esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Trajectory Tracking Controller for Underwater Vehicles with Shifted Center of Mass Model
## 4 Numerical Simulations
### Model of the Tested Vehicle and Assumptions
Root mean square of the tracking error (RMS), i.e., \(RMS=\sqrt{\frac{1}{t_{f}-t_{0}}\int_{t_{0}}^{t_{f}}\left\|e(t)\right\|^{2}dt}\) where \(\left\|e_{L}(t)\right\|=\sqrt{(x_{e})^{2}+(y_{e})^{2}+(z_{e})^{2}}\) (\(x_{e},y_{e},z_{e}\) mean the linear position errors in the body frame) and \(\left\|e_{A}(t)\right\|=\sqrt{(\theta_{e})^{2}+(\psi_{e})^{2}}\) (\(\theta_{e},\psi_{e}\) mean the angular position errors);
4. Mean kinetic energy \(\text{ mean}(K)\), \(K=\sum_{i=1}^{3}K_{i}=\sum_{i=1}^{5}\frac{1}{2}N_{i}\xi_{i}^{2}\) (\(K\) denotes the kinetic energy). _Controllers' parameter selection method_. The selection of parameters used in the tests was based on the heuristic method described in [41], which worked well when parameters could be ranked according to their role in the controller. This intuitive method of taking into account the vehicles' dynamics can also be used for five DOF vehicles.
## 5 Experimental Evaluation  
### Experimental Setup  
A simulation environment is required to train the RL model to learn the shared control principles. As our framework requires a patient who walks together with a robot, the simulation environment proposed by [PERSON] et al. [43] is updated to include this functionality. The patient walks behind the robot with a gait velocity in x and y directions (\(v_{p,x}\),\(v_{p,y}\)). Initially, the robot and patient are initialized, such that the distance between them is less than 0.25 m to ensure that they are in contact.A random goal, different from the initial starting point, is selected as the target that the robot must navigate the human to. Then, the patient's gait velocity is simulated as follows:  
\[\begin{split} v_{p,x}&=v_{r,x}+\mathcal{N}(0,1)\\ v_{p,y}&=v_{r,y}+\mathcal{N}(0,1)\end{split} \tag{7}\]  
where the notation \(\mathcal{N}(0,1)\) represents a normal distribution with a mean of 0 and standard deviation of 1 meter per second. This distribution is commonly used to model random variables that are centered around 0 and have similar magnitudes. In this case, it could be used to model the random fluctuations in the patient's velocity. The selection of the standard deviation value is based on the limits specified by [PERSON] et al. [46], in that the speed of a robot approaching a human should not be more than 1 m/s. As the robot adjusts to the speed of the patient, the patient's velocity should not be more than 1 m/s to adhere to social norms. Adding a normal noise to the velocity components simulates the real-world scenario where the tracked gait velocity is not in the same direction or magnitude as the robot, but it would be nearly equal. Finally, after each time step, the positions of each human \(i\) in the scene (\(p_{c_{i},x}\),\(p_{c_{i},y}\)), the robot (\(p_{r,x}\),\(p_{r,y}\)), and the patient (\(p_{p,x}\),\(p_{p,y}\)), respectively, are updated based on Equation (8).  
