esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Energy Analysis of The Scalf  
### Energy Model  
In order to analyze the energy consumption conditions of SCalf, the energy model must be established. For SCalf, the calculation of the energy consumption is obtained by the integration of the joint power. The joint power consists of the mechanical power and heat rate, as shown in Equation (34), where \(T\) is the gait cycle, \(E\) is the energy consumption in one gait cycle, and \(P\) is the joint power.  
\[\begin{split} E&=\int_{0}^{T}Pdt\\ &=\int_{0}^{T}(|\tau\theta|+f_{f}\dot{x}_{p})dt\end{split} \tag{34}\]  
In the second line of Equation (34), \(f_{f}\) and \(\dot{x}_{p}\) are the friction and piston velocity of the hydraulic cylinder, respectively. The joint torque \(\tau\) can be acquired by the dynamics analysis.  
The structure of the hydraulic actuator is shown in Figure 4. The displacement and force of the piston can be measured by the position and force sensors. The motions of the piston are controlled by the servo valve.  
The friction of hydraulic cylinders are strongly coupled by many parameters, and it has a high nonlinearity. It depends on many conditions like the cylinder velocity \(\dot{x}_{p}\), the pressure difference \(\Delta p\) across the piston and possibly on the cylinder position \(x_{p}\), and the temperature \(t\)[15]. The SCalf uses the asymmetry cylinders to realize the joint motions. The model parameters are different when the actuator are extending or shorting. According to the research made by [PERSON], the model of the hydraulic frictions can be written in Equation (35) [16].  
\[f_{f}(\dot{x}_{p})=\begin{cases}B^{+}\dot{x}_{p}+\left(F_{c0}^{+}+F_{s0}^{+}e^ {-\frac{|\dot{x}_{p}|}{C_{s}^{-}}}\right),\dot{x}_{p}>0\\ B^{-}\dot{x}_{p}-\left(F_{c0}^{-}+F_{s0}^{-}e^{-\frac{|\dot{x}_{p}|}{C_{s}}} \right),\dot{x}_{p}<0\end{cases} \tag{35}\]  
In Equation (35), \(B\) is the viscous friction coefficient (N/m), \(F_{c0}\) is the Coulomb friction coefficient (N), \(F_{s0}\) is the static friction coefficient (N), and \(C_{S}\) is the attenuation coefficient of static friction. Using the measured result made by [PERSON] et al. [17], the parameters in Equation (35) can be obtained as follows. When \(\dot{x}_{p}\geq 0\), \(B\) = 220 Ns/m, \(F_{c0}\) = 50 N, \(F_{s0}\) = 30 N and \(C_{s}\) = 0.015 m/s. When \(\dot{x}_{p}<0\), \(B\) = 180 Ns/m, \(F_{c0}\) = 50 N, \(F_{s0}\) = 20 N, and \(C_{s}\) = 0.007 m/s.  
The cylinder velocities can be obtained by the differential of cylinder lengths. The cylinder lengths can be calculated by the mechanical parameters in Figure 5 and Table 2.  
Figure 4: The structure of the hydraulic actuator.  
The relationship between the cylinder lengths and mechanical parameters are shown in Equation (36)  
\[\begin{cases}c_{1}=\sqrt{a_{1}^{2}+b_{1}^{2}-2a_{1}b_{1}cos(-\theta_{1}+\frac{ \pi}{2}-e_{1}-e_{2})}\\ c_{2}=\sqrt{a_{2}^{2}+b_{2}^{2}-2a_{2}b_{2}cos(-\theta_{2}+\pi-e_{3}+e_{4})} \end{cases} \tag{36}\]
# ILRS Reference Point Determination Using Close Range Photogrammetry
## 4 Conclusions
For instance, the standard deviations of the coordinate components of the reference point derived by the fully populated dispersion are about three times larger than for the most simplified model, cf. Figure 8. To avoid misinterpretations and to derive reliable and traceable results, it is highly recommended to introduce an appropriate stochastic model. The following are available online at [[https://www.mdpi.com/2076-341/7/11/6/2785/s1](https://www.mdpi.com/2076-341/7/11/6/2785/s1)]([https://www.mdpi.com/2076-341/7/11/6/2785/s1](https://www.mdpi.com/2076-341/7/11/6/2785/s1)). Conceptualization, M.L. and C.E. ; methodology, M.L. and C.E. ; analysis software, M.L. ; remote control software, S.R. ; formal analysis, M.L. ; investigation, M.L. and C.E. ; writing--original draft preparation, M.L., C.E., and T.K. ; writing--review and editing, M.L., C.E., T.K., and S.R. ; visualization, M.L. ; project administration, C.E. and T.K. All authors have read and agreed to the published version of the manuscript. This project 18 SIB01 GeoMetre [10] has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme. Not applicable. All data used in this paper are available in Supplementary Materials. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} DN & Drift nest \\ DORIS & Doppler orbitography and radiopositioning integrated by satellite \\ GGOS & Global geodetic observing system \\ GGRF & Global geodetic reference frame \\ GGRS & Global geodetic reference system \\ GMM & Gauss-Markov model \\ GNSS & Global navigation satellite system \\ GOW & Geodetic Observatory Wettzell \\ ILRS & International Laser Ranging Service \\ IRP & Invariant reference point \\ ITRF & International Terrestrial Reference Frame \\ MCS & Monte Carlo simulation \\ MPE & Maximum permissible error \\ MSSUT & Minimal skew simplex unscented transformation \\ NNN & No-net-rotation \\ NNS & No-net-scale \\ NNT & No-net-translation \\ UT & Unscented transformation \\ SLR & Satellite laser ranging \\ SOS-W & Satellite Observing System Wettzell \\ SSUT & Spherical simplex unscented transformation \\ VLBI & Very long baseline interferometry \\ \end{tabular}
# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
###### Abstract  
Three-dimensional reconstruction entails the development of mathematical models of three-dimensional objects that are suitable for computational representation and processing. This technique constructs realistic 3D models of images and has significant practical applications across various fields. This study proposes a rapid and precise multi-view 3D reconstruction method to address the challenges of low reconstruction efficiency and inadequate, poor-quality point cloud generation in incremental structure-from-motion (SFM) algorithms in multi-view geometry. The methodology involves capturing a series of overlapping images of campus. We employed the Scale-invariant feature transform (SIFT) algorithm to extract feature points from each image, applied the KD-Tree algorithm for inter-image matching, and Enhanced autonomous threshold adjustment by utilizing the Random sample consensus (RANSAC) algorithm to eliminate mismatches, thereby enhancing feature matching accuracy and the number of matched point pairs. Additionally, we developed a feature-matching strategy based on similarity, which optimizes the pairwise matching process within the incremental structure from a motion algorithm. This approach decreased the number of matches and enhanced both algorithmic efficiency and model reconstruction accuracy. For dense reconstruction, we utilized the patch-based multi-view stereo (PMVS) algorithm, which is based on facets. The results indicate that our proposed method achieves a higher number of reconstructed feature points and significantly enhances algorithmic efficiency by approximately ten times compared to the original incremental reconstruction algorithm. Consequently, the generated point cloud data are more detailed, and the textures are clearer, demonstrating that our method is an effective solution for three-dimensional reconstruction.  
feature extraction and matching; image processing; three-dimensional reconstruction; structure from motion +
Footnote †: journal: Applied Sciences
# A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data
## 4 Discussion
### Limitations  
A significant limitation of this study is the availability of data for every phenological stage. Adverse meteorological conditions in a given region may hinder the acquisition of satellite imagery during one or more phenological stages. This issue can be partially mitigated by utilizing alternative satellite imagery sources (e.g., Landsat), which offer different revisit times. However, the limitation persists, as unfavorable weather conditions may still prevent the capture of high-quality data over the study area.  
Another limitation concerns the spatial resolution of freely available satellite data. In the case of very small fields, the presence of mixed pixels--where a single pixel encompasses multiple land cover types--may reduce model accuracy.  
Lastly, a critical limitation is the presence of weeds, as they can significantly alter vegetation index values, leading to results that do not accurately reflect true crop growth. Consequently, this may result in false predictions, as the AUC values will be artificially inflated.
# Role of Cyclin-Dependent Kinase 1 in Translational Regulation in the M-Phase
## 6 CDK1 Modulates LARP1 Activity  
CDK1 has been identified to phosphorylate the evolutionarily conserved RNA binding protein, LA-related protein 1 (LARP1) [13]. Recent studies revealed that LARP1 plays a key role in protein synthesis, as it was shown to regulate both the stability and translation of mRNAs characterized by a 5'TOP motif [90, 91]. LARP1 was identified as a potential mTOR complex 1 (mTORC1) substrate [92, 93] and LARP1 was shown to associate with the 3' end of TOP mRNAs via binding with poly(A) binding protein (PABP) [91, 94]. LARP1-bound mRNAs reveal that nonphosphorylated LARP1 interacts with both 5' and 3'UTRs of RP mRNAs and inhibits their translation [91]. Phosphorylation of LARP1 dissociates it from 5'UTRs and reduces its inhibitory activity to facilitate synthesis of ribosomal proteins, a subclass of TOP mRNAs. Thus, in response to CDK1 and mTOR activity, LARP1 serves as a phosphorylation-sensitive molecular switch for turning on TOP mRNA translation and subsequent ribosome biogenesis. Concomitantly, phosphorylated LARP1 scaffolds mTORC1 on the 3'UTRs of translationally-competent TOP mRNAs [91]. Depletion of LARP1 leads to mitotic arrest with a detectable increase in the level of cyclin B1 [95]. The effect of LARP1 depletion appears to be reduced cell growth and proliferation [90, 94], which is consistent with the activity of CDK1. In connection with this, LARP1 was found to be significantly upregulated in several malignancies [95, 96, 97, 98]. These findings suggest that elevated LARP1 expression might be linked to cellular proliferation.
