esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Accuracy of Control-Free Nap-of-the-Object Photogrammetry Images  
#### 3.2.1 DSM Positioning Accuracy  
The processing results for images acquired after two phases of the control-free nap-of-the-object photogrammetry image acquisition are shown in Table 5. Both acquisition phases yielded over 1100 images, with 100% completion of in-flight operations and 100% post-data processing image calibration. The average reprojection errors obtained from the two sets of data are similar, both around 0.012 pixels, which is a relatively small value. The DOM image resolution is 5 mm for both phases. DSMs were acquired using both image control- and control-free-image-based nap-of-the-object photogrammetry, as shown in Figure 12. Table 6 shows where the accuracy of the DSMs before and after rainfall in both control-free images and image control scenarios is around 0-0.05 m in the X-, Y-, and Z-coordinates. The resolution of the DSMs for both periods is 1.9 cm. The mean absolute errors vary in the planar and vertical directions obtained by the two photogrammetric methods and range from 0.01 to 0.02 m and from 0.01 to 0.03 m, respectively. The mean absolute error and RMSE of X- and Y-coordinates for 06.22 and 07.15 are both 0.01 m. The average error and RMSE in the vertical direction of the Z-coordinate are larger than those of the X- and Y-coordinates. In the vertical Z-coordinate, the absolute average error for both periods is larger than the absolute average error of the image control scenario, with a difference of approximately 0.01 m. The maximum RMSE is 0.03 m, and the minimum is 0.01 m, corresponding to the control-free nap-of-the-object photogrammetry image measurements. The errors between the two periods are small, only 0.01 m and 0.02 m.  
Figure 11: Data volume differences of DSMs for all experimental groups. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Processing Parameter** & **22 June 2022** & **15 July 2022** \\ \hline Number of images & 1184 & 1176 \\ Number of dense point clouds & 243,626,208 & 25,329,724 \\ Number of point clouds in the Benggang area & 4,412,945 & 4,534,858 \\ Number of ground checkpoints & 10 & 9 \\ Average reprojection error (pixels) & 0.013 & 0.012 \\ Ground resolution (mm) & 5.0 & 5.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Basic information of captured images. In the DSM data, there is strong consistency between the horizontal errors and the vertical errors for the two periods. Horizontal errors are relatively stable, fluctuating around 0.01 m. However, there is a large fluctuation the vertical errors compared with the horizontal errors, which is a small fluctuation of the vertical errors.
# Towards Deterministic-Delay Data Delivery Using Multi-Criteria Routing over Satellite Networks
## 2 System Model
### Spatiotemporal Distribution Model of Service Traffic
According to the literature [18], the spatiotemporal traffic transmission demand model between any two satellites can be derived from the traffic transmission demand based on the geographical distribution \(R_{u,v}\) and the traffic ratio based on the time distribution \(p_{\tau}\), expressed as:  
\[\lambda_{\tau,u,v}=\frac{R_{u,v}}{\sum_{u=1}^{N}\sum_{u=1}^{N}R_{u,v}}*p_{\tau}* \frac{A}{3600},u\
eq v \tag{2}\]  
where \(A\) represents the total traffic across the entire network for the whole day, measured in bits. In the service model, the data packets generated per unit time follow a Poisson distribution, and \(\lambda_{\tau,u,v}\) represents the amount of traffic transmitted from satellite \(u\) to satellite \(v\) during the time period \(\tau\), measured in bits per second (bit/s). Given the time range \(T\) and the snapshot graph \(G\), the traffic transmission demand between any two satellites during each time period can be defined as \(\mathcal{L}=\{\lambda_{\tau,u,v}|\forall\tau\in T,u,v\in\mathcal{V}\}\). The spatiotemporal traffic factor \(\lambda_{\tau,u,v}^{ts}=\omega\times\lambda_{\tau,u,v}\) (where \(\omega\) represents the proportion of time-sensitive services within the total traffic), which is used to characterize the time-sensitive traffic demand in each time period.