\[\begin{split} p_{c,i}x\big{[}t+1\big{]}&=p_{c,i}x \big{[}t\big{]}+v_{c,i}x\big{[}t\big{]}\Delta t\\ p_{c,i}y\big{[}t+1\big{]}&=p_{c,i}y\big{[}t\big{]}+v_{c,i }y\big{[}t\big{]}\Delta t\\ p_{r,x}\big{[}t+1\big{]}&=p_{r,x}\big{[}t\big{]}+v_{r,x} \big{[}t\big{]}\Delta t\\ p_{r,y}\big{[}t+1\big{]}&=p_{r,y}\big{[}t\big{]}+v_{r,y} \big{[}t\big{]}\Delta t\\ p_{p,x}\big{[}t+1\big{]}&=p_{p,x}\big{[}t\big{]}+v_{p,x }\big{[}t\big{]}\Delta t\\ p_{p,y}\big{[}t+1\big{]}&=p_{p,y}\big{[}t\big{]}+v_{p,y }\big{[}t\big{]}\Delta t\end{split} \tag{8}\]  
Figure 6 shows simulation environments used to train the presented framework for co-navigation with only dynamic obstacles (Model V1) and with both static and dynamic obstacles (Model V2). Nvidia RTX 3090 was utilized for training both V1 and V2 models, with each model requiring 16 hours to complete 1000 epochs of training. In the following subsection, the experimental results are presented and discussed.  
Figure 5: Proposed framework for the patient–robot co-navigation in environments with dynamic and static obstacles (Model V2).
# Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation
## 2 Materials and Methods
### EBR FAPAR Product  
The fraction of absorbed photosynthetically active radiation (FAPAR) is a key variable in describing the exchange of energy, mass, and momentum fluxes between the surface and atmosphere in global agricultural and ecological models [33; 34]. Most global FAPAR products, such as MCD15A2H, SeaWiFs, MERIS and Copernicus Global Land products [35; 36; 37; 38], correspond to FAPAR under black-sky conditions at the satellite overpass time only. A method based on the energy balance residual (EBR) principle was used to retrieve the white-sky and black-sky FAPAR using the MODIS broadband VIS albedo (white-sky and black-sky) product (MCD43A3), the LAI product (MCD15A2H), and the clumping index (CI) product [39]. The EBR FAPAR product [39] can be downloaded freely from the National Earth System Science Data Center ([[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/)), accessed on 19 December 2021). The EBR FAPAR product provides an eight-day compositing period with a spatial resolution of 500 m. The validation results showed R\({}^{2}\) values of 0.917 and 0.909 and RMSE values of 0.088 and 0.012, respectively, for black-sky and white-sky conditions corresponding to VALERI data sets [40].
## 3 Test Process
### Sample Preparation  
The sand used in tests was fine silica sand collected from a site off the east coast of Tianjin with poor grain gradation. The properties of sand are listed in Table 2. The dry sand was placed into the tank by pluviation to keep its relative density at \(I_{\mathrm{D}}\) = 88%; the model pile was pre-embedded with the same target embedment depth, \(z\) = 0.6 m; the total thickness of the sand from the bottom of the tank to the soil surface was equal to 1.4 m. Then, the sand sample was saturated by elevating water with a velocity of 0.25 m/day, which is slow enough to avoid uneven seepage or piping. The final thickness of the sand sample, overlaid by 0.1 m water to keep saturated, was 1.5 m. After the preparation of sand sample, the DPL tests were conducted to make sure of the uniformity of the sand sample; it is DPL tests (light dynamic penetration tests), which can provide reliable data for soil depth less than 8 m [24]. They were conducted to make sure of the uniformity of the sand sample. The introduction of the DPL test can be found in a European Standard [25]. The results of the DPL test were used to double check the consistency and repeatability of soil.  
Figure 5: Sand tank and testing apparatus: (**a**) servo motor which controls vertical speed of model piles; (**b**) servo motor which controls rotational speed of model piles; (**c**) the monitoring part.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 6 Impact of Quantum Money on the Banking System
### Implications for Financial Stability  
By eliminating arbitrary deposit creation, quantum money could introduce several stabilizing effects:  
* Elimination of uncontrolled credit expansion: Since banks could only lend what they actually possess in quantum money, the risk of credit bubbles would be mitigated.
* Stabilization of inflation and preservation of savings: By restricting money creation to authorized institutions, quantum money could prevent excessive monetary expansion, reducing inflationary pressures and ensuring that savings retain their value over time.