# Experimental Investigation on Flow-Induced Rotation of Two Mechanically Tandem-Coupled Cylinders
## 3 Experimental Methods  
### Physical Model  
#### 3.1.1 Recirculating Water Tunnel  
All experiments were carried out in the recirculating water tunnel at Tianjin University. The test area of the tunnel had a width of 1.0 m, and a water depth of 1.34 m. The water in the tunnel was driven by a variable frequency power pump with a velocity range of 0.0-1.8 m/s, as shown in Figure 5. The differences in the flow velocity and turbulence in the vertical direction were small, which illustrated that the incoming flow in the test area of the circular cylinder was uniform [51]. Additionally, a rectifying grid was set in front of the test area to allow water flow to enter the test area smoothly. The test velocities were set at 0.229 m/s \(\leq U\leq 1.1\) m/s, with the corresponding Reynolds numbers of 20,105 \(\leq Re\leq\) 96,575. It should be noted that \(Re\) is defined as \(Re=UD/\
u\), where \(U\) is the characteristic velocity of the flow field, which is the test velocity in this paper; \(D\) is the characteristic length of the flow field, which is the diameter of the circular cylinder in this paper; \(\
u\) is the kinematic viscosity.
# Effect of Curing Conditions on Microstructure and Pore-Structure of Brown Coal Fly Ash Geopolymers
## 4 Results
### Compressive Strength vs. Curing Time  
The compressive strength in brown coal fly ash geopolymer mortar (50 mm cubes) and brown coal fly ash geopolymer concrete (both 50 mm and 100 mm cubes) at 8, 10, 12 and 14 h curing are shown in Table 4 and Figure 6. It is noted that the standard size laboratory scale concrete and mortar specimens were tested for compressive strength. The data shows an increase in strength with curing time, consistent with that observed in previous studies [28].  
The 7-day compressive strength of geopolymer mortar (50 mm cube) and geopolymer concrete (100 mm cube) varied from 4.6-31.0 MPa (4-14 h) and 1.1-30.85 MPa (4-24 h), respectively, with the mortar showing higher strength than the concrete at all curing times. The maximum 7-day strength obtained for the mortar and concrete, are 42.1 MPa and 30.85 MPa, respectively. These are comparable with the strengths achieved by a number of Class F fly ashes [33; 40; 41]. It is interesting to note that geopolymer mortar obtained its highest compressive strength after 12 h but subsequently reduced in strength from 12 to 14 h, losing 11 MPa or 26% of the strength. On the other hand, the 100 mm concrete specimen strength increased by 4 MPa in the same period and gained 16 MPa, more than doubling in strength, from 12 to 24 h.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Curing Time**} & \multicolumn{3}{c}{**Mean Compressive Strength (MPa)**} \\ \cline{2-4}  & **Mortar (50 mm cube)** & **Concrete (50 mm cube)** & **Concrete (100 mm cube)** \\ \hline
4 h & \(4.60\pm 1.05\) & \(3.05\pm 0.65\) & \(1.10\pm 0.45\) \\
6 h & \(23.00\pm 6.75\) & \(14.55\pm 2.75\) & \(8.75\pm 1.85\) \\
8 h & \(33.55\pm 6.75\) & \
# Compact Midwave Imaging System: Results from an Airborne Demonstration
## 3 Results
### CALIPSO Under-Flight  
The Hold D flight path covers a nearly homogenous cloud field above western Pennsylvania nearly simultaneously with an overpass by CALIPSO approximately at 19:00Z on 27 January 2021. For this case, we constrain the wind height using CALIPSO and solve for both wind vector components. This case provides a good example of the synergy between simultaneous aerosol-cloud LiDAR and stereo observations. Figure 14 shows the CMIS retrieval sites for Hold D1 with collocated CALIPSO vertical profiles.  
