esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The integration of millimeter wave (mmWave) and low frequency interfaces brings an unique opportunity to unify the communications and positioning technologies in the future wireless heterogeneous networks (HetNets), which offer great potential for efficient handover using location awareness, hence a location-aware handover (LHO). Targeting a self-organized communication system with autonomous vehicles, we conduct and describe an experimental and analytical study on the LHO using a mmWave-enabled robotic platform in a multi-radio environment. Compared to the conventional received signal strength indicator (RSSI)-based handover, the studied LHO not only improves the achievable throughput, but also enhances the wireless link robustness for the industrial Internet-of-things (IIoT)-oriented applications. In terms of acquiring location awareness, a geometry-based positioning (GBP) algorithm is proposed and implemented in both simulation and experiments, where its achievable accuracy is assessed and tested. Based on the performed experiments, the location-related measurements acquired by the robot are not accurate enough for the standalone-GBP algorithm to provide an accurate location awareness to perform a reliable handover. Nevertheless, we demonstrate that by combining the GBP with the dead reckoning, more accurate location awareness becomes achievable, the LHO can therefore be performed in a more optimized manner compared to the conventional RSSI-based handover scheme, and is therefore able to achieve approximately twice as high average throughput in certain scenarios. Our study confirms that the achieved location awareness, if accurate enough, could enable an efficient handover scheme, further enhancing the autonomous features in the HetNets.  
Keywords:dead reckoning; geometry-based positioning; indoor industrial environments; location-aware handover; mmWave communications; multi-radio access; radio positioning +
Footnote †: journal: Sensors
## 6 Conclusions  
To reveal the exact causes of the abnormal deformation of a construction bridge following Typhoon Lekima, an FE model considering the temperature field based on measurement data was established, and rehabilitation measures were proposed based on simulation and on-site monitoring. The conclusions are summarized as follows:  
1. The exact cause of the abnormal deformation of the bridge was the inconsistency of the temperature field between the top and bottom plates, and the sudden approach of the typhoon aggravated the inconsistency of this temperature field;
2. Abnormal deformation of the construction bridge was the result of a temperature decrease caused by the typhoon, and other abnormal weather conditions should be addressed with special treatments, such as replacing bearings by jacking the whole superstructure before forming the bridge because short-term recovery is difficult.
3. An extreme temperature field should be considered in the design of a beam-arch combination bridge. To prevent abnormal deformation of the beam-arch combination bridge caused by the extreme temperature field, controlling measures should be taken to ensure the uniform distribution of the temperature field inside and outside the steel box girder during the construction period, such as assembling the steel box girder in a construction shed with a constant temperature. These results can provide a reference for the design and construction of similar bridges.  
Conceptualization, investigation, Y.L.; software, Y.L. and L.Z.; analysis, Y.L. and M.F.; resources, M.F.; writing--original draft preparation, M.F. and Y.L.; writing--review and editing, Y.L. and B.W.; project administration, G.T.; funding acquisition, M.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51878277, 52068022, the Youth Science Foundation of the Education Department of Jiangxi Province, grant number GJJ180335, GJJ190341, and the Natural Science Foundation of Jiangxi Province, Grant number 20212 BAB204010).  
**Acknowledgments:** The authors would kindly like to thank all the collaborators and engineers working in the engineering background bridge, whose contributions played a key role in constructing the beam-arch combination bridge.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Evaluation of Potentially Robotic Traits
#### 3.2.3 Autoaggregation Assay  
The assessment of autoaggregation ability, next to hydrophobicity, allows for the estimation of the behaviour of the studied strains in the conditions of the digestive system, and values above eighty percent are considered high [14; 36]. Each of the studied strains demonstrated autoaggregation values above 80%, except for 110_Metsch_pulcherrima, which showed results at 78.96% after 24 h. However, the positive control, Sacch_boulardi, presented with very high results in the assay in all tested times (2, 4, and 24 h), with values of 30.71%, 50.02%, and 96.91%. Comparing these values to other tested strains, only three strains presented with better results than the control: 107_Metsch_pulcherrima (21.39%; 40.77%; 99.27%), 101_Pich_kudriavzevii (19.36%; 40.76%; 97.10%), and 058_Cand_metapislossi (53.58%; 70.39%; 98.41%). The results of the assay are presented in Figure 3. The _P. kudriavzevii_ strains isolated by [PERSON] et al. [13] showed a higher degree of aggregation, with most already reaching values of around 80% after 3 h, except for one strain. After 24 h, all strains aggregated in the range of 94.4-99.8%, which was similar to the reference strain of _S. boulardi_ (98.7%). A high degree of autoaggregation was also achieved by [PERSON] and [PERSON] [19] for its _P. kudriavzevii_ isolate, with results of 76% and approximately 100% after 2 h and 24 h, respectively. On the other hand, the _P. kudriavzevii_ IPNFG1 isolated by [PERSON] et al. [15] exhibited a high autoaggregation percentage after 15 min of incubation, reaching 79.2 \(\pm\) 6.6%. After 1 h, these yeasts demonstrated an autoaggregation ability exceeding 99%.  
