esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation
### CASE 2 (Composite Comparison)  
To further illustrate the effectiveness of the whole proposed control strategy, we would show all-sided simulation results from Section 5.1.2. Moreover, a single backstepping controller without any observer is also conducted on quadrotor UAV manipulator system for comparison. Three-axis components of disturbance torque \(d_{r}=\left[\begin{array}{cc}d_{r\varphi}&d_{r\theta}&d_{r\varphi}\end{array} \right]^{T}\) are equal and shown in Figure 10, which are applied on the UAV simultaneously. The result curves of both attitude and angular rate are shown in Figures 11-16.  
Figure 10: Estimation results from ESO2, STESO, and FASTESO with scalar \(a=5\).  
Figure 9: Observer gain of FASTESO during adaptation process with scalar \(a=20\).  
Figure 11: Results of roll angle with unknown external torque disturbance under backstepping controller combined with nothing, ESO2, STESO and FASTESO.  
Figure 12: Results of angular rate \(\omega_{x}\) with unknown external torque disturbance under backstepping controller combined with nothing, ESO2, STESO and FASTESO.  
Figure 13: Results of pitch angle with unknown external torque disturbance under backstepping controller combined with nothing, ESO2, STESO and FASTESO.  
Figure 16: Results of angular rate \(\omega_{z}\) with unknown external torque disturbance under backstepping controller combined with nothing, ESO2, STESO and FASTESO.  
Figure 14: Results of angular rate \(\omega_{y}\) with unknown external torque disturbance under backstepping controller combined with nothing, ESO2, STESO and FASTESO.  
Figure 15: Results of yaw angle with unknown external torque disturbance under backstepping controller combined with nothing, ESO2, STESO and FASTESO.  
We could easily find that, without an observer, the single BAC has biggest attitude offset and angular rate fluctuation, which proves its bad robustness under disturbances. Specifically, the attitude curves even follows the applied torque disturbance well and angular rate has a dramatic change at special points. As for BAC+ESO2, although the estimation value follows the disturbance well to some extent, it has a performance of drastic change on both attitude and angular rate at peak point owing to the over estimation near the peak. As for BAC+STESO, in spite of the good performance of STESO on following the disturbance, it has a terrible chattering phenomenon and leads to a mess on UAV system. Compared to other situations, the UAV under BAC+FASTESO has the best performance on both attitude offset and angular rate fluctuation with varied torque disturbance.
## 5 Conclusions and Outlook  
Coded excitation has the potential to reduce the ambiguity of signals used for ultrasound time-of-flight measurements. While not every signal sequence provided a large reduction in the side maxima, selected sequences such as the empiric pulse sequences (Figure 5) resulted in a reduction of the absolute side maximum by 37 percentage points and 64 percentage points for the positive side maximum.  
The definition of a general optimal stimulation sequence is not possible. Major components of the transmission chain are the piezoelectric ultrasonic transducer and the testing object itself. The usage of the same sequence in other testing object of the same material and the same transducer, but with another thickness, is possible. However, changing the specimen geometry to a more complex form or changing the transducer would require a completely new calculation of an optimized stimulation sequence. More amplitude levels in the stimulation signal would enable the use of different forces on the transducer without the need for a huge bandwidth, predefine the desired echo much more precisely and calculate the required stimulation signal. Further projects will investigate the replacement of the transfer function with physical models of the transducer and the material. This could offer added value for the automated estimation of an optimized stimulation sequence and would enable a quick estimation on the effect of changing different components in the experimental system. Furthermore, other transmitter technologies could result in further potential for optimization through coded excitation.  
Conceptualization, H.T., M.M.B. and S.C.L.F.; Formal analysis, M.S.; Funding acquisition, M.M.B. and S.C.L.F.; Investigation, M.S. and H.T.; Project administration, S.C.L.F.; Supervision, H.T.; Visualization, M.S.; Writing--original draft, M.S.; Writing--review and editing, M.M.B. and S.C.L.F. All authors have read and agreed to the published version of the manuscript.  
This work was partially funded by the Fraunhofer Internal Programs under Grant No. Attract 025-601314 as well as the Federal Ministry for Economic Affairs and Energy on the basis of a decision by the German Bundestag as part of the IGF Project No. 19.671N.  