Typically, CALIOP detects clouds and aerosols at lower optical depths than passive imaging, and so CALIOP heights may be higher than those detected with stereo methods [10]. Fixing the mean height for the CMIS wind field at 1400 m based on CALIOP Layer-1 heights, we retrieve CMIS AT and XT winds over the collect with distributions as shown in Figure 15. These are rotated into U (+East) and V (+North) vector components to show mean wind vector values of (3.38, \(-\)4.39) m/s.  
Figure 14: (**a**) CMIS retrieval sites for Hold D1 are collocated in space and time with those of CALIOP and (**b**) CALIOP layer-1 cloud tops and bottoms show nearly constant CTHs in the curtain over the collect. A feature template is centered at each retrieval site indicated by the blue dots. CALIOP heights from the 333-m cloud-layer product have been referenced to the WGS-84 ellipsoid by adding the geoid height of \(-\)33 m at the center of the collect.  
Figure 13: Geometry of stereo calculation to calculate wind-corrected height. The factor 56 (m per m/s) is the time elapsed between observations.  
The Hold-D1 collection has an Aqua overpass approximately an hour earlier at 17:55Z that was used for MODIS and GOES-16 stereo winds to corroborate the CMIS results. A larger domain (41.25 \(\pm\) 1\({}^{\circ}\), \(-\)79.05 \(\pm\) 1\({}^{\circ}\)) is considered since the density of satellite retrievals is much less than those from CMIS and there is approximately a one-hour time mismatch. The distribution of MODIS-GOES stereo winds within this domain is shown in Figure 16. The mean CMIS retrieval of (3.38, \(-\)4.39) m/s falls near the most probable satellite U-V winds and its 5.54 m/s speed and mean height of 1400 m also falls near the most probable speed-height combinations.
# A Meta-Analysis on Dual Protocols for Chronic Stroke Motor Recovery: Robotic Training and tDCS
## 3 Results  
### Upper Limb Motor Functions: Short-Term Effects  
A random-effects model meta-analysis on the 20 comparisons revealed no significant short-term effects of combined tDCS and robotic training protocols on motor capabilities of the upper limbs post-stroke: \(SMD\) = 0.041; \(SE\) = 0.082; variance = 0.007; 95% CI = \(-\)0.120-0.202; \(Z\) = 0.504; \(p\) = 0.614 (see Figure 2). Heterogeneity tests indicated a relatively low variability across the individual effect sizes: (a) \(Q\)-value = 24.082 with \(p\) = 0.193; and (b) \(l^{2}\) = 21.104%. Further, the publication bias assessments showed minimal asymmetry levels of individual effect sizes: (a) a revised funnel plot with four imputed values after applying the trim and fill technique (see Figure S1) and (b) [PERSON]'s regression test: \(\beta_{0}=-1.095\) and \(p\) = 0.220. These findings indicate that robot training combined with tDCS showed no significant transient effects on the rehabilitation of the upper limbs post-stroke.
# 47.5 GHz Membrane-III-V-on-Si Directly Modulated Laser for Sub-pJ/bit 100-Gbps Transmission
## 5 Conclusions  
Intra data center networks and high-performance computing systems are expected to incorporate optical interconnects operating at beyond 100 Gbps/lane in the near future, in order to cope with a rapidly-growing data traffic and processing. To support such a growth with a low-power consumption levels, high-speed transmitters are in need, capable of operating at temperatures of 50 \({}^{\circ}\)C and above. However, energy-efficient DML-based transmitters have an inherent power-bandwidth tradeoff that limits their energy-efficiencies at higher modulation speeds.  
In this work, by introducing a DBR-based PPR-design on our previously-demonstrated low-power consumption membrane-III-V-on-Si laser platform, we have alleviated this power-bandwidth tradeoff, and have demonstrated > 100 Gbps PAM-4 signals over 2-km transmissions with 0.17 pJ/bit at 25 \({}^{\circ}\)C and 0.34 pJ/bit at 50 \({}^{\circ}\)C. Deploying such DMLs in an integrated multichannel transceiver should ensure a smooth evolution towards Terabit-class Ethernet links and on-board optics subsystems.  
Conceptualization, N.-P.D., T.K. and S.M.; methodology, N.-P.D., T.F., K.T., T.T., T.K. and S.M.; software, N.-P.D. and K.T.; validation, N.-P.D. and S.Y.; formal analysis, N.-P.D.; investigation, N.-P.D., S.Y., H.N. and K.T.; resources, S.M.; data curation, N.-P.D.; writing--original draft preparation, N.-P.D.; writing--review and editing, N.-P.D., T.K. and S.M.; visualization, N.-P.D.; supervision, S.M.; project administration, S.M.; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data available upon reasonable request.  