Figure 3: Results of the autoaggregation assay of tested strains within 2, 4, and 24 h (Two-Way ANOVA: strain: F (27,168) = 580.26, \(p\) = 0.0000, se \(\pm\) 0.376682; time: F (2168) = 71,106, \(p\) = 0.0000, se \(\pm\) 0.123298; strain \(\times\) time: F (54,168) = 294.56, \(p\) = 0.0000, se \(\pm\) 0.652432).
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 4 Designs
### Node
#### 4.1.1 Node Virtualization  
Virtualization is a resource management technology, which realizes the goal of virtualizing a physical computer system into multiple virtual computer systems. The virtual computer system has a strong consistency with the real computer system. Typical virtualization methods include virtual machines and containers. Compared with the virtual machine, the container which relied on the Linux kernel features is more flexible and lightweight. Due to these advantages, the container-based virtualization is applied to realize the emulation node in the proposed platform. Moreover, container orchestration, such as Kubernetes [28], is employed to achieves functions that cannot be supported by native container-based virtualization. If needed, we can develop container orchestration ourselves. The Pod is the basic building block of Kubernetes. A Pod contains a group of one or more containers, such as Docker [29]. Containers in a Pod share some resources with each other, e.g., storage and network resources.  
As shown in Figure 3, an emulation node is a Pod which comprises an emulation container and a capture container. Network protocols or technologies which need to be validated are deployed at the emulation container. The capture container is responsible for real-time network measurement and full packets capture in simulation scenarios. Considering changeable simulation scenarios and different networks, researchers need to set network measurement points as needed. Via binding to each other in the form of Pods and sharing network resources, the capture container can measure and monitor all network information of the emulation container. Thus, the flexible configuration of network measurement on demand is realized.
## 2 Materials and Methods
### Characterization of LDH  
The morphology of LDH was observed by scanning electron microscopy (SEM, ZEISS ULTRA55) and transmission electron microscopy (TEM, JEM2100). The elemental composition of LDH was characterized by energy dispersive spectrometer (EDS), and chemical composition was obtained by Fourier transform infrared spectroscopy (FTIR, Nicolet IS10). The scanning range was from 4000 cm\({}^{-1}\) to 700 cm\({}^{-1}\), and the resolution was 4 cm\({}^{-1}\). Diamond was used as the crystal, and 16 scans were performed. In addition, the chemical composition was also obtained by X-ray photoelectron spectroscopy (XPS, PHI5000 VersaProbe III) using Al K\(\alpha\) source (1486.6 eV). The XPSPEAK 4.1 was used for peak fitting. The X-ray beam adopted high power mode (100 W, 20 kV), and the analysis area was 100 \(\upmu\)m \(\times\) 1400 \(\upmu\)m. The spectrum was corrected by shifting C 1s to 248.4 eV. X-ray diffraction (XRD, X'Pert PRO) test using Cu K\(\alpha\) radiation (\(\lambda\) = 1.5406 A) was carried out in the scanning range from 5 to 80\({}^{\circ}\) to characterize the crystalline structure of LDH. The applied voltage was 40 kV, and the current was 40 mA.
## 2 Overview of Related Works  
### VCD Approaches  
The famous related work is the TRECVID [13] VCD track. TRECVID is the international conference on benchmarking technology for content-based video indexing and retrieval. From 2008 to 2011, the VCD task ran for four years. Before that, deep learning networks had not been proposed, so the methods are all based on low-level content features. As shown in Table 2, the methods can be divided into keyframe-based and shot-based methods. Among them, the keyframe-based methods are mainly used but use of the shot-based methods is on the increase. Furthermore, using a fusion of fingerprints continues to show an upward tendency. Some of those methods use a fusion of features of global content, local content, and deep-learning-based features, and some of those methods use a fusion of features of video and audio. Figure 1: An illustration of the proposed image query based video searching/fingerprint detection process. SIFT, Scale-Invariant Feature Transform.