Not applicable.  
The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
# A Multi-Objective Optimization of Secure Pull Manufacturing Systems
## 2 Literature Review
### e-Kanban Card-Based Pull Control Systems  
Different mechanisms have been proposed to powerfully change the quantity of cards available for use and to improve the presentation of card-based force control frameworks, in conditions confronting vacillations, in client interests. This theme has received significant consideration from numerous analysts, and it still establishes an open examination zone [22; 23].  
A few versatile card-based pull control systems have been intended to lessen WIP, while keeping up great consumer loyalty, in conditions with fluctuating client interests and irregular creation times [24]. In 1998, a statistical throughput control (STC) way was constructed to deal with and adjust the quantity of cards in a ConWIP framework, to restrict WIP, as a reaction to stochastic changes in customer demand [25]. To do that, they considered an objective throughput rate and checked the normal throughput of the framework. In the event that the circumstance is considered as wild (as per an objective throughput rate to be accomplished), at that point, a card is added or recovered, and the information of the calculation is re-introduced. Changes in the quantity of cards are permitted solely after a predefined number of prepared items. After that, in 2006, the authors built up a card controlling strategy for a ConWIP framework [26]. Their strategy continually screens the throughput rate to check whether it is under a specific creation target or above it. The extra cards are added to, or deducted from, the creation line, individually, to accomplish an objective throughput rate esteem. As the framework execution can be enormously influenced by the boundaries utilized for changes, similar creators applied a reaction surface system to decide these boundaries [27]. In 2009, a card controlling methodology was introduced, which can change the quantity of cards to guarantee an alluring throughput rate [28]. An objective throughput esteem is set; the distinction among it, and the continuous throughput esteem, is then used to change the quantity of cards. Figure 1 shows the representation of Kanban card. It is shown that the Kanban card consists of all the data required for item transfer, which include supplier name, supplier ID, the unit of the box, customer location, Kanban ID, item ID, quantity, routing, and quality approval.
# Optics Based Label-Free Techniques and Applications in Brain Monitoring
## 5 OCT in Brain Imaging
### OCT Studies on the Brain  
Within the last few years, OCT has been used for in vivo imaging and mapping of cortical tissue in relation to evoked neural activity or peripheral sensory stimulation. Modulation of the optical refractive index by neuronal activity during activation of brain tissue coincides with changes in surface reflectivity [146].  
OCT based angiography (OCTA), the first demonstration of the Fourier Domain-OCT (FD-OCT) based OCTA technique, was made in 2007 on a rodent cerebrovascular model using optical coherence tomography which was based on microangiography (OMAG) technique. With light source at 1300 nm central wavelength [148], the OMAG technique could non-invasively visualize pial vessels, but only roughly capillaries due to the sensitivity limitation. With the improvement of the system and the use of high-pass filtering of time series of OCT signals, in vivo imaging became available. Currently, longitudinal monitoring of vascular responses to stimuli in the cortex is possible [148]. In addition, changes in vessel diameters and microcirculation responses in the cerebral cortex during hypercapnia through a cranial window during hypoxia can be performed [148]. OCTA can be also used to quantify cerebral blood flow (CBF) in rat models. In addition to monitoring and measuring vasculature in the brain, OCTA techniques were able to monitor vasodilynamics after stroke, detect ischemia regions, and capture the capillary response to a traumatic brain injury (TBI) in the cortex [148].  
[PERSON] et al. [149] developed a method to monitor the cerebral metabolic rate of oxygen (CMRO2) by combined Doppler and spectroscopic OCT. Measurements were performed using a single visible light spectral/Fourier domain OCT microscope, with Doppler and spectroscopic capabilities, through a thinned-skull cranial window in the mouse brain. However, the validation of the experiments does not prove the accuracy of CMRO2 measurements. Furthermore, [PERSON] et al. [150] developed noninvasive, in vivo imaging of subcortical brain regions in mice. Imaging hippocampal tissue architecture and white matter microvasculature were demonstrated in vivo through thinned-skull, glass coverslip-reinforced cranial windows in mice. This imaging platform also includes applications to monitor disease progression and pathophysiology in rodent models of AD and subcortical dementias, including vascular dementia.  
