esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Schemes in FANETs: A Survey
## 3 Taxonomy
### Objectives  
There are three main routing objectives in FANETs:  
1. [label=O.0]
2. Enhancing route stability. In FANETs, route stability and route lifetime are lower because a) the network topology is highly dynamic due to high mobility degree and b) the distance between nodes in FANETs is comparatively long. Hence, frequent route discovery and maintenance are needed to reestablish routes. In addition, route stability increases with node density and residual energy.
3. Enhancing network coverage. In FANETs, node density is lower, so a large transmission range and network coverage is needed to reduce the effects of link disconnections and network partitions in order to improve network connectivity. In addition, network coverage increases with node density in multi-UAV scenario.
4. Enhancing routing performance and QoS. Since route stability is lower in FANETs, the routing performance reduces due to increased route discovery and maintenance. Hence, suitable routes (e.g., routes that provide high route stability and route lifetime, and routes with lesser number  
Figure 4: A taxonomy of routing attributes in FANETs.  
of hops that reduce the latency of data dissemination in an ad hoc manner) are identified and selected. In addition, QoS increases with node density and transmission range, which increases network connectivity.  
In general, route stability is higher, network coverage is higher, and routing performance improves:  
* higher QoS (e.g., higher throughput, as well as lower latency and packet loss);
* lower routing cost;
* longer route lifetime;
* lower latency of data dissemination in an ad hoc manner.
# Portable Smart Spectrometer Integrated with Blockchain and Big Data Technology
## 5 Blockchain System
### Related Applications  
The authenticity of spectral data is especially important if spectroscopy is applied to regulatory uses such as food inspection. For traditional IoT-based regulation methods, the regulated organizations may tamper with the measured spectral data and results in order to deceive the regulators, or the regulators may modify the measured spectral data and results themselves. The authenticity of the data is then questionable. Applying blockchain technology to regulatory uses can increase the credit of the data.  
Blockchain technology uses decentralized public ledgers to ensure reliable data and reduce the cost of data review, making it possible to make trusted transactions of spectral data possible, and the regulators can easily apply spectroscopy to the regulation of certain industries. Industries such as food production and industrial wastewater treatment can be effectively regulated using spectroscopy and blockchain technology. The regulators can work with the relevant regulated organizations to build a blockchain network, and the regulators install a smart spectrometer with blockchain technology like the PSS-1.0 in a specific area of the regulated organizations. The regulated organization must upload relevant spectral data to the blockchain network in real time, and the regulators and this regulated organization have the authority to record spectral data, or to record only the results of the spectral analysis. Other regulated organizations only record digest of the spectral data, including hash values, digital signatures, system IDs, time stamps. Then, no one can tamper with the spectral data, and it can protect the privacy of the regulated organization to some extent.
# Massive MIMO Indoor Transmissions at 38 and 65 GHz Applying Novel HBF Techniques for 5G
## 2 Description of the Experimental Setup
### Channel Sounder  
A Rohde & Schwarz ZVA 67 vector network analyzer (VNA) was used to measure the radio channel at both frequency bands. The system was based on measuring the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Rx Position** & **9** & **11** & **8** & **4** & **10** & **7** & **5** & **6** & **3** & **2** & **1** \\ \hline Tx–Rx Distance (m) & 0.95 & 1.44 & 1.56 & 2.15 & 2.26 & 2.40 & 2.70 & 3.17 & 3.60 & 3.80 & 4.10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distances between transmitting and receiving antennas for each Rx position.  
Figure 1: Location of Tx and Rx in the laboratory.  
transmission coefficient (\(S_{21}\)), which measured the insertion loss in the transmission. By placing one antenna in each port, separated by a given distance, the propagation channel was measured.  
In the case of 38 GHz, a broadband radio over fiber (RoF) link converter (reference EMCORE, Optiva OTS-2, 50 MHz-40 GHz) was used to separate both antennas. In the case of 65 GHz, two coaxial cables of lengths 4 m and 2 m with insertion losses of around 5 dB/m at 62 GHz were employed. In this case, to compensate for the attenuation of the cables, two amplifiers (reference HXI HLNA-465) with a 25 dB gain were used; see Figure 2.  
5.2 dB within the considered band, and the typical 3 dB elevation beamwidth varied from 24\({}^{\circ}\) to 33\({}^{\circ}\), while it was omni-directional in the horizontal plane.  