# Comparison of GPM IMERG and TRMM 3B43 Products over Cyprus
## 4 Results and Discussion
### Seasonal Validation  
The results for the seasonal validation are summarized in Table 3. Both TRMM 3B43 and GPM IMERG estimates presented high R values (0.91 and 0.90, respectively) in winter, which suggests a good agreement with ground-based measurements on a seasonal scale. Similar results were found for autumn (0.84 and 0.83, respectively) and spring (0.81 and 0.80, respectively), while the poorest correlation (0.68 and 0.67, respectively) was established in summer.  
The BIAS for TRMM 3B43 and IMERG ranged from \(-\)12.6 to 0.9 and from \(-\)8.3 to 2.6, respectively, in the four seasons. The 3B43 overestimated precipitation in spring (0.9) and autumn (0.3), while underestimation is noticed in summer (\(-\)1.3) and is rather significant in winter (\(-\)12.6). Similar is the pattern for IMERG, with overestimation in spring (0.8) and autumn (2.6), while underestimation is noticed in summer (\(-\)1.8) and is rather significant in winter (\(-\)8.3).  
In terms of rBias, 3B43 presented larger values than that of IMERG in spring (15.0 and 17.5, respectively) and autumn (6.4 and 18.4, respectively), while IMERG showed better performance in winter (\(-\)1.1 and \(-\)9.9, respectively). Both products displayed their worst values in summer (440.7 for 3B43 and 299.8 for IMERG, respectively).  
Precipitation products displayed a similar trend for RMSE and MAE with higher values in winter, spring and autumn and lower values in summer. For winter, spring and autumn, TRMM 3B43 had RMSE values of 33.4, 15.2 and 13.9, which were slightly lower than those of IMERG, which were 38.6, 19.1 and 16.7, respectively. In summer, 3B43 had lower RMSE values, 6.3, compared to those of IMERG, 8.7, respectively. Similar results occurred for MAE values between 3B43 and IMERG, with larger values noticed in winter (33.4 and 38.6, respectively) and lower in summer (3.8 and 4.9, respectively). These results may indicate that both SP products exhibit a similar error level, on a seasonal scale.
## 4 Technologies for Nanocellulose Production
### Alkali Pretreatment  
Alkali pretreatment mostly aids in the removal of lignin and solubilizes hemicellulose to a little extent. The alkali pretreatment is usually used for the LB which has a high lignin content [71; 72]. Alkali pretreatment disrupts the lignin carbohydrate complex by hydrolyzing ester, aryl-ether, alkyl-aryl linkages via the process of saponification and salvation reactions. The acetyl and uronic acid groups in hemicellulose are also removed using alkalis. Generally, sodium hydroxide, potassium hydroxide and calcium hydroxide are used in the biomass pretreatment for delignification [57; 71]. [PERSON] et al. reported the pretreatment of rice husk using a thermal-assisted alkaline approach. The pretreatment was effective in lignin removal (54%, \(w/w\)), mildly solubilized hemicellulose (32.65-51.65%, \(w/w\)) and increased the concentration of cellulose (51.65%, \(w/w\)) [73]. In another study, water hyacinth was subjected to NaOH pretreatment (5%), which was followed by bleaching treatment using 2% acidified NaClO2 for nanocellulose preparation. The pretreatment led to an enhanced cellulosic content (76.57%), and decreased non-cellulosic components (lignin and hemicellulose) [74].
# Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain
## 6 Discussion
### NPP in Current Condition
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Forest** & **LAT** & **LON** & \(h\) & \(T\) & \(R_{\text{g}}\) & **PRE** & **PET** \\
**Type** & (\({}^{\circ}\)) & (\({}^{\circ}\)) & **(m)** & (\({}^{\circ}\)C) & **(MJ m\({}^{-2}\))** & **(mm)** & **(mm)** \\ \hline
**EBF** & 43.6161 & \(-\)7.9821 & 225 & 13.75 & 4514 & 1366 & 856 \\
**LDBF** & 42.6964 & \(-\)2.4732 & 679 & 12.43 & 4855 & 743 & 921 \\
**HDBF** & 42.7946 & \(-\)2.0714 & 1135 & 12.47 & 5098 & 1018 & 962 \\
**LENF** & 40.3303 & \(-\)6.0982 & 574 & 13.91 & 5984 & 811 & 1220 \\
**HENF** & 41.8304 & \(-\)2.9821 & 1160 & 10.37 & 5513 & 676 & 923 \\ \hline \hline \end{tabular} LAT is latitude, LON is longitude, \(h\) is the elevation from sea level, \(T\) is the average mean annual air temperature, \(R_{\text{g}}\) is the average annual incoming global solar radiation, PRE is the average annual precipitation, and PET is the average annual potential evapotranspiration. Averages refer to the period considered in the present study (2005-2012) and were calculated from the datasets described in Section 3.3.  