[PERSON] et al. [151] demonstrated the feasibility of non-invasive deep brain vascular imaging and reported the use of microvascular imaging in the hippocampal formation of mice using a 1.7 \(\upmu\)m swept-source OCT system. The imaging results demonstrated that the proposed system can visualize blood flow at different locations of the hippocampus corresponding with deep brain areas.
# Analysis of Liquefaction in Tailings Deposits by Fem Modeling of Underained Cyclic Triaxial
## 3 Experimental Tests  
Triaxial tests were performed under a controlled load without drainage according to the ASTM D3999 standard (2011) [42]. Cylindrical specimens 50 mm in diameter (D) and 100 mm in height (H) were used for the silt, while a specimen 70 mm in diameter and 140 mm in height (H) was used for the silty sand. The materials were tested at various cyclic stress ratios (CSR). To obtain the optimal experimental representation, frequencies of 0.1 and 0.5 Hz were used for the silt and sand, respectively.  
Table 1 provides details of the tests performed, including the number of cycles to liquefaction (N) according to the criterion adopted (\(R_{u}>0.8\)). Based on these data, the cyclic stress ratio was plotted against N, as shown in Figure 2.  
Figure 1: Particle size distributions of the silt (ML) and sand (SM).  
The results show that, for the same CSR value, the parameter N is considerably higher in sand than in silt. For example, for a CSR of 0.10, sand (CTX 4) requires 342 cycles to liquefy, while silt (CTX 1) requires only 55 cycles. This difference in liquefaction resistance can be attributed to the characteristics of the materials; that is, the sand undergoes a controlled compaction process during the formation of the perimeter walls of the tailings dam, whereas the silt is deposited in the tailings dam basin without experiencing the same compaction process. On the other hand, Figure 3 presents the results of the experimental test in terms of the relationship between the excess pore pressure ratio (\(R_{u}\)) and the number of cycles (\(N_{c}\)), which was used to establish the liquefaction criterion. A total of six tests were performed, all at the same effective confining stress (\(\sigma^{\prime}{}_{3}\)) of 100 kPa, by varying the applied deviatoric stress (\(\sigma_{d}\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & \(\sigma_{d}\)** kPa** & \(\sigma^{\prime}{}_{3}\)** kPa** & **CSR (\(\sigma_{d}\)/2\(\sigma^{\prime}{}_{3}\))** &
\begin{tabular}{c} **Number of Cycles to** \\ **Liquefaction (N)** \\ \end{tabular} \\ \hline Silt (CTX 1) & 20 & 100 & 0.100 & 55 \\ Silt (CTX 2) & 23 & 100 & 0.115 & 23 \\ Silt (CTX 3) & 27 & 100 & 0.135 & 11 \\ Sand (CTX 4) & 20 & 100 & 0.100 & 342 \\ Sand (CTX 5) & 32 & 100 & 0.160 & 161 \\ Sand (CTX 6) & 42 & 100 & 0.210 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the cyclic triaxial tests performed.  
Figure 3: Experimental results showing the excess pore pressure (\(R_{u}\)) versus the number of cycles (\(N_{c}\)) for CTX tests performed on (**a**) silt and (**b**) silty sand.  
Figure 2: The cyclic stress ratio (CSR) versus the number of cycles to liquefaction for silt and silty sand tailings.  
The excess pore pressure ratio reached values greater than 0.8 in all tests; therefore, \(R_{u}>0.8\) was defined as a liquefaction criterion. These findings are consistent with previous studies that showed that \(R_{u}\) was a reliable parameter for predicting liquefaction [22; 24; 43].
# Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry
## 4 Datasets
### Model Data  
To validate our ASL trend estimation method, we conduct an additional examination using standard products that have been used in earlier studies. Hence, in addition to instantaneous along-track SA and TG data, three monthly gridded model data were employed to estimate the trends which are discussed in Section 5.4.3. These datasets are as follows:  
1. Global SA gridded SLA ([[https://doi.org/10.48670/moi-00148](https://doi.org/10.48670/moi-00148)]([https://doi.org/10.48670/moi-00148](https://doi.org/10.48670/moi-00148)) (accessed on 9 October 2023)) computed with respect to a twenty-year 1993-2012 mean from the Copernicus Marine Data Store (www.data.marine.copernicus.eu (accessed on 9 October 2023)): This SLA (denoted as global gridded SLA in Section 5.4.3) is estimated by Optimal Interpolation, merging the Level-3 along-track measurement from the different available SA data. This product is processed by the DUACS (Data Unification and Altimeter Combination System) multi-mission altimeter data processing system.