* Direct central bank oversight: The money supply would be entirely controlled by the central bank, eliminating the disproportionate influence of commercial banks.
###### Abstract  
The study investigates the effectiveness of a novel Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) technology in enhancing the physical layer security of Multiple-Input Single-Output (MISO) systems. To address the complexity of the security challenge, we examine two distinct phase shift strategies, namely, the coupled phase scheme and the independent phase scheme. The coupled paradigm focuses on optimizing weighted sum secrecy rate (WSSR) through a customized Block Coordinate Descent (BCD) approach integrated with path tracking, aiming to achieve a balanced security enhancement. Furthermore, for the independent phase shift paradigm, an optimization algorithm based on the Concave-Convex Procedure (CCCP) is explored to provide a flexible security solution. The numerical results validate the superior performance of STAR-RIS, confirming its potential as a robust security enhancer for MISO systems.  
coupled phase shifts; simultaneous transmitting and reflecting reconfigurable intelligent surface; weighted sum secrecy rate; physical layer security +
Footnote †: journal: Applied Sciences
# Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image
## 3 Materials and Methods
### Research Area and Data
#### 3.1.2 Aquarius LiDAR Waveform Data  
The Aquarius LiDAR bathymetry system (Optech Company, Canada) is an amphibious measuring system for shallow sea areas which can measure both land information and shallow water information within 15 m. The bathymetry system uses a blue-green laser (532 nm). The specific parameters are shown in Table 1.  
The waveform data obtained by an Aquarius-airborne LiDAR were acquired from the findings of an onsite survey conducted on 19 January 2013. Flight time was approximately 3 h, and total data volume was approximately 55 GB. The information stored in each waveform data included reflected pulse signals, received pulse signals, and GPS time. The left side of the yellow vertical line in Figure 2 represents the transmitting pulse, while the right side represents the receiving pulse. Different targets or regions appear with varying return signal characteristics, mainly in terms of energy value, shape, and quantity of echo signals.
## 5 Social Big Data Analysis Applications
### Frequent Trajectory Mining from Social Media Data  
AUDESOME [7] is a method for extracting the most frequent user trajectories and mobility patterns through a set of Points-of-Interest (PoS) located in a geographical area (e.g., a city). Generally, PoIs refer to tourist attractions, such as monuments, squares or bridges, or to business places, such as airports, shopping malls, or train stations. A trajectory is a sequence of locations visited by a user. For analyzing users' behavior, it is useful to understand whether a user visited or not a PoI. Since information on a PoI is generally limited to an address or to GPS coordinates, it is difficult to match trajectories with Pols. For this reason, it is useful to define the so-called Regions-of-Interest (RoIs) that represent the boundaries of the PoIs' area [24].  
To obtain the most frequent trajectories from a set of geotagged items from social media platforms, the following steps are performed (see Figure 1):  
* _Keyword extraction_ for discovering the main keywords that are used by social media users to identify the Pols that are located in the area. Such keywords are used to group social media posts according to the place they refer to. The intermediate output of this step is the sets of keywords identifying the PoIs.
* _Rol detection_ for extracting the Regions-of-Interest (RoIs) from social media posts that were grouped by keywords. Specifically, the geotagged social media posts referring to a PoI are transformed into a series of geographical points and clustered to define RoIs. The intermediate output of this step is the RoIs calculated for the different PoIs, which can also be easily displayed on a map.
* _Trajectory mining_ for finding the trajectories across RoIs for each user, and thus obtaining the mobility patterns (i.e., the most frequent trajectories).