\end{table} 
Table 7: Descriptive information of the forest sites selected for Figure 5.  
and available information otherwise its applicability is missed. Additionally, the accuracy of the NPP estimates is also strictly dependent on the quality of the drivers utilized, among which GSV is one of the most important [49]. GSV from NFI3 was currently used instead of the GSV map to evaluate the modeling strategy in the best possible conditions. As expected, greater relative errors were obtained for NPP than for GPP. Some differences in the results obtained among the five forest types can be appreciated. The greatest \(R^{2}\) and smallest errors were obtained for EBF, while the smallest \(R^{2}\) and greatest errors were obtained for HENF. This is in agreement with the results obtained in Italy by [49], who tested a similar NPP modeling approach against reference CAI data collected by the Italian National Forest Inventory. They also found a clear NPP underestimation for Mediterranean pines, which can be partly attributed to a suboptimal parameterization of the coefficients reported in Table 3. However, there is no conclusive indication that one GPP should be preferred over the other. Therefore, the use of GPP\({}_{\text{BGC}}\) in the calculation of NPP in future condition is justified.
# Design and Trajectory Tracking Control of a Magnetorheological Prosthetic Knee Joint
## 2 Design of MR Prosthetic Knee Joint  
A complete movement cycle can be divided into a support phase and a swing phase according to the classification of walking characteristics [25]. Figure 1 shows the reference swing curves of the hip and knee joints when a normal person walks on flat ground. It can be seen that the bending extension angle of the normal knee joint is 2\({}^{\circ}\)-15\({}^{\circ}\) in the standing stage, and the bending extension angle of the normal knee joint is 2\({}^{\circ}\)-60\({}^{\circ}\) in the swinging stage. The flexion and extension angle of the normal hip joint is \(-\)15\({}^{\circ}\)-22\({}^{\circ}\).  
Figure 2 shows the change of thigh swing angle \(\theta_{1}\) and calf swing angle \(\theta_{2}\) when a normal person walks on flat ground. It can be seen that the thigh swing angle \(\theta_{1}\) has a minimum value of \(-\)19.1\({}^{\circ}\) at the gait cycle of 52%, and the thigh swing angle \(\theta_{1}\) has a maximum value of 24.7\({}^{\circ}\) at the gait cycle of 0%. When the gait period is 67%, the calf swing angle \(\theta_{2}\) has a minimum value of \(-\)61.4\({}^{\circ}\), and the maximum leg swing angle \(\theta_{2}\) is 21.5\({}^{\circ}\) at the gait period of 0%.  
Figure 1: Swing trajectory of hip and knee joints.  
Figure 2: Swing angle of thigh angle \(\theta_{1}\) and calf angle \(\theta_{2}\).  
The thigh swing angle \(\theta_{1}\) and the calf swing angle \(\theta_{2}\) are fitted using Matlab software, and the fitting equation can be expressed as:  
\[\theta_{1}=\sum_{i=1}^{6}a_{1i}\sin(a_{2i}wt+a_{3i}) \tag{1}\]  
\[\theta_{2}=\sum_{i=1}^{6}b_{1i}\sin(b_{2i}wt+b_{3i}) \tag{2}\]  
where \(w\) is the movement frequency with a value of 1 Hz; \(t\) is the time with a value of 1 s; \(a_{1i}\), \(a_{2i}\), \(a_{3i}\), \(b_{1i}\), \(b_{2i}\) and \(b_{3i}\) are the fitting parameters. The specific fitting parameters are shown in Table 2.