# Role of Autologous Platelet Gel (APG) in Bone Healing: A Rabbit Study
## 2 Materials and Methods  
In this study, 6 New Zealand white mature male (age ~11 weeks and weighing around ~2.5 kgs) were used. The local Ethics Committee of Albania University approved the study and it was carried out in accordance with the relevant guidelines and regulations of Albanian law on animal research. The in-vivo study followed the guidelines (national and international) for animal treatment and conformed to the ARRIVE guidelines. The rabbits were housed under veterinary supervision in standardized rabbit cages (only one rabbit per cage), maintained in a laboratory environment with a controlled temperature (21-24.5 \({}^{\circ}\)C), humidity (43-56%), and 12-h light and dark cycles. All animals had ad libitum access to a standard rabbit chow diet and water. The leg was shaved and disinfected using Povidone-iodine 10% solution (SALVAVIDAS PHARMACEUTICAL PVT. LTD. Surat, India). The rabbits were anesthetized with intramuscular injections of fluanizone (0.7 mg/kg b.wt.) and diazepam (1.5 mg/kg b.wt.), and local anaesthesia were applied using 1 mL of 2% atcaine/adrenalin solution (Pierrel Pharma, Italy). After identification, a longitudinal incision was performed in the tibia and dissection of the skin flap exposed the bone. A conventional dental handpiece with a physio-dispenser (Vario-Surgery NSK, Tochigi, Japan) was used for bone defect preparation with drills cooled by saline irrigation. In each rabbit tibia, twocritical-size circular defects (6-mm diameter) were created (Figure 1).  
For each tibia, one defect was filled with APG while the second defect was left empty and was used as control. The defect filled with APG was randomized to avoid the influence of the dimension of the tibia on the healing process. No membrane was used for covering the bone defect. The periosteum and fascia were sutured with resorbable suture (Glicofil Lac(r), Assut, Magliano dei Marsi, Italy) and the skin with silk (Assusilk(r), Assut, Magliano dei Marsi, Italy). No deaths or other complications occurred in the postoperative period. All the rabbits were sacrificed with an overdose of intravenous pentobarbital, after 4 weeks. A total of 12 specimens (6 tests and 6 controls) were retrieved
## 3 Results
### Nutrient Uptake  
Nitrate uptake rates were more than 2-fold greater in vegetated ditches but decreased with increasing HRT (Figure 3, Table 1). HRT had overall significant effects on nitrate net uptake(\(F_{1,351}\) = 45.35, \(p\) < 0.001), but was also influenced by a significant interaction between HRT and vegetation with stronger effects of HRT observed in vegetated compared to unvegetated ditches (\(F_{1,351}\) = 4.29, \(p\)= 0.04; Figure 3A). Initially, observed concentrations were similar across HRTs (2 h = 2.43 \(\pm\) 0.46 mg L\({}^{-1}\); 4 h = 2.2 \(\pm\) 0.47; 6 h = 2.41 \(\pm\) 0.53). Shorter residence times (2 h) displayed significantly higher uptake rates and removal (32% in unvegetated ditches) than longer residence times (4 h and 6 h, 21% and 17% for unvegetated ditches) with the magnitude of fluxes higher in vegetated drainage ditches (2 h: 63%, 4 h: 44%, 6 h: 32%). For P net uptake, HRT treatment was found to significantly influence rates overall (\(F_{1,351}\) = 4.23, \(p\) = 0.04) but differences between 2 h, 4 h, and 6 h were not significant at the \(p\) < 0.05 level (Figure 3B). There was no significant overall effect of vegetation as similar patterns between unvegetated and vegetated drainages were observed in the 2 h treatment (14% vs. 16% P removal) and rates exceeded 7 mg P m\({}^{-2}\) h\({}^{-1}\) (\(F_{1,4}\) = 2.41, \(p\) = 0.19; Table 1). The interaction of vegetation and HRT was not significant (\(F_{1,351}\) = 0.17, \(p\) = 0.68); rates of P uptake in vegetated drainages remained high while rates in unvegetated drainages declined in the 4 h and 6 h treatments (Figure 3B).  
Figure 3: Drainage (**A**) nitrate and (**B**) soluble reactive phosphate (SRP) hourly net uptake rates by hydraulic residence time and vegetation treatment for all runs. Letters distinguish significant differences among HRT treatments within vegetation treatments. Bars are the means and error bars represent the standard error of the mean.