Finally, during the measurements, nobody was inside the environment to guarantee stationarity of the wireless channel. All the measurements were carried out in direct line-of-sight (LOS) conditions for all positions.
## 3 Design Optimization
### Performance Metrics & Design Criteria
#### 3.3.11 General Design Guidelines
Furthermore, any deviations \(\Delta\Gamma\) or \(\Delta U_{0}\) intimately affect the magnitude of the radial product \(\overline{r}\Delta r\). To stabilize both, \(\overline{r}\) and \(\Delta r\), the minimization of the band width \(\Delta\Omega\) thus typically involves widening of the radially inner segments near their meniscus positions \(r_{0}\) and \(R_{\text{crest}}\) assumed in the vicinity of the retention rate \(\omega=\Omega\). The results presented here for the optimizing valving structures \(\Gamma\) teach that the typical standard deviation \(\Delta\Omega\) of \(\Omega\) ranges in the order of \(\Delta\Omega/2\pi\approx 1\) Hz. To assess the impact on robust fluidic multiplexing, we consider the rotationally actuated serial release of \(N\) simultaneously loaded liquids, with each valve operating at a component-level reliability \(P_{M}=99.99\%\) for \(M=4\); in this case, the width of a band \(\Omega\pm M\cdot\Delta\Omega\) calculates to \(2\cdot M\cdot\Delta\Omega\approx 8\) Hz. Consequently, a frequency envelope spanning over 40 Hz between \(\omega_{\text{min}}/2\pi=15\) Hz and \(\omega_{\text{max}}/2\pi=55\) Hz could, in theory, assure a high system-level robustness of \(P_{M=4}^{N}\approx 99.96\%\) for \(N=5\) independent, rotationally controllable (high-pass) valves. However, this envelope is often \"squeezed\" into upper regions of the frequency space, i.e., reducing the frequency envelope \(\omega_{\text{min}}^{\prime}\gg\omega_{\text{min}}\). The new, higher boundary \(\omega_{\text{min}}^{\prime}\) may be attributed to an LUO occurring early in the liquid handling protocol of an assay, such as plasma separation, which would require an inner position \(R\) of \(\Gamma\), and thus a substantial critical spin rate \(\Omega\gg\omega_{\text{min}}\), to provide a sufficient field strength \(f_{\omega}\) (8) for guaranteeing swift and high-quality sedimentation. All simultaneously loaded, and later opened high-pass valves, accordingly, must accommodate their bands in this much narrower, upper-frequency corridor.
## 3 Results and Discussion
### Lipozyme RM IM-Catalyzed Synthesis of (S)-IBU-Glycerol Ester
Figure 10: AutoDock Vina representation of the best poses of (\(R\))-ibuprofen (**left**, purple) and (\(S\))-ibuprofen (**right**, blue) in the active site of _Rhizomucor miehei_ lipase (PDB ID: 4 TGL; catalytic triad: Ser144, His 257, and Asp 203). (**a,b**) \(d\) values computed for the distance between the acyl carbon of ibuprofen and serin oxygen of RM. (**c,d**) residues in the active site involved in binding interactions with ibuprofen. (**e,f**) Kyte–Doolittle hydrophobicity scale representation of the active site of RM with docked ibuprofen (blue, hydrophobic regions; red, hydrophilic regions). While previous research has explored ibuprofen-glycerol ester synthesis, our study introduces a novel catalytic approach using _Rhizomucor miehei_ lipase, yielding significant ibuprofen conversion rates. Unlike the widely used _Candida antarctica_ lipase B, RM IM displays distinct properties. To the best of our knowledge, this is the first article to report the selective esterification of (_S_)-ibuprofen with glycerol using the RM IM enzyme employing a molecular docking approach, assessing solvent effects, and fully characterizing the reaction product, thereby filling the gap left by previous literature. Our overarching objective is crafting selective (_S_)-enantiomer esters, yielding highly potent prodrugs with superior water solubilities compared to their glyceric counterparts.