The authors declare no conflict of interest.
The deployment of wireless sensor networks (WSNs) is a realistic solution for many markets, such as manufacturing and environment monitoring, military and critical infrastructure monitoring, and, more recently, in energy-efficiency and healthcare sectors, due to their great capabilities in acquiring and transmitting data and processing them for different purposes. Current designs and architectures use radio channel(s) to share information between nodes and a gateway/hub, and implement embedded sensors with autonomous battery or low power microprocessors. Standard platforms, such as Telos B, and operating systems, such as TinyOS or Contiki, are used by a majority of stakeholders.  
Security, network topology, and communication protocol are critical issues in the current deployment of WSN applications. Different strategies should be developed according to the application requirements, such as distance, number of transmissions during a period of time, authentication needs, and rate of the frequency band, to name a few.  
This Special Issue is aimed at fostering the latest developments in the design, implementation, and evaluation in the field of WSN deployments.
###### Abstract  
Laser-induced breakdown spectroscopy (LIBS), accompanied by chemometric data analysis, is used to identify and classify gemstones of various hardness. The study involves several gemstones: amethyst, aquamarine beryl, bloodstone citrine, diopside, and enstatite. Their hardness is determined through a correlation utilizing the spectral intensity ratio of the ionic to atomic spectral lines of an identified element in the LIB spectrum. The result of the relative hardness obtained from the LIBS analysis is in good agreement with the hardness measured from Mohs's scale of hardness, a popular qualitative method to determine hardness. In this work, a linear relationship has been established between the Mohs's hardness and the plasma excitation temperature. Thus, the hardness of the gemstones can be determined with the help of plasma excitation temperature. Moreover, the analysis of trace elements in LIB spectral data reveals that a particular element is responsible for the colors of gemstones. Therefore, the relative concentration of constituents is calculated for all gemstones and compared. Principal component analysis (PCA) is successfully applied to all gemstone spectra for rapid classification and discrimination based on their variable elemental concentrations and respective hardness.  
laser-induced breakdown spectroscopy; LIBS; gemstone analysis; classification; principal component analysis; Mohs's hardness +
Footnote †: journal: Applied Sciences
## 1 Introduction
The pesticide-spraying process is a complex task that requires instant decision-making by the robot during a survey [27], in contrast to applications such as yield prediction [28] or disease detection [29], which can be performed separately using a high-performance computer. For that, limited choices on the suitable detection model are available. This study highlighted the strengths and weaknesses of YOLO versions that are already being implemented in robotic platforms for instant decision-making, offering a data-driven basis for selecting the most suitable model for pesticide spraying robots. For embedded systems with limited computational resources, YOLOv3 is the most suitable YOLO version in terms of memory and computational requirements compared with the later versions. Some studies, such as [11; 20; 22; 30], used embedded YOLO and affirmed a good balance between the speed and accuracy of YOLOv3 for real-time applications, even if newer versions existed. Based on that, this study aims to develop a real-time plant detection algorithm that can be implemented in an agricultural pesticide-spraying robot using an NVIDIA Jetson TX2 GPU. The developed method, using YOLOv3, was specifically applied to strawberry plants within a greenhouse environment. To assess the effectiveness of our implementation, we used the Open Neural Network Exchange representation (ONNX). In order to show the performance of our proposal, a set of real-world experiments was carried out in a didactical strawberry greenhouse, which revealed that the proposed model can reach a mAP of over 97% with a processing time of 15 ms per frame. This paper is organized as follows: Section 2 presents a brief discussion of the robot and YOLOv3 model that is used in our application. The main results are detailed in Section 3, and a section on the main conclusions ends this paper.
## 1 Introduction
### Motivation  
PD-NOMA offers several significant benefits over traditional MA techniques, such as enhancing capacity, increasing spectral efficiency, improving user fairness and QoS, mitigating interference and serving multiple users with the same RB, thus addressing the shortcomings of current communication infrastructure and directing future networks. Therefore, PD-NOMA flexible PA enables and prioritises eMBB, mMTC and URLLC TI applications/services with the required resources for seamless operation. However, research in PD-NOMA is needed to improve system performance further and address the aforementioned challenges, such as PA algorithms, hardware impairments, receiver design with imperfect SIC and CSI and the integration of PD-NOMA with other MA techniques to cater to the diverse needs of user devices. Subsequently, it is driven by the need for an efficient, flexible and user-centric communication infrastructure that meets the ever-growing demands of future networks.