esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Proximate Composition of Medicinal Plants  
The proximate composition of the analyzed medicinal plants is presented in Table 2. The results highlight that the humidity of the samples, dried at room temperature, varies between 6 and 12%. The average moisture content in medicinal samples was 8.57%; theminimum value was recorded in HP (6.19%), and the maximum value was detected in EP (12.45%).  
The ash content varied significantly from 3.57% HP to 12.82% MO, with an average value of 8.035%. No significant differences were recorded (\(p<0.05\)) between the pairs: MP/MO, LA/AM/SO1, TS/OV.  
The lipid content varied between 0.43 mg/100 g in HO and 3.84 mg/100 g in SO2. The content of seeds in SO2 samples is responsible for the highest lipid value.  
The protein content of the analyzed medicinal plants varies between 8.75 and 21.34%. The minimum value of the protein content was recorded in the case of species TS and the maximum in the case of species MO. According to the \(t\)-test (\(p<0.05\)), no significant differences were recorded between the MP/HP, LA/SO2/SO1, and LA/OV.  
The proximate composition of the analyzed plants is consistent with data reported in the specialized literature. [PERSON] et al. (2013) revealed values between 5.55-14.22% for moisture, 2.32-21.15% for ash, 4.51-12.10% proteins, 2.52-16.49% lipids, and 32.35-92.65% carbohydrates in different medicinal plants ([PERSON] et al., 2013).  
Similar results regarding the proximate composition were reported in the literature for medicinal plants from the Lamiaceae family from the Banat region, Romania. Thus, the protein content varied between 4.81-8.25%, lipid 5.64-8.96%, mineral substances, expressed as ash, between 7.44-10.69%, and the carbohydrate content between 64.54-74.02%, for an average moisture content of 8.79 % ([PERSON] et al., 2013).  
Another study reported the protein content in the medicinal plant as MP between 2.19 and 7.69%, lipid between 0.5 and 5% depending on the humidity content of the sample, and ash content between 3.5% in the fresh plant and 22% in the dry plant material ([PERSON] et al., 2013). Some differences regarding the obtained chemical composition values are due to environmental, pedological, and agrotechnical factors and botanical variation.
## 5 Results and Discussion
### Load-Settlement Analysis--Experimental Result  
Figure 7a-c illustrates the load-settlement responses of both unreinforced and reinforced loose clays with stone columns of varying diameters (50, 75, and 100 mm) and different forms of reinforcement for columns for VESCs, while Figure 8a-c depicts the load-settlement behaviour of HRSCs. OSCs, HRSCs, and VESCs were discovered to increase the load-carrying capacity of soft soil. When increasing the \(\mathrm{a_{s}}\) from 6.25 to 25%, the ultimate carrying capabilities of all three types of columns (OSCs, HRSCs, and VESCs) were increased. To understand the behavioural change for various configurations, the ultimate load at 50 mm settlement was considered for the analysis. For \(D\) = 50, 75, and 100 mm, when an OSC is used in soft clay, the load capacity increases by 6.15, 16.43, and 29.06%, respectively. For the VESC case, when \(Lr\) = 0.25 L, the load capacity further increases by 9.72, 7.5, and 1.14% for 50, 75, and 100 mm diameter columns, respectively, with respect to the OSC case of individual diametral columns. Similarly, for \(Lr\) = 0.5 L, as compared to their individual OSC values, the load capacity increases by 15.58, 14.11, and 5.49% for \(D\) = 50, 75, and 100 mm, respectively. For \(Lr\) = 0.75 L, the increase was found to be 20.24, 19.78, and 10.41%. Moreover, when \(Lr\) = \(L\), the percentage increase in load capacity for the same cases was 23.52, 23.95, and 18.86%.  
Figure 6: Various failure modes by numerical analysis for 100 mm diameter column: (**a**) OSC, (**b**) VESC, (**c**) HRSC (The colour coding indicates the displacement of the column in the x-direction simulating the bulging of the column).  
Figure 7: Experimental result of load–settlement variation of VESCs for various \(Lr\) on single stone columns having diameters of (**a**) 50 mm, (**b**) 75 mm, and (**c**) 100 mm.  
For HRSCs, when \(Lr\) = 0.5 L from the centre of the column to the end, the load-bearing capacity was increased by 9.72, 9.87, and 2.27% for \(D\) = 50, 75, and 100 mm, respectively, compared to their corresponding OSC values. Similarly, when \(Lr\) = 0.5 L from the head to the centre of the column, the capacity increased by 14.14, 15.11, and 6.52%. Moreover, for \(Lr\) = \(L\) throughout the column length, the percentage increase in capacity was 17.72, 19.78, and 12.24%.  