# A Novel QoS Provisioning Algorithm for Optimal Multicast Routing in WMNs
## 2 Preliminaries
### Description of MPH Algorithm  
In 1980, [PERSON] and [PERSON] put forward the MPH algorithm to help solve the Steiner tree problem [14]. The following are its steps:  
**Step 1:** Choose any node \(d_{1}\) from the set \(\mathbf{D}\) of multicast destination nodes, let \(j=1\), and initialize the generation of tree \(\mathbf{T}_{1}=\{d_{1}\}\), \(\mathbf{V}_{\mathbf{T}_{1}}=\{d_{1}\}\);  
**Step 2:** The cost \(w\left(d_{k},\mathbf{T}_{j-1}\right)\) from \(d_{k}(k\leq q)\) in \(\overline{\mathbf{D}\cap\mathbf{T}_{j-1}}\) to \(\mathbf{T}_{j-1}\) can reach the minimum value by comparing formula (1):  
\[w(d_{j},\mathbf{T}_{j-1})=\min\left\{w(d_{k},\mathbf{T}_{j-1})|d_{k}\in \overline{\mathbf{D}\cap\mathbf{T}_{j-1}}\right\} \tag{1}\]  
By connecting \(d_{j}\) to \(\mathbf{T}_{j-1}\) through the shortest \(\mathrm{PATH}\left(d_{j},\mathbf{T}_{j-1}\right)\), the updated tree can get the result of \(\mathbf{T}_{j}=\mathrm{PATH}\left(d_{j},\mathbf{T}_{j-1}\right)\cup\mathbf{T }_{j-1}\);  
**Step 3:** Until \(i>q\), find the Steiner generating tree; otherwise, let \(j=j+1\), then repeat **Step 2**.
# A 0.18 um CMOS Millimeter Wave Antenna-on-Chip with Artificial Magnetic Conductor Design
## 2 Materials and Methods
### Current Distribution  
Figure 13(a),b show the current distribution of the antenna at 42 GHz and 72 GHz and analyze the structure operation of the antenna. This paper used the GSG probe to feed in, and the current was distributed to the antenna through the feed line. As shown in Figure 13(a), the current entered from the feeder line and was mainly distributed to the lower half of the patch antenna, part of the inverted C branch and L-shaped slot resonance, while the AMC current was more concentrated near the feeder line. In Figure 13(b), the current was distributed mainly to the entire inverted C branch and excited around the transverse and L-shaped slot. The current distribution of the AMC was more concentrated under the antenna body.  
Figure 12: Increase of numbers of AMCs: (**a**) Type 1; (**b**) Type 2; (**c**) Type 3.  
Figure 13: Simulation results of increased numbers of AMC.
###### Abstract
The parameters are as follows: type of fibre coating, extraction mode, separation and detection, agitation method, analyte derivatization, sample volume or weight, pH, ionic strength, water content, organic solvent content, extraction temperature and time and desorption conditions. These parameters are strongly dependent upon the type of sample. Recently SPME-Arrow has been employed in the analysis of volatile compounds in food materials. The analysis has been carried out on salmon, mushroom [24], fish samples [25; 26], soy sauce [27], vinegar [17], milk [28] and distillates [23; 29]. Regarding the wine volatiles, the published work is scarce and includes the work of [PERSON], et al. [30]. The work focuses on optimising SPME-Arrow conditions for the analysis of terpenoids, compounds that could contribute to the mint aromas of red wine bouquet. However, there are virtually no reports on SPME-Arrow analysis of volatile compounds from grape samples. Even though the above-mentioned work focuses on wine volatiles, the optimized method is not usable for grape samples. While grapes are solid matrices, wine is a complex liquid matrix that has been affected by numerous processes during vnification, such as maceration, alcoholic fermentation, or malolactic fermentation. All these processes alter the VOCs and thus do not represent the grape volatile profile. Thus, the aim of this work was to develop and validate SPME-Arrow sampling technique coupled with GC/MS instrument for the analysis of free and bound volatile organic compounds from grape skins.