## 5 Hybrid Localization Algorithms
### Distributed Localization Algorithms
#### 5.2.1 An Range-Free Scheme Based on Mobile Beacons (RSMB)  
An Range-free scheme based on Mobile Beacons (RSMB) is proposed in [68]. A mobile anchor node moves on the sea surface at a constant speed and broadcasts beacons at regular intervals, called beacon distance \(d\). Here, the mobile anchor node follows the random way-point (RWP) model [69]; the mobile anchor node moves in a series of straight paths to random destinations. Unknown nodes are installed with pressure sensors to obtain depth information. As shown in Figure 11, the unknown node can receive five beacons from the mobile anchor node. Since the unknown node knows its own depth information, the received beacons can be projected on the plane where it locates. RSMB consists of two steps. The first step is to select three beacons among the received beacons. The second step is to estimate the location of unknown node.  
In RSMB, each unknown node can estimate its own location independently. However, there are some problems of RSMB. First, localization time and accuracy mainly depend on the beacons' sending interval. Second, high energy is consumed for the anchor node's movement and the projection technology. Third, RSMB cannot localize unknown nodes in deep water. Furthermore, the authors do not research the path planning of the mobile anchor node, which severely affects localization accuracy.
## 3 Simulation Modeling of Dynamics for EMU Gearbox System
### CRH5 gearbox Rigid-Flexible Coupling Dynamic Modeling
#### 3.1.2 Working Environment and Model Setup  
The 3D model of the CRH5 gearbox is imported into ADAMS, and the material and mass properties of the solid unit are defined to realize multi-rigid body dynamics modeling of the CRH5 gearbox. Since it is necessary to analyze the vibration characteristics of the gearbox universal joints, driving gear, and driven gears in normal and fault states, the research object is softened to establish a rigid-flexible coupling dynamics model. The entire rigid-flexible coupled dynamics simulation model of the CRH5 gearbox is composed of three flexible bodies, including universal joints, the driving gear, and driven gears, and eleven rigid bodies, including the gearbox case, bushings, axes, and connecting shafts. The six bearings in the gearbox are modeled using the Adams Machinery module in ADAMS. The gearbox case and axle take one vertical degree of freedom to characterize the wheel-rail excitation; the rest of the components take three degrees of freedom in the longitudinal, transverse, and vertical directions; the whole system contains 38 rigid and flexible degrees of freedom.  
The View Flex module of ADAMS can generate flexures, but the generated flexures are only oriented to simpler components, and for complex components such as gears their accuracy is greatly reduced, which is not suitable for research needs. Therefore, by using ANSYS to divide the cross joints and gears into unit meshes that satisfy the calculation accuracy, performing rigid treatment at the connection of components as connection nodes, saving the modal analysis calculation results into.mnf format files, and importing the components into ADAMS for further parameterization, the rigid-flexible coupling dynamics model of the CRH5 gearbox is constructed.
# The Mechanical Properties of Reinforced Concrete Columns with Longitudinal Pre-Embedded Holes
###### Abstract  
While the longitudinal pre-embedded holes in a reinforced concrete column have a variety of beneficial functions during the whole life of the building, they have certain negative influences on the mechanical properties of the column. To investigate the influences of longitudinally pre-embedded holes on the mechanical properties of reinforced concrete (RC) columns, numerical simulations were conducted using the finite element software ABAQUS 2021 to analyze the effects of various parameters, including hole diameter, concrete strength, stirrup ratio, and slenderness ratio, on the mechanical behavior of RC columns under axial pressure. The results show that the presence of longitudinally pre-embedded holes reduces the load-bearing capacity of the columns. Furthermore, when the hole diameter exceeds 75 nm and the concrete strength is over C35, the failure mode of the columns shifts from axial compression failure to shear failure at the bending section of the pre-embedded hole. Increasing the stirrup ratio effectively enhances the ductility and load-bearing capacity and avoids brittle failure, whereas the influence of slenderness ratio variations on the column's bearing capacity is negligible. These results provide a theoretical basis for the safe design of longitudinally pre-embedded hole columns in green buildings, effectively balancing the requirements between structural lightweight design and load-bearing performance.  
longitudinally pre-embedded holes; reinforced concrete columns; mechanical properties; finite element analysis Article
# Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint
## 2 Smart Mobility Development
### Current Goals  
The vision of Smart Mobility consists of many elements, which transform the philosophy of transport into new possibilities, as shown in Figure 2, and offer a possible solution to the current situation. According to [7], the Sustainable and Smart Mobility Strategy presented by the European Commission, important milestones for Smart Mobility include:  
* By 2030: \[\circ\] multimodal passenger transport will be deployed facilitated by an integrated electronic ticket and freight transport that will take place in a so-called paper-less form; \[\circ\] automated mobility will be deployed on a large scale, emissions will be reduced by 55%; \[\circ\] at least 30 million vehicles on European roads will be zero-emission vehicles; \[\circ\] high-speed rail transport will be doubled; \[\circ\] there will be carbon-neutral collective travel within Europe within 500 km.  
