esa-sceva/satcom-chunk-collection huggingface dataset:
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 4 Discussion and Research Directions towards Green LoRaWAN Protocol
This could be complemented with the usage of sectorial antennas at the end devices so as to avoid collisions. Having, as a basis, the aforementioned discussion, we present hereafter the basic research directions towards a green LoRaWAN communication protocol, endeavoring to also address robustness, scalability, and reliability issues. A green LoRaWAN communication protocol: The motivation of the design of a green LoRaWAN communication protocol lies in the fact that they exist several practical scenarios in which LPWANs need to be installed and operate in harsh environments, (e.g., precision agriculture) without or intermittent power supply from electric grid. Inspired by this, we have turned our attention on the energy longevity of network GWs. We strongly believe that the design of a green, robust, and resilient communication protocol, which aims at maximizing access network lifetime, through minimization of energy consumption at the IoT end devices and GW, while also enhancing network robustness, reliability, capacity, as well as scalability and avoiding network collapse events due to exhaustion of GWs' energy resources, is an important topic that demands further investigation. Currently, in LoRaWAN technologies uplink packets sent by end-devices are received and forwarded by all GWs within range. In contrary to prior related research that propose an association of end-devices to GWs only for the downlink direction [11], we aim at the development of a communication protocol, which considers the association of end-devices to a single GW at both uplink and downlink directions, taking into account the available energy resources of each GW at the specific time [79]. In more detail, we change the network topology and dynamically a list of end-devices accosiated to each GW. Packets generated by end-devices associated with the GW will be forwarded, while other end-devices packets are rejected. The algorithm was designed to balance the network backhaul transmission load between network GWs depending on the available residual energy. This accosiation will be complimented with an efficient resource allocation mechanism by defining optimal LoRaWAN transmission parameters (SF, TP) in order to minimize energy consumption and prolong network's lifetime, needs to be developed. This approach is expected to minimize the data transmitted from GWs to the \(NS\), which result in energy consumption reduction. Additionally, the use of sectorized antennas on end-devices is considered to reduce collisions and further reduce energy consumption due to receiving multiple packets, while end-devices will be informed each day on the selected GW and the sector to be activated. Finally, the integration of a multi-access mechanism will be discussed in order to further reduce collisions.
## 4 Results
### Classification of Intention Generation  
Under the circumstance that the above analysis results in physiological characteristics such as ECG, EDA, and RESP by ESAX, here the intelligent icon method is used to represent these physiological characteristics. In this section, these physiological characteristics will be used to identify whether the pilot has intentions during the flight simulation. Moreover, the typical characteristic pattern in the intention generation process is tried to identify. CSA and ELM are coupled together in paper. The input weight and bias of ELM are optimized by CSA to identify whether the pilot has an intention. The specific steps are as follows.  
(1) Determination of network structure  
Figure 6: Discretization of physiological signal time series. (**A**) Electrocardiogram, (**B**) Electrodermal activity, (**C**) Respiration.  
According to the physiological characteristics data obtained above, the number of neurons in the input layer is determined to be three. The simulation value corresponding to whether each sample generates intention is used for network output. Namely, there are two neurons in the output layer. An identification model including three inputs and two outputs is constructed.  
Sigmoid is the best activation function and is a key to build the most powerful ELM. In this paper, the Sigmoid function is used as the excitation function of the ELM model, as shown in Equation (16) [38]:  
\[g(x)=\frac{1}{1-e^{-x}} \tag{16}\]  
After the activation function is determined, the performance of ELM is only affected by the number of hidden-layer neurons. Here, K-fold cross validation (K = 10) is adopted to determine the best number of neurons in the competitive layer through a large number of experiments. The effect of the number of hidden-layer neurons on ELM performance is illustrated in Figure 7. It can be seen from the curves of \(RMSE\) and \(R^{2}\) in Figure 7 that the fitting effect of ELM on the training sample set is improved with the increase of the number of hidden-layer neurons. When the number of hidden-layer neurons is more than 100, ELM could approximate all training samples with no error. However, according to the curves of \(RMSE\) and \(R^{2}\), when the number of hidden-layer neurons increases, \(R^{2}\) presents the trend of first increasing and then decreasing, while \(RMSE\) first decreased and then increased. Thus, it suggests that there should not be too many or too few neurons in the hidden layer. Too many hidden-layer neurons will lead to overfitting, while too few may lead to underfitting, which will reduce the generalization ability of ELM. Therefore, the appropriate number of hidden-layer neurons should be selected to ensure that the network has a good generalization ability. In this paper, in the light of Figure 7, the number of neurons in the hidden layer is 53.
## 2 Materials and Methods
### Strains, Plasmids, and Media  
Beta-cypermethrin-degrading bacteria were previously isolated from a sewage disposal pool at Guangxi Chemistry Institute and identified as _B. cereus_ BCC01 by our group [20]. This strain was stored at \(-80\)\({}^{\circ}\)C and thawed before use. _Escherichia coli_