## 3 Materials and Methods
### Relationship between Non-Linear Parameters and Carbonation Progress  
As shown in Figure 11a, after 120 days of carbonation the non-linear parameters change greatly compared to those without carbonation. The relative non-linear parameters of CII (\(w/c=0.53\)) are larger than that of CI (\(w/c=0.47\)). Figure 11b shows that the non-linear parameters of specimens CI, CII, and CI-W decrease by 20% and 40% on the 14 th and 28 th day of carbonation, since the carbonation during this 28-day period develops fast  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Category** & & **CI** & & & & **CIW** & & & **CII** & \\ \hline \(\beta_{r}\) (\(\times 10^{-2}\)) & 5.124 & 3.985 & 2.995 & 1.910 & 4.283 & 3.209 & 2.470 & 1.701 & 7.861 & 5.657 & 3.766 & 2.181 \\ cd (mm) & 0 & 6.4 & 9.8 & 14.1 & 0 & 6.4 & 9.8 & 14.1 & 0.4 & 9.5 & 13.7 & 19.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The average non-linear parameters with carbonation depth(cd).  
Figure 10: Fundamental amplitudes, harmonic amplitudes and non-linear parameters of CI (dry) and CI-W (water saturation 100%).  
in Figure 7b. Concrete carbonation with a slow rate in the days ranging from 28 th to 120 th results in a decrease in the change rate of relative non-linear parameters. Ultrasonic non-linear parameters are sensitive to changes in the concrete carbonation depth. As shown in Figure 11c, the rate of variation of the relative non-linear parameters changes significantly with the carbonation depth. Meanwhile, CI (\(w/c=0.47\)) and CI-W (\(w/c=0.47\)) do not exhibit much difference in the variation of non-linear parameters (62.73%, 60.25%) due to the level of water content. Figure 12 shows the fitting lines between the concrete carbonation depth and the relative non-linear parameters of the ultrasonic longitudinal wave. The fitting parameters are the average relative non-linear parameters (3 samples per type of concrete) and carbonation depth (10 points per type of concrete) in the study. Subsequently, the linear equation is used in the fitting models. As shown in Figure 12, the dispersion of the relative non-linear parameters of specimens (CI-W \(w/c=0.47\)) is lower than that of specimens (CI \(w/c=0.47\)), while non-linear parameter discreteness of CII (\(w/c=0.53\)) is higher than that of CI (\(w/c=0.47\)). There exists a good correlation between the carbonation depth and the relative non-linear parameters, with \(R^{2}\) of 0.93, 0.96, and 0.89 for CI, CI-W, and CII, respectively. Though the non-linear parameters are relative in this study, the non-linear parameters of longitudinal waves can still be used to characterize the carbonation depth for different types of concrete.
# A Novel Energy-Saving Route Planning Algorithm for Marine Vehicles
## 4 Computational Simulations
### Example 2  
The above experiment is to compare the optimized ant colony algorithm with the traditional ant colony algorithm to verify the practical significance of the energy-saving route. In experiment 2, we choose to calculate the energy consumption of the ship master's customary route at the same starting and ending position, departure time, and the same  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & **Sailing Time** & **Path Planning Distance** & **Energy Consumption** \\ \hline Improved & 9171.0 s & 46,491.7 m & 102,931,597,865.8 J \\ Traditional & 9037.2 s & 45,813.4 m & 106,472,685,239.8 J \\ \hline \hline \end{tabular}
\end{table}
Table 2: Index comparison between improved and traditional ant colony algorithm.  
departure speed. As shown in Figure 6, it is compared with the energy consumption of the optimized ant colony algorithm.  
After the algorithm simulation test, the path planning results of the two algorithms are shown in Figures 6 and 5a. We used the energy consumption of ocean currents to compare the difference between the two, and the results are shown in Table 3.  
The same start and end points were selected during the simulation for both routes. Based on the above environment and the speed of the ship, the ship master's customary route and the energy consumption of the optimized ant colony algorithm were obtained. Using the cubic B-spline curve method, the path points of the optimized ant colony algorithm were subjected to quadratic smoothing. We calculated the energy saving rate of the optimized ant colony algorithm relative to the master's customary route.  