By 2050:  
\(\circ\): rail freight transport will be doubled;  
\(\circ\): a multimodal Trans-European Transport Network (TEN-T) for sustainable and intelligent transport with high-speed connectivity capable of operating on the comprehensive network will be built;  
\(\circ\): climate neutrality (reducing transport's dependence on fossil fuels);  
\(\circ\): high-speed rail transport will be tripled;  
\(\circ\): almost all cars, buses and vans will have zero emissions.  
The intention is to integrate connected and automated multimodal mobility into real transport. Interconnected and automated systems have great potential to improve the operation of transport systems, their sustainability and safety. Smart Mobility offers a solution that, based on real-time data, could evaluate and optimize the current traffic situation in the form of traffic intensity management on a certain section or provide information on the number of freely available parking spaces.  
Not only the European Commission, but also the Smart Mobility concept itself has defined objectives such as:  
* Prevention of road accidents.
* Ensuring smooth transport and thus reducing transport times.
* Reduction of capacity congestion in transport infrastructure.
* Enhancing safety.
* Improving the economic efficiency of transport.
* Reduction in the number of means of transport for individual transport [14].
# Globally Optimal Facility Locations for Continuous-Space Facility Location Problems
## 3 Location Estimation Based on the Manhattan Distance
### Properties of the Minimizer
#### 3.3.4 Example 4  
In this case, the weighted median on the axes are given by  
\[\widetilde{u}=\text{ weighted median }(x_{1},\ x_{2},\ x_{3})=3,\ 5,\text{ or }4 \tag{31}\]  
\[\widetilde{v}=\text{ weighted median }(y_{1},\ y_{2},\ y_{3})=3 \tag{32}\]  
Thus, there are three black hollow squares in Figure 1d, where any of them can be considered as the optimal location for the facility.  
As presented in Figure 1 for Examples 1-4, we can conclude that the globally optimal facility location is based on the coordinate values of the observations on different axes. After constructing the set of mesh points that are consist of the coordinate combinations, it is obvious that the optimal single-facility location is an element from the set of mesh points. Then, we have the following formula  
\[(\widetilde{u},\widetilde{v})\in\left\{(x_{i},y_{j}):i,j=1,2,\ldots,n\right\} \cup\left\{\left(\frac{x_{i}+x_{i+1}}{2},\frac{y_{j}+y_{j+1}}{2}\right):i,j=1,2,\ldots,n-1\right\} \tag{33}\]  
Similarly, if we choose only one minimizer among \(x_{(k)}\) and \(x_{(k+1)}\) for the second case in (39), the optimal single-facility location is also an element from the other set of mesh points, which is given by  
\[(\widetilde{u},\widetilde{v})\in\left\{(x_{i},y_{j}):i,j=1,2,\ldots,n\right\} \tag{34}\]  
Note that the number of candidate locations in (34) is smaller than that in (33) but the objective function obtained through (34) is the same as that through (33). In this sense, the number of candidate locations in (34) is suggested in this study.