2. The Baltic Sea Physical Reanalysis product ([[https://doi.org/10.48670/moi-00013](https://doi.org/10.48670/moi-00013)]([https://doi.org/10.48670/moi-00013](https://doi.org/10.48670/moi-00013)) (accessed on 15 May 2023), which provides a reanalysis of the physical conditions of the whole Baltic Sea area: The product (denoted by Baltic Sea Nemo, gridded in Section 5.4.3) is produced by using the ice-ocean model system Nemo. The data are available at the native model resolution (1-nautical-mile horizontal resolution, 56 vertical layers).  
Figure 6: Annual DT variation of TG observations (hourly data) before (black, RSL) and after (blue, ASL) application of the VLM correction for six selected TG stations during 1995–2022. The bars represent the sea-level variations (minimum and maximum) in each year. The dots represent the DT annual average.  
Baltic + SEAL gridded SA data over the Baltic Sea region: This product provides SSH after the multi-mission cross-calibration and outlier detection [29]. In this product, the SA observations are interpolated on an unstructured triangular grid (i.e., geodesic polyhedron) with a spatial resolution of 6-7 km.  
It is important to consider that these models may have challenges related to vertical data incoherency (e.g., [33; 50; 51]). However, it needs to be emphasized that despite potential discrepancies in the vertical data, these issues are not anticipated to significantly impact the outcomes of sea-level trend estimations.
## 3 System Model and Problem Formulation
### System Model
#### 3.1.2 Communication Model
Finally, the overhead consumption for the communication can be computed as follows:  
\[T_{i}^{Comm}=T_{i}^{U}+\zeta y_{i,j+1} \tag{4}\]  
\[E_{i}^{Comm}=p_{i}T_{i}^{U} \tag{5}\]  
where \(\zeta\) denotes the prorogation delay between the cloud and edge server, and \(T_{i}^{U}\) denotes the upload time that can be expressed as:  
\[T_{i}^{U}=\big{[}\sum_{j=0}^{J+1}\frac{\alpha_{i}}{R_{i,j}}y_{i,j}\big{]} \tag{6}\]
## 7 Case Study
### Effectiveness of the Proposed Sensor Deployment Scheme
#### 7.3.2 Comparison of Hexagonal and Random Sensor Deployments  
Our optimal hexagonal deployment scheme and the random one are compared under retransmission via simulation. The simulation parameters are the same as in Section 7.3.1. As \(L=9\), the number of sensors in the hexagonal deployed WSN is \(N=270\). In the WSN under the random deployment scheme, the only difference is that 270 sensors are randomly deployed in the target area.  
In the comparison simulation, the WSN lifetime is defined as the time period between the starting time and the time when the coverage falls below 85%. We do not consider 100% coverage since it is calculated based on the pixel method, and there is some pixel error in the coverage rate calculation. The simulation is repeated for 1000 times, and the results are presented in Table 11.  
From Table 11, one can see that the lifetime and the success transmission rate of hexagonal deployed WSN are 1.022 and 3.012 times of that of random deployed WSN, respectively. Apparently, the lifetime of the sensor deployment scheme proposed in this work is better than that of a random deployment and gives a much higher success transmission rate.
## 4 Effectiveness of Data Imputation  
This section is aimed at assessing whether and how different imputation strategies mitigate the impact of missing data. It is noted that the decision to impute missing data involves a trade-off between recovering missing information and potentially introducing noise.
## 3 Results and Discussion
### Tests with the Laboratory Specimens
#### 3.2.2 Uncracked Specimens Constantly Submerged in a Solution of NaCl (35 g/L)  
In Figure 12, the chloride-affected areas of the tested specimens from each mixture are shown. Initially, it can be observed that a smaller affected area was found in the mixtures containing SAPs in comparison to WR2 and slightly smaller when compared to WR1, especially in the specimen containing only SAP1.  