## 2 Method
### Optimization Objectives
#### 2.2.2 Life-Cycle Cost  
This study employed the life-cycle cost as the index for evaluating economic indicators, encompassing the present values of both the initial investment and the costs incurred throughout the building's operational lifespan. In order to evaluate the economic feasibility of a building renovation project, it is essential to consider the initial investment and the life-cycle energy savings achieved once the program is implemented. Given that typical existing residential buildings constructed in severe cold climate regions in China during the 1980s have a typical lifespan of 70 years, it is crucial to consider the remaining operational lifespan when planning a renovation project; a renovated building typically has a lifespan of 20 to 40 years. Consequently, this study uses a 30-year life cycle and employs the net present value method to calculate life-cycle costs, incorporating the time value of money. The calculation formula is as follows:  
\[LCC=C_{inv,j}+\sum_{i=1}^{n}\frac{C_{m,j}}{(1+r)^{i}}+\sum_{i=0}^{n}a(r_{e},i) \cdot C_{a,j} \tag{2}\]  
\[C_{m,j}=8\%\cdot C_{inv,j} \tag{3}\]  
\[C_{a,j}=\frac{Q_{H}}{COP_{H}}\cdot\mu_{coal}\cdot P_{coal}+E_{C}\cdot P_{elec} \tag{4}\]  
where Cinv,j is the investment cost for the building retrofitting strategy; Cm,j denotes the cost of maintenance in the i-th year following the renovation; the annual maintenance cost is hypothesized to be 8% of the initial investment; C\({}_{a,j}\) is the annual cost for energy; \(\mu_{C}\) is the heating energy to coal conversion factor, with a value of 0.36 in this study; P\({}_{coal}\) is the local coal price; P\({}_{elec}\) is the local electricity price; and a represents the present value factor.  
The net present value (NPV) method was employed to calculate the life-cycle costs, incorporating the time value of money, inflation, and energy price escalation. The following formula was used:  
\[a=\frac{1-(1+r_{e})^{-i}}{r_{e}} \tag{5}\]  
\[r_{e}=\frac{r-e}{1+e} \tag{6}\]  
\[r=\frac{r_{i}-f}{1+f} \tag{7}\]  
where n is the number of years for the operation, assumed to be 30 in this study; i is the number of years that have elapsed since the original year; r\({}_{e}\) is the rate modified to account for market energy price escalation; r is the effective interest rate; r\({}_{i}\) is the interest rate, assumed to be 0.07 in this study; f is the inflation rate, assumed to be 0.02; and e is the cost of total energy escalation during the study period above the rate of inflation, assumed to be 0.012 in this study.  
These parameters were chosen to align with typical market trends in the region, aiming to capture both short-term and long-term economic impacts. While this study does not explicitly model material cost volatility, future research could incorporate sensitivity analyses to better understand the impact of fluctuating material prices on life-cycle costs.
## 2 Experimental Details  
The InGaN/GaN heterostructures were deposited on commercial AlGaN/GaN-on-Si HEMT templates (provided by Enkris Semiconductor, Inc., Suzhou, China) by using the metal-organic chemical vapor deposition (MOCVD). Trimethylgallium (TMG), trimethylindium (TMI), and ammonia served as the precursors, and nitrogen as the carrier gas. The process of MOCVD deposition is schematically shown in Figure 1a. Figure 1b is the structure of InGaN/GaN heterojunction on the HEMT template. The thickness of Mg-doped InGaN and GaN were 90 nm and 1 \(\upmu\)m, respectively, which were simulated for the maximum piezoelectric field. A thin unintentionally doped GaN spacer layer with the thickness of 5 nm was introduced before the InGaN deposition. Then a 20-min growth interruption in both nitrogen and ammonia ambient was introduced to polish the interface. The growth interruption can improve the interface quality, which was confirmed in our previous studies [13; 14]. Finally, a 5-nm-thick GaN cap layer was introduced to improve the surface morphology and screen the surface trap effects. During the InGaN deposition, a low Mg doping was performed to compensate the \(n\)-type background concentration. The growth temperature of the InGaN layers was varied from 760 to 780 \({}^{\circ}\)C. The InGaN/GaN heterostructure was also deposited on the GaN/sapphire template at the same growth condition as the reference. The crystalline qualities and strain states of the epitaxial structures were characterized by high-resolution X-ray diffraction (XRD) and reciprocal space mapping (RSM) using Panalytical Xpert PRO XRD system. All XRD peaks were calibrated using the standard peak of the bulk substrate (sapphire or Si). The photoluminescence (PL) measurement was carried out at room temperature with a He-Cd laser excited micro-photoluminescence system at 325 nm.