Figure 8: Experimental result of load–settlement variation of HRSC for various \(Lr\) on single stone columns having diameters of (**a**) 50 mm, (**b**) 75 mm, and (**c**) 100 mm.
## 3 Results
### SPMD Effects  
Regarding the results of SPM1D, as shown in the blue rectangle parts in Figures 3 and 4, significant differences were also found for both hip, knee, and ankle joints. Regarding the hip joint, when using the bionic shoes, the extension angle decreased by 57-100% (\(p\) = 0.035), and extension angle velocity decreased by 28-70% (\(p\) < 0.001). The flexion moment increased by 22-28% (\(p\) = 0.008) and 51-100% (\(p\) < 0.001) respectively, while the extension moment decreased by 0-22% (\(p\) = 0.018) and 28-43% (\(p\) = 0.002) respectively. Flexion power was increased by 20-46% (\(p\) < 0.001), and extension power was decreased by 69-100% (\(p\) < 0.001). At the knee joint, when using the bionic shoes, the flexion angle was observed to increase by 10-38% (\(p\) = 0.010), while the extension angle increased by 57-100% (\(p\) = 0.001). The extension angle velocity increased by 20-78% (\(p\) < 0.001), and a 98-100% decrease in flexion angle velocity was observed (\(p\) = 0.047). The extension knee moment increased by 19-35% (\(p\) < 0.001), and the flexion knee moment decreased by 50-100% (\(p\) < 0.001); decreased flexion power was observed at 22-26% (\(p\) = 0.015) and 71-100% (\(p\) < 0.001), and extension power increased by 30-66% (\(p\) < 0.001). At the ankle joint, when wearing the bionic shoes, the dorsiflexion angle increased by 37-50% (\(p\) = 0.035), and the plantarflexion angle decreased by 65-100% (\(p\) = 0.003). The dorsiflexion angle velocity was observed to increase over the stance phase by 30-40% (\(p\) < 0.001), and a decrease in plantarflexion angle velocity of 18-25% (\(p\) = 0.003) and 42-97% (\(p\) < 0.001) were found; the ankle moment decreased by 23-60% (\(p\) < 0.001) and increased by 68-90% (\(p\) < 0.001). Decreased plantarflexion was detected while wearing the bionic shoes at 28-42% (\(p\) < 0.001) and 88-100% (\(p\) < 0.001), while dorsiflexion increased by 46-83% (\(p\) < 0.001).
## 3 RIS Modeling and Simulation
The RIS device is placed in the wireless channel, and the beamforming pattern is measured using a calibrated antenna array. The measured beamforming pattern is compared with the simulation results to validate the RIS's design [34]. Energy harvesting measurements are used to verify the energy harvesting capabilities of RIS devices. The RIS device is placed in a simulated energy-harvesting environment, and the amount of harvested energy is measured using a power meter. The measured energy harvesting results are compared with the simulation results to validate the RIS's design [44]. Imaging and sensing measurements verify RIS devices' imaging and sensing capabilities. The RIS device is placed in a simulated imaging or sensing environment, and the image or sensing results are measured using an imaging or sensing system. The measured results are compared with the simulation results to validate the RIS's design [23; 45].
## 4 Discussion
### Suspended Sediment Yield Rating Curve  
The accuracy of the suspended sediment rating curves must be checked, regardless of the regression coefficients. The goodness of them was checked by calculating the difference between the measured value and the value calculated by the usage of the curve equation.  
Figure 10: Suspended sediment yield as a function of near-bank suspended sediment concentration.  
In the case of the 2020-2021 measurement campaign, the average difference between them was 52% in relative terms and 88 kg/s in absolute terms. In half of the cases (6 out of 11), the relative difference was above 50%, and in five cases, the difference was equal to or above 100 kg/s.  
By analyzing the historical data, it can be concluded that based on the regression coefficient (R\({}^{2}\) = 0.76) or the average relative and the average absolute difference (31%, 46 kg/s), this relationship is much more accurate than the one based on the recent measurement. However, the calculation error in the discharge range above 2000 m\({}^{3}\)/s is higher with an average value of 72 kg/s. About 22% of the differences (13 out of 58) in this range are above 100 kg/s.  
Based on these, the accuracy of the suspended sediment yield rating curve is not adequate enough to perform accurate calculations about the annual sediment regime.
