author [PERSON] was employed by the company Aselsan Inc. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Tea Sample Processing  
Fresh tea leaves with one bud and two leaves were harvested from the Lj43 cultivar (_Camellia sinensis var. sinensis cv. Lj43_) in modern tea plantations in Pukou County at 32.101216\({}^{\circ}\) N, 118.657506\({}^{\circ}\) E (Nanjing City, Jiangsu Province, China) in July 2023. The tea shoots were subjected to five different withering treatments: indoor natural withering (CK), shaking (S), yellow light plus shaking (YLS), high temperature plus shaking (HTS), and high temperature with yellow light plus shaking (HYS). For the CK treatment, tea leaves were withered in a withering machine (LCWD-5) for 12 h at 25 \({}^{\circ}\)C and 60% relative humidity (RH) with natural light intensity below 200 lux. For the S treatment, after 12 h of indoor natural withering, the tea leaves were shaken three times using a shaker machine (6 CWY-90) for 10 min each, with 15 min intervals between shakes. For the other treatments, tea leaves were withered under yellow-light irradiation (YLS, 25 \({}^{\circ}\)C, 60% RH, 844-864 lux), at a high room temperature (HTS, 32 \({}^{\circ}\)C, 60% RH, <200 lux), or at a high room temperature with yellow-light irradiation (HYS, 32 \({}^{\circ}\)C, 60% RH, 844-864 lux) before shaking. The shaking parameters were consistent with those of the S treatment. During the 12 h withering process, the leaves were stirred every hour to ensure uniform light exposure. After five different treatments, the tea leaves underwent further withering by fan in withering machine (LCWD-5) until their moisture content decreased to 60.0 \(\pm\) 1.0%, respectively. Subsequently, half of the withered leaves were frozen in liquid nitrogen and stored at \(-\)80 \({}^{\circ}\)C for subsequent experiments, while the other half were processed into black tea through rolling and cutting (2 times), fermentation (30 \({}^{\circ}\)C, 90% RH for 3 h), and drying (110 \({}^{\circ}\)C for 30 min, then 80 \({}^{\circ}\)C until the moisture content reached approximately 6%). Each treatment was repeated three times, and the experimental processing flowchart is shown in Figure 1.  
Figure 1: The experimental processing flowchart of black tea.
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 3 Design of Vehicle State Estimator Based on IMM-MCCKF
### Derivation of the Cubature Kalman Filter Algorithm
#### 3.3.2. Update  
The cubature points are calculated using Equation (27).  
\[X_{(i,k|k-1)}=chol\Big{(}P_{(i|k-1)}\Big{)}\xi_{i}+\hat{x}_{(k|k-1)} \tag{27}\]  
The propagated cubature points of the nonlinear state equation are calculated using Equation (28).  
\[Z^{*}_{(i,k|k-1)}=h\Big{(}X_{(i,k|k-1)},u_{k}\Big{)} \tag{28}\]  
The measured state is estimated using Equation (29).  
\[\hat{z}_{(k|k-1)}=\sum_{i=1}^{N}\omega_{i}\ Z^{*}_{(i,k|k-1)} \tag{29}\]  
The innovation error covariance matrix is estimated using Equation (30).  
\[P^{zz}_{(k|k-1)}=\sum_{i=1}^{N}\omega_{i}\ Z^{*}_{(k|k-1)}\Big{(}Z^{*}_{(k|k- 1)}\Big{)}^{T}-\hat{z}_{(k|k-1)}\hat{z}^{T}_{(k|k-1)}+R_{k} \tag{30}\]  
The cross-covariance matrix is estimated using Equation (31).  
\[P^{xz}_{(k|k-1)}=\sum_{i=1}^{N}\omega_{i}X_{(i,k|k)}\Big{(}\ Z^{*}_{(i,k|k-1)} \Big{)}^{T}-\hat{x}_{(k|k-1)}\hat{z}^{T}_{(k|k-1)} \tag{31}\]The Kalman gain, updated state, and the error covariance matrix are estimated using Equations (32)-(34).  
\[K_{k} =P_{(k|k-1)}^{xz}\Big{(}P_{(k|k-1)}^{zz}\Big{)}^{-1} \tag{32}\] \[\hat{x}_{(k|k)} =\hat{x}_{(k|k-1)}+K_{k}\Big{(}z_{k}-\hat{z}_{(k|k-1)}\Big{)}\] (33) \[P_{(k|k)} =P_{(k|k-1)}-K_{k}P_{(k|k-1)}^{zz}K_{k}^{T} \tag{34}\]