The resulting profiles are shown in Figure 13. In a general overview, the ingress of chlorides in the middle part is similar for all mixtures, which provides evidence that the inclusion of SAPs with extra water did not increase the permeability of the mixtures, which would have a negative effect in this case by increasing the chloride ingress. Additionally, by considering the total length of the tested surface, it becomes clear that, indeed, the mixtures containing SAP1 were less affected by the chloride ingress, especially compared to WR2. Analysis of the relative affected areas also provided the following values: 21.2%, 28.9%, 18.7%, 27.2%, and 19.9% for WR1, WR2, WS1, WS2, and WSC, respectively.  
It can also be observed that in the first 20 mm on one or both sides of the specimens, a deeper chloride penetration was identified. There are two possibilities that could explain this result: (1) failure in the coating of the specimens, or (2) the wall effect during the casting of the specimens. Given that all specimens were coated using the same material and procedure, and that they did not present the same penetration depth (WR2 for instance has a deeper chloride penetration on the edges in comparison to the other mixtures), the second theory seems to be more plausible, but a combination of both effects to some extent should not be discarded. In fact, the wall effect during casting causes a concentration of cement paste at the contact surface with the molds. The cement paste is the most porous phase of the concrete, thus it is more susceptible to chloride penetration. As was already observed for the specimens evaluated for carbonation, in the mixtures containing SAP1, a densification of the cement matrix due to internal curing and further hydration could  
Figure 12: Chloride profile visualized by contrast with silver nitrate. The whitish-colored area indicates the presence of chlorides.  
also be responsible for the lower chloride ingress when compared to both references. In addition, a second hypothesis considering the binding of chloride ions by the SAPs should not be discarded. It is widely known that upon contact with ionic solutions, there is an ion exchange between the SAP particles and the solution, which in most cases results in the entrapping of ions by the SAPs, for example, calcium ions present in the cement pore solution. Regarding salts, a similar effect has been reported in [38, 39], where the authors state that hydrophilic groups inside the polymer structure can bind salt ions easily in a mechanism quite similar to the complexation observed with the calcium ions in the cement pore solution.
## 2 Materials and Methods
### Data Source  
#### 2.2.1 Meteorological Data  
The meteorological datasets adopted in this research were the newest routine meteorological observation data from 545 weather stations in the middle and lower reaches of the Yangtze River during 1961-2020, which were provided by the China Integrated Meteorological Information Sharing System (CIMISS) of the China Meteorological Administration (CMA). The datasets include daily average air temperature (Tem, \({}^{\circ}\)C) and precipitation (PPT, mm). The annual (monthly) mean Tem and annual (monthly) mean accumulative PPT for analysis were obtained by averaging and summing the daily data of all meteorological stations in the MLRYR to the annual (monthly) time scale. Quality control of the datasets has already been conducted by the National Meteorological Information Center (NMIC) of CMA.
# Gaussian Quantum Trajectories for the Variational Simulation of Open Quantum-Optical Systems
## Quantum Trajectories for Expectation Values and Gaussianity  
### Quantum Trajectories for Expectation Values  
For the remainder of this work, we assume for simplicity systems where the interaction with the environment is entirely characterized by their Markovian dissipation (photon-leaking towards empty space) at rate \(\gamma\). At the level of the master equation for the ensemble density matrix \(\hat{\rho}\), such a system with Hamiltonian \(\hat{\mathrm{H}}\) is described by:  
\[\partial_{t}\hat{\rho}=-i\big{[}\hat{\mathrm{H}},\hat{\rho}\big{]}+\frac{ \gamma}{2}\left(2\,\hat{\mathrm{a}}\,\hat{\rho}\,\hat{\mathrm{a}}^{\dagger}- \hat{\mathrm{a}}^{\dagger}\,\hat{\mathrm{a}}\,\hat{\rho}-\hat{\rho}\,\hat{ \mathrm{a}}^{\dagger}\,\hat{\mathrm{a}}\right), \tag{1}\]  
with \(\hat{\mathrm{a}}(\hat{\mathrm{a}}^{\dagger})\) the photonic annihilation (creation) operators.