## 2 Commissioning Goal  
The bunch compressor will be used to maximize the FEL pulse energy with the booster phase as a beam-energy-chirp tuning knob. However, the bunch compressor was installed downstream of a longitudinal bunch profile measurement station, and the bunch profile after compression cannot be directly measured. In order to predict the bunch compression outcome via the start-to-end simulation results, both beam momentum chirps \(\mathrm{d}p/\mathrm{d}t\) upstream and coherent transition radiation (CTR) downstream of the chicane are measured to benchmark the simulation results in [2].  
Firstly, the generation of the beam momentum chirps upstream to the chicane via tuning booster phases was verified to benchmark ASTRA [9] tracking simulations with space charge effects for bunch charges of 10 pC, 30 pC, and 2 nC with the booster phase between 0 and \(-\)25 degrees [2,10]. The momentum chirps are extracted from the longitudinal phase space (LPS) measurement at PITZ with the setup consisting of a transverse deflecting system (TDS) and a dipole spectrometer (HEDA2), respectively [11]. Note that in the case of 2 nC, the 50-\(\upmu\)m-slit at a distance of 5.3 m upstream of the TDS is used to reduce horizontal emittance influence in order to enhance the LPS resolution. Thus, the beamlet of 2 nC beam momentum chirps is benchmarked ASTRA simulations.  
Secondly, the CTR is measured at the measurement station between the chicane and the LCLS-I undulator. The CTR measurement is expected to indicate the fully compressed-bunch booster phases for the beam with the bunch charge up to a few hundred pC, which are simulated [2] by the combination of the simulation programs ASTRA, IMPCT-t [12], and OCELOT [13,14]. In these simulations, program ASTRA with space charge effects tracks the beam from the RF gun to the chicane entrance, and then program IMPACT-T [15,16] with both space charge and CSR effects tracks the beam throughout the chicane. Ultimately, program OCELOT with only CSR effects also tracks the beam throughout the chicane to benchmark with program IMPACT-T.  
The CTR signals from compressed bunches can be measured after the chicane dipole magnet currents are obtained by the commissioning method. This benchmarking also represents that the commissioning method results in bunch compressor properties such as \(R_{56}\), as expected from the simulations.
## 3 Gold Nanosystems
In vivo studies have shown how multifunctional fluorescent magnetic nanocomplexes are used to trace the distribution of the nanocomplexes on tumor tissues. Nanocomplexes conjugated with specific antibodies targeting human epidermal growth factor receptor 2 (HER2) that overexpression breast cancer tumors could then be identified using magnetic resonance imaging (MRI) of the nanocomplex. As antibody-conjugated nanocomplexes are tracked throughout the body, we observe clear differences in the amounts of tumor uptake between over-expressed HER2 and low-expression HER2 tumors. This study demonstrated that it is possible to visualize tumors in vivo and that MRI could reveal a detailed picture of the distribution of nanoparticles in tumors and internal organs [50]. The diagnostic capabilities of the nanocomplexes have been visualized in vivo on HER2-expressing breast cancer tumors in animal models. Molecular targeting is achieved by combining anti-HER2 antibodies on the surface of the nanoparticle via the streptavidin-biotin binding procedure. In addition, poly ethylene glycol (PEG) conjugated to nanocomplexes is used to weaken nonspecific binding in vivo, to sterically stabilize the complexes, to implement circulation time, to lower immunogenicity and, in combination with antibodies, to increase accumulation of nanoparticles in the tumor [51]. Advantageous biological systems were investigated that exploit polyvalent interactions, allowing an organism to take advantage of a set of monovalent ligands with lower affinity, rather than using new and higher affinity monovalent ligands for each function. Ligand binding to a gold nanoparticle in the multivalent mode is an effective way to generate a high local concentration of ligands. The binding equilibria between the surface-bound ligand and the receptor can be shifted towards the formation of more ligand-receptor pairs in the presence of a high local ligand concentration according to the Le Chatelier principle [52]. One type of ligands, conjugated to gold nanoparticles, are carboxales, extensively studied for their antioxidant and antimicrobial properties. Carbazole derivatives have also become important for their efficient inhibition of topoisomerase, tubulin, telomerase, kinase and integrase [53; 54]. These compounds induce antiproliferative activity and a significant apoptotic response in a selective manner towards tumor cells [55; 56; 57]. The gold nanoparticles functionalized with \(N\)-thiocarbazole derivatives (Figure 2) have been employed as antiproliferative agents against breast and uterine cancer cell lines without affecting non-tumor cells [58]. The unique properties of gold nanoparticles were exploited for detection assays, employing the nanoparticles as cell-permeable multivalent systems, and further investigating promising and targeted therapies for known receptors [59]. Figure 2: Gold nanoparticles functionalized with \(N\)-thioalkylcarbazole derivatives.