As shown in Figure 5a, the optimized ant colony algorithm considers the influence of the ocean current and selects the area with low resistance to the ocean current relative to the sailing direction. Compared with the ship master's customary routes, this route has a \"detour\" phenomenon, but the ant colony algorithm is optimized to find a path with less energy consumption. Experiments show that the optimized ant colony algorithm's route has an energy saving rate of about 2.709% compared to the customary route.  
It can be seen that the planned path with the low energy consumption has distinct practical significance. Similarly, compared with the ship master's customary paths, the optimal path for the corresponding energy consumption planned by the optimized ant colony algorithm achieved satisfactory results.
## 2 Materials and Methods  
### IVD Donor Materials and Cell Isolation  
Human IVDs were obtained from patients suffering from DISH disease. As controls, traumatic or degenerative IVDs of patients undergoing spinal surgery were used (Table 1). Patients gave their written consent, and procedures were approved by the ethics committee of the Canton of Bern. Tissues were processed within 24 h after surgery. Human IVD tissue was separated into nucleus pulposus (NP), annulus fibrosus (AF) and cartilaginous endplate (CEP) tissue by an experienced surgeon or processed as mixed IVD. Cell isolation from the native extracellular matrix was performed as described earlier [28]. Briefly, tissue was enzymatically digested by 1 h in pronase (Roche, Basel, Switzerland) followed by overnight incubation in collagenase type 2 (Worthington, London, United Kingdom). Subsequent expansion of NP cells (NPC), AF cells (AFC), and CEP cells (CEPC) followed in proliferation medium (low-glucose (Ig/L) Dulbecco's Modified Eagle Medium (LG-DMEM) supplemented with 10% FBS and 1% P/S). IVD cells were obtained from six DISH donors aged from 65 to 84 years (72.3 \(\pm\) 3.1 years) and three \"control\" donors, which were lacking symptoms of DISH, suffering from degenerated or traumatic discs aged from 17 to 66 years (45 \(\pm\) 14.5 years). Analysis of Specific Gene Expression in Human Primary IVD Cells with Quantitative Polymerase Chain Reaction (qPCR)  
Cells were cultured at passage 1-2 (P1-2) in monolayer, and then trypsinised before 2 Mio of cells were lysed directly with TRI. The extraction of total RNA was performed as described in the methods before [28; 30]. Prior qPCR RNA integrity and purity were checked on selected samples by the Experion(tm) Automated Electrophoresis System (Bio-Rad, Reinach, Switzerland). Any possible remaining DNA was digested by using DNase I (AMP-D1, Sigma-Aldrich, Buchs, Switzerland) for 15 min. Reverse transcription was performed by using all-in-one cDNA Synthesis SuperMix (Bimake distributed by LuBio-Science GmbH, Lucerne, Switzerland). Real-time PCR was performed using SYBR Green PCR master mix on a CFX96 touch qPCR system (all from Bio-Rad). Ninety genes (Table 2) were tested with PrimePCR(tm) Pathway plate 96 well (PrimePCR(tm) TGF(b) BMP Signalling Pathway Plus H96, 90 genes, Bio-Rad, US). The qPCR was run using a two-step protocol with an annealing temperature of 60 \({}^{\circ}\)C for 30 s and 95 \({}^{\circ}\)C for 5 s for 40 cycles (according to the recommendations of the manufacturer). Amplicon specificity was ensured by performing a melting curve analysis after cycling. CFX manager software version 3.1 (Bio-Rad) was used for the quantification analysis of normalised relative gene expression. The number of PCR cycles needed for each sample to reach the threshold level was recorded as the _Cq_ value [31]. Values were normalised relative to two reference genes (18S and ACTB). DISH and control cells were compared in scatter plots and RNA transcripts that differed more than four times were indicated in red (up-regulated in DISH patients) or in green (down-regulated in DISH patients).