## 4 Theoretical Framework
### Time Series Modeling
#### 4.2.5 SARIMA Model  
The SARIMA model, autoregressive and integrated of the seasonal moving average, is based on the ARIMA model, with some of its coefficients at zero and additional components to integrate the seasonal behavior of the series in the model. The SARIMA model has the following notation:  
\[\Phi_{p}(L)\Phi_{p}(L)(1-L^{s})^{D}(1-L)^{d}(Y_{t}-\mu)=\Theta_{Q}(L)\Theta_{q} (L)u_{t}. \tag{5}\]
## 2 Experimental Section
### Catalyst Characterization  
The phase purity and crystallinity of the catalysts were studied by a powder X-ray diffractometer (XRD, D/Max Ultima IIIdiffractometer, Rigaku Corporation, Tokyo, Japan) equipped with monochromatic Cu K\({}_{a}\) radiation (wavelength, \(\lambda\) = 0.154 nm) operated at 40 mA and 40 kV. The surface morphology was analyzed using field emission scanning electron microscope (FESEM, JEM 1200 EX II, JEOL, Tokyo, Japan) and transmission electron microscopy with EDX attachment (TEM, TECNAI F20, Philips Corp., Eindhoven, The Netherlands). Identification of the basic chemical functional groups (and quantification of CO\({}_{2}\) during CO oxidation experiment) was performed by FTIR spectroscopy (IFS 66/S spectrometer, Bruker, Billerica, MA, USA). In addition, the surface of the catalysts was also characterized by using an X-ray photoelectron spectroscopy (XPS, ESCA 2000, VG Microtech, West Sussex, UK) with monochromatic Mg K\({}_{a}\) X-ray radiation (1253.6 eV) operated at 13 kV and 15 mA as an excitation source. The Quantachrome software (version 2.11, Quantachrome Instruments, Boynton Beach, FL, USA) was used to determine the surface area and pore characteristics of the catalysts. Chemisorption measurements (temperature programmed reduction (TPR) experiments) were conducted using a gas sorption analyzer (Autosorb iQ, Quantachrome Instruments, Boynton Beach, FL, USA) to predict the catalyst dispersion. In this test, 100 mg of catalyst and a mixture of H\({}_{2}\) and Ar (10%) were used. The samples were pretreated and the TPR profile was recorded from room temperature to 600 \({}^{\circ}\)C at a steady ramping.  
Exactly 10 g of the catalyst prepared as above was loaded in a tubular reactor (diameter: 1.5 cm) and then fixed inside a furnace equipped with a temperature controller. The feed gas was prepared by mixing CO, oxygen and nitrogen. The concentration of CO was fixed at 2000 ppm (parts per million, volumetric), and the oxygen concentration was varied from 0.5 to 2.5 vol.% with the total flow rate kept at 1 L\(\cdot\)min\({}^{-1}\). The volume of the catalyst inside the reactor was 11.48 cm\({}^{3}\), corresponding to a space velocity of 5226 h\({}^{-1}\) at a feed gas flow rate of 1 L\(\cdot\)min\({}^{-1}\). All experiments were performed under isothermal conditions (the feed gases were introduced immediately after the required temperature was attained). The difference between the temperature measured at the catalyst using a thermocouple and that recorded by the temperature controller was negligible. The concentration of CO at the outlet was measured by using the FTIR spectrometer after 1 h of the reaction.
## 5 Conclusions  
In this paper, a data augmentation method based on an improved Deep Convolution Generation Adversarial Network for gas turbine rotor system fault diagnosis is proposed. This method utilizes the powerful feature extraction and data generation ability of GAN to generate effective fault samples for fault diagnosis. The main highlights are summarized as follows.  
Firstly, based on the Deep Convolution Generation Adversarial Network, an Improved DCGAN method combining the data generation ability of DCGAN with the feature extraction ability of WDCNN was proposed, and a new signal generator more suitable for gas turbine rotor fault signals was constructed.  
Secondly, a gradient penalty was adopted to adaptively optimize the loss function according to different gas turbine rotor system faults under unknown conditions.  
Finally, the effectiveness of the method was verified by gas turbine rotor system fault experiments. The results indicate that the classification accuracy improved greatly with the proposed method compared to the GAN method and the DCGAN method, and the proposed method can generate effective fault samples to solve imbalances of fault samples.  
In the proposed Improved DCGAN method, the improvement of the generator makes the calculation process more complex, as it needs to be calculated by high-performance computers and this consumes more time compared with GAN and DCGAN. In further work, the attention module could be introduced to enhance the extraction of fault features. In addition, the Improved DCGAN network structure optimization could be improved to shorten the consuming time for fault diagnosis.  
To sum up, the proposed method effectively supplements fault samples, solves the problem of small samples and unbalanced samples, improves the fault diagnosis accuracy, and provides an effective method and data supply for fault diagnosis of gas turbine rotor systems. Meanwhile, the method makes it more convenient for managers to understand the operation status of gas turbines, formulate the evaluation criteria of maintenance and repair, and save costs associated with suspending detection and maintenance. In the future, different sensor signals such as vibration, temperature, and flow of gas turbines can be combined for multi-source data fusion to further improve the diagnosis accuracy of gas turbines.  
S.L., methodology and writing; H.W., review and formal analysis; X.Z., data curation and field inspection. 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 (No. 51975058).  
Not applicable.  
The data presented in this study are available from the corresponding author upon reasonable request.  
The study was approved by the Beijing Information Science and Technology University.  
The authors declare no conflict of interest.