## 4 Validation I: Simulation of Open-Hole Tension Tests with Optimal GA Solution
### Qualitative Damage Analysis  
Figure 9 shows a qualitative comparison of the strain fields in vertical \(y\) direction obtained from FE simulation (using element size of 1 mm \(\times\) 1 mm) and Digital Image Correlation (DIC) [6] for the OHT8 specimen geometry at 90% of the maximum simulated/measured OHT strength. Both strain fields show similar concentrations in magnitude and shape around the hole in the normal direction to the applied load. Minor differences can be explained by the natural inhomogeneity of wood where microscopic imperfections can alter the load path leading to a slightly asymmetric DIC strain field shown in Figure 9. Nonetheless, the good qualitative match of the strain fields in Figure 9 and the reasonable quantitative comparison of OHT strength data show that the presented FE methodology can yield fast and accurate results for the simulation of progressive damage in wood vener laminates.  
Apart from the strain field, it is important to compare the simulated damage zones with experimentally observed fracture. Figure 10 shows the predicted damage zones for each OHT test geometry next to a corresponding photo of a post-mortem OHT sample from experiments. Again, the simulations using the coarse mesh of 1 mm \(\times\) 1 mm are able to accurately estimate the location and extent of damage which is concentrated around the hole and in normal direction to the applied loads. Similar to the observation of simulated CT tests shown in Figure 6, the virtual damage bands in OHT tests grow along one row of elements. As mentioned before, this motivates the application of [PERSON]'s crack band scaling to ensure physically meaningful damage simulations.  
Figure 9: Comparison of simulated and experimental strain fields in open-hole tension test (OHT8) at 90% of maximum strength.
# LSTM Network-Assisted Binocular Visual-Inertial Person Localization Method under a Moving Base
## 2 Materials and Methods
### Basic Description of Visual-Inertial
#### 2.2.2 LSTM Network Architecture  
In this paper, a special type of RNN, LSTM network (Long Short Term), is used. the memory of RNN is short term and there is a problem of gradient disappearance in RNN during backpropagation. The gradient is the value used to update the weights of the neural network, and if the gradient value becomes very small, it will not continue learning. Therefore, network layers with small gradient updates in the RNN stop learning, and these are usually the earlier layers. Since these layers do not learn, the RNN cannot remember what it has learned in longer sequences, so its memory is short-term. The LSTM is a proposed solution to overcome the short-term memory problem by introducing internal mechanisms called \"gates\" that regulate the flow of information. These gate structures can learn which data in a sequence are important to keep and which to delete. By doing so, they can pass relevant information along a long chain of sequences to perform predictions. The collected dataset is a long sequence, so the LSTM neural network was chosen to be used.  
Figure 5 shows the architecture of the LSTM network:  
Figure 4: Data Acquisition. The IMU data collected on the wearable are the training set, and the data collected by the camera device are the ground truth information.  
From bottom to top, the first layer is the coordinate system of IMU on the device, i.e., (b-system); the second layer is the normalized coordinate system, i.e., transformed to (n-system); the third layer is the LSTM neural network; the fourth layer is the 6-dimensional IMU data output by the neural network, and the output position is solved by the fifth layer, which is supervised by the ground truth information in the uppermost layer and constitutes the loss function.