# Study on Classification Method of Soil Liquefaction Potential Based on Decision Tree
## 5 LR liquefaction Probability Equation
### Comparison with Database C  
Database C was selected as the verification set, and the \(V_{\text{scr}}\) with a preset probability of 50% was used for verification. Statistics on the misjudgments are shown in Table 3.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Statistical Items** & & **Total** & **Liquefaction** & **Non-Liquefaction** \\ \hline \multirow{3}{*}{Total} & Total number & 415 & 287 & 128 \\ \cline{2-5}  & \multirow{2}{*}{Misjudgment cases} & Number & 122 & 73 & 49 \\  & & Proportion & 29\% & 25\% & 38\% \\ \hline \textless{VII} & 0 & 0 & 0 & 0 \\ \hline \multirow{3}{*}{VII} & Total number & 81 & 37 & 44 \\ \cline{2-5}  & \multirow{2}{*}{Misjudgment cases} & Number & 35 & 11 & 24 \\ \cline{2-5}  & \multirow{2}{*}{Proportion} & 43\% & 30\% & 55\% \\ \hline \multirow{3}{*}{VIII} & Total number & 137 & 107 & 30 \\ \cline{2-5}  & \multirow{2}{*}{Misjudgment cases} & Number & 37 & 23 & 14 \\ \cline{2-5}  & \multirow{2}{*}{Proportion} & 27\% & 21\% & 47\% \\ \hline \multirow{3}{*}{IX} & Total number & 181 & 143 & 38 \\ \cline{2-5}  & \multirow{2}{*}{Misjudgment cases} & Number & 50 & 39 & 11 \\ \cline{2-5}  & \multirow{2}{*}{Proportion} & 28\% & 27\% & 29\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Retrieval accuracy of the equation for PL = 50%.  
Figure 2: Success rates of different preset liquefaction probabilities for fitted samples. The data range in the figure is marked at the lower left corner, i.e., all the sites (TOTAL), zone VII, zone VIII, and zone IX.  
In terms of the ground motion zones, the applicability of the logistic regression equation (based on the discrimination accuracy) was higher in the zone VIII and zone IX seismic intervals than in the zone VII seismic intervals. Liquefaction could be accurately predicted for 70% to 80% of the sites in these seismic intervals. The applicability of the logistic equation was directly related to the data distribution in the fitting set. In the fitting set, there was only a small amount of data for the zone VII intensity, and the boundary between the liquefied data and the non-liquefied data was not distinct. When distinguishing the data for the VII intensity, the discrimination accuracy of the equation for a liquefied site was higher than that for a non-liquefied site. For these reasons, the model is safer for engineering use in this case.  
Based on the statistics of all the sites, misjudgment cases accounted for 29% of all sites, among which extreme misjudgment cases accounted for 14% of all the sites and 49% of all the misjudgment sites. The statistical results showed that the prediction results of most sites were consistent with the actual site classification. The \(P_{L}\) of 51% of the data of the misjudgment cases was concentrated between 33% and 66%, which was near the liquefaction critical line of \(P_{L}\) = 50%.
## 3 Results and Discussion
### RSSI Localization
#### 3.2.2 RSSI Localization
Under these two assumptions, the hybrid localization approach may be able to outperform traditional solutions.
## 3 Numerical Calculation and Simulation Analysis
### Single-Model Simulation Analysis
#### 3.1.2 Operating Subsidy Model Simulation Analysis  
Referring to the subsidy-free equilibrium outcomes, \(r\) is set to 0.3, the simulated results are calculated with \(f\) taking the values 0.1, 0.3, 0.5, and 0.8, respectively, as shown in Figure 3. Then, \(f\) is set to 0.3 to analyze the impact of the cost-sharing contract on supply chain performance, and the results are shown in Figure 4.  
Figure 4: The influence of suppliers’ eco-feeding apportionment coefficient on supply chain pricing and utility under the development and operation subsidy.(**a**) The wholesale price, market price, and ecological level. (**b**) The profit of the supplier, distributor, and supply chain.  
Figure 3: The influence of the development and operation subsidy coefficient on supply chain pricing and utility. (**a**) The wholesale price, market price, and ecological level. (**b**) The profit of the supplier, distributor and supply chain.  
Consistent with Proposition 2, the results show that: (1) Under the assumption of a fixed contribution contract, the operating subsidy can directly improve the ecological level, ecological feedback investment, wholesale price, and market price, followed by decreasing sales; with the increase in the subsidy coefficient, the distributor's utility decreases, mainly because of the greater change rate of the market price and the higher cost-sharing ratio; although the supplier's utility has increased, the total utility of the supply chain drops. (2) Under the condition of a certain subsidy, the impact of the cost-sharing contract on supply chain pricing and utilities is in accordance with the subsidy-free mode. Therefore, the operating subsidy motivates the supplier more; meanwhile, the selection of a reasonable coefficient and cost-sharing contract is beneficial to the supply chain's performance and EVVR.
# Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems
## 3 System Model
### Computational Complexity  
The proposed SSA-CoSaMP algorithm requires \(M\!K\!N_{i}+M\!K\) complex multiplications to complete the calculation of threshold-based channel estimation, and also requires \(2K^{2}\) complex multiplication to obtain the final estimate of the CIR. The complexity of the proposed channel estimation algorithm is mainly related to the LS algorithm and the number of iterations. Therefore, the total complexity of the proposed algorithm is \(\mathcal{O}\!\left(M\!K\!N_{i}+M\!K+2K^{2}\right)\).
## 3 Large Arrays Fabrication
### TES Bilayer  
The TES itself is the core of an X-ray calorimeter. It is the sensing element that can detect very small temperature changes. As shown in Figure 7, it consists of a superconducting thin film connected between superconducting leads. For high resolution spectroscopy, the transition temperature of the film is chosen typically within the range from 50 to 100 mK. However, other transition temperatures can be selected depending on applications. Various types of films have been proposed and successfully used for almost any photon wavelength to achieve the targeted transition temperatures; a single layer of tungsten [86; 87], alloys such as Al-Mn [88], and proximity-coupled bilayers (or multilayers) of Ti/Au [89], Mo/Au [90; 91; 92; 93], Mo/Cu [94], Ir/Au [95], and Al/Ti [96; 97]. The geometric size of the superconducting slices is normally tens of microns or larger so wet etch, dry etch, or lift-off processes can be adopted in combination with a standard UV photolithography.  
The materials for the superconducting leads must have a higher transition temperature and a critical current with respect to the TES film. Niobium is widely used, but other superconductors such as niobium nitride, molybdenum, and aluminum are also applied. One of the technical challenges for the scalability of the sensing structures is the topology of the wirings on a detector chip. Superconducting broad-side coupled micro-striplines are essential in creating dense-packed arrays. An example of the structure of the microstriplineis given in the schematic of Figure 7a. It consists of two layers of the superconducting leads, which are electrically isolated by a thin oxide layer. The state-of-the-art system of wiring lines consists of multiple broad-side coupled lines, embedded within oxide layers by applying chemical mechanical polishing so that planarized microstripline layers can be stacked in a vertical direction. Massachusetts Institute of Technology Lincoln Laboratory has demonstrated the process with six superconducting Nb layers for superconducting electronics [98; 99; 100].
# A Perspective on Terahertz Next-Generation Wireless Communications
## 3 Terahertz Devices
While dual semiconductor lasers can be implemented in impressively small packages [85], the overall systems are still far from suitable for modern mobile devices. Neither are such systems highly integrable into modern electronics. Nevertheless, for fixed point links, the hybrid photonic-electronic approach may prove to be a highly viable architecture. All of this prompts the question of how to move forward with terahertz devices. To date, there has been a concerted effort to leverage mainstream integrated circuit technologies in terahertz communication front-ends (e.g., CMOS). Pushing the limits of these technologies and adopting new ideas from other disciplines have been major drivers of the research thus far, and the result has been a steady improvement in device performance in the 0.1 to 1 THz regime. However, the next step may be a holistic optimization across multiple layers of the entire system, which should open another dimension of engineering to improve the front-end performance. As an example, ongoing THz antenna development, driven by new concepts in metamaterial-inspired antennas, could change the interface between the antenna system and the front-end, which may call for a completely new front-end design. Furthermore, as the baseband data rate increases, physical and logical partitioning between front-end and baseband circuits could need to be revisited. Last but not least, high-frequency, high-speed packaging and interconnects will introduce other design challenges [94]. In other words, in order to get to viable and robust terahertz wireless communications, it probably will not be sufficient to solve only isolated problems. It appears that wholesale changes in electronic _systems_ or fundamentally new hybrid photonic-electronic architectures will be required and the exact nature of these changes is not clear yet.