# Deriving a Dilution of Precision Indicator for GNSS Factor Graph Optimization Solutions +
## 2 Materials and Methods
### Efficient Computation of the Batch Estimation Covariance  
To compute efficiently the batch solution covariance matrix, one can use the particular shape of (10). Additionally, we are interested only in the diagonal elements of the matrix, to characterize the uncertainty of the considered scalar state estimate at each epoch \(k\). The symbolic expression of the diagonal elements for the first few values of \(K\) is shown in Appendix A. It is observed that the \(k\)-th diagonal element is a ratio of 2 expressions \(N_{j,k}\) and \(D_{j}\) which depend on \(q,\sigma_{j,1},\ldots,\sigma_{j,K}\):  
\[(\mathbf{P}_{j}^{(batch)})_{k,k}=\frac{N_{j,k}(q,\sigma_{j,1},\ldots,\sigma_{ j,K})}{D_{j}(q,\sigma_{j,1},\ldots,\sigma_{j,K})} \tag{11}\]The denominator \(D_{j}\) is common to all epochs of the considered trajectory. It can be computed using a second-order linear recurrence relation:  
\[p_{0} =1/q^{2},\quad p_{1}=1+\sigma_{j,1}^{2}/q^{2}\] \[p_{k} =(q^{2}+2\sigma_{j,k}^{2})p_{k-1}-\sigma_{j,k}^{2}\sigma_{j,k-1}^{ 2}p_{k-2}\quad,\text{for }2\leq k<K\] \[D_{j} =p_{K} =(q^{2}+\sigma_{j,k}^{2})p_{K-1}-\sigma_{j,k}^{2}\sigma_{j,K-1}^{ 2}p_{K-2} \tag{12}\]  
The numerator \(N_{j,k}\) depends on the considered epochs within the trajectory and can be computed using the following expression:  
\[N_{j,k}=D_{j}(\sigma_{j,k}=0)\sigma_{j,k}^{2} \tag{13}\]  
where \(D_{j}(\sigma_{j,k}=0)\) is the evaluation of expression \(D_{j}\) at \(\sigma_{j,k}=0\) and leaving other parameters untouched.  
The expressions of \(N_{j,k}\) and \(D_{j}\) have been validated by comparing the diagonal elements of Equation (10) to the values computed using Equation (11), using a symbolic mathematical software up to \(K=10\), and numerical simulations up to \(K=100\). Elements of the mathematical demonstration to obtain those expressions are available in Appendix B.
# Summarization of Remaining Life Prediction Methods for Special Power Plants
## 2 Research Status at Home and Abroad
### Existing Problems  
In conclusion, national and international scholars have conducted extensive research on mechanical equipment RUL, and a series of research results have been achieved, with good results in the field of rotating machinery. However, for the field of special power plants, breakthroughs are still needed regarding the creation of a dynamic stochastic degradation model with interpretability and generalization potential and the construction of mapping relationships between dynamic parameters and RUL. In summary:  
1. The complexity of the special power plant and the variability of the operating conditions cause the coupling interference of a variety of degradation factors, which is not conducive to describing the degradation trends in the plant's dynamic performance. This makes it difficult to obtain an accurate and generalized degradation model.
2. The RUL prediction of data-driven mechanical equipment requires one to monitor big data; thus, the data acquisition method is the first problem that needs to be solved. The existing data acquisition method often uses mechanical equipment with wired or wireless sensor external measuring equipment that runs incentive response parameters. This method of obtaining data leads to a high degree of random noise, and only the internal mechanism dynamic process is passed on to the external data. Therefore, this method cannot directly and effectively reflect the actual dynamic state of the internal mechanism.
3. For special power plants, the RUL prediction method driven by the dynamic model mainly builds a parametric mathematical model based on the dynamic degradation process but fails to combine the real-time monitoring data. This makes it difficult to reflect the actual state of the current operation; therefore, the prediction deviation is significant. The RUL prediction method driven by dynamic data lacks a strict dynamic theoretical basis and interpretability and has a poor generalization ability. Additionally, it is difficult to design hyperparameters for the network.
## 3 Materials and Methods
### AIS Data Source  
To meet the needs of monitoring global ships, the Haiyang-1C (HY-1C) and Haiyang-2B (HY-2B) satellites are all equipped with highly sensitive AIS receivers. The monitoring widths of the HY-1C and HY-2B satellites are better than 950 km and 1000 km, respectively. The HY-1C and HY-2B satellites are in sun-synchronous orbits (SSO), and can achieve the ability to collect information and monitor global ocean ships twice a day. These two satellites can obtain the location and attribute information of global maritime ships, and provide data services for the maintenance of maritime rights and interests, marine disaster prevention and mitigation, and marine fishery production activities. Based on the AIS data received by the HY-1C and HY-2B satellites, this paper makes full use of the advantages of the wide coverage and rich ship navigation information of the spaceborne AIS data to perform ship classification and anomaly detection research. Figure 1 shows a chart drawn from the partial AIS data received by the HY-1C satellite.  
Figure 1: The partial AIS data received by the HY-1C satellite.  
This paper analyzes the AIS data received by the HY-1C and HY-2B satellites within one year, and mainly studies messages 1, 2, 3, and 5 among 27 types of AIS messages. These messages comprehensively cover the static and dynamic information of ships, including ship maritime mobile service identification (MMSI), size, type, position, draught, rate of turn (_ROT_), speed over ground (_SOG_), and heading information. The AIS data used in this paper are shown in Table 1.