## 3 Materials and Methods
### Qualitative Analysis of Terpenoids in Prepared Extracts by HS-SPME-GC-MS  
For the qualitative analysis of terpenoid constituents of the obtained extracts, a headspace solid-phase microextraction Arrow (HS-SPME Arrow) method technique with a 1.10 mm DVB/C-WR/ PDMS SPME Arrow fiber (Shimadzu, Kyoto, Japan) was applied. Briefly, 20.0 \(\pm\) 0.05 g of extracts was placed into 20 mL headspace vials, along with 20 \(\upmu\)g of undecane-2-on (Sigma-Aldrich, Steinheim, Germany) as the internal standard. Before the extraction, the samples were incubated for 5 min at 45 \({}^{\circ}\)C and, thereafter, the extraction was carried out at the same temperature for 30 min. Then samples were desorbed in a GC-MS apparatus injector for 3 min at 250 \({}^{\circ}\)C, with a split ratio of 10. The carrier gas helium 5.0 was used with a linear velocity of 36.3 cm s\({}^{-1}\). Extractions were performed in three repetitions.  
For analyte separation, the Shimadzu GCMS QP 2020 Plus (Shimadzu, Kyoto, Japan) equipped with a Zebron ZB-5 MSi capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m; Phenomenex, Torrance, CA, USA) was used. The following GC program was applied: 50 \({}^{\circ}\)C immediately raised to 130 \({}^{\circ}\)C with a ratio of 3 \({}^{\circ}\)C min\({}^{-1}\), then to 180 \({}^{\circ}\)C with a ratio of 5 \({}^{\circ}\)C min\({}^{-1}\), and then to 280 \({}^{\circ}\)C with a ratio of 20 \({}^{\circ}\)C min\({}^{-1}\). The MS operational conditions included an ion source and interface temperature of 250 \({}^{\circ}\)C, with a scan mode range of 40-400 \(m/z\). For qualitative analysis, the experimentally obtained analytes' mass spectra were compared with the Flavours and Fragrances of Natural and Synthetic Compounds 3.0 (FFNSC 3.0) and National Institute of Standards and Technology (NIST20) libraries. Only scores with a similarity of \(\geq\)90 were considered as potential targets. Identification was confirmed by linear retention indices (LRIs), calculated against a C7-C40 mixture of homologous n-alkanes (Sigma-Aldrich, Steinheim, Germany) and restricted to \(\pm\)15 points. Quantitative analysis was performed by area peak normalization, calculated using the internal standard peak area.
## 2 Materials and Methods
### Flavonoid Content Extraction  
Fresh holy basil canopy leaves were dried at 40 \({}^{\circ}\)C for 72 h. The dried leaf tissue was then ground into a fine powder using a pestle. Extractions were carried out using a modified method [39]. Specifically, 10 mg of fine powder was extracted with 5 mL of absolute methanol (99.9%, HPLC grade, FISHER, Waltham, MA, USA) containing 1% HCl. The mixture was thoroughly vortexed and incubated at 25 \({}^{\circ}\)C for 3 h. After incubation, the solution was centrifuged at 15,249\(\times\)\(\it{g}\) for 5 min using an Eppendorf Centrifuge 5810R equipped with a rotor F-34-6-38 (6 \(\times\) 125 g). After extraction, the supernatant was transferred to a separate microcentrifuge tube (2 mL) for subsequent assessment of flavonoid content  
The total flavonoid content was quantified using a colorimetric method, following the procedure outlined by [PERSON] et al. [40] and [PERSON] et al. [41], with slight modifications. A 350 \(\upmu\)L aliquot of the extracted solution was combined with 75 \(\upmu\)L of 5% sodium nitrite (NaNO\({}_{2}\)) in a 1.5 mL microcentrifuge tube, then centrifuged at 12,000 rpm for 2 min at 25 \({}^{\circ}\)C. After allowing the mixture to rest at room temperature for 5 min, 75 \(\upmu\)L of 10% aluminium chloride (AlCl\({}_{3}\)-6H\({}_{2}\)O) was added and thoroughly mixed via vortex mixer. The reaction mixture was centrifuged again and left to stand for another 5 min. Subsequently, 1 M sodium hydroxide (NaOH) was added. The homogenised solution was centrifuged and maintained at 25 \({}^{\circ}\)C for 15 min. Absorbance was measured at 515 nm using a spectrophotometer (Multiskan Sky, Thermo Fisher Scientific, Waltham, MA, USA). The total flavonoid content was calculated based on a standard curve of ruitin dissolved in dimethyl sulfoxide (DMSO), with the concentration expressed as milligrams of ruitin equivalents per gram of dry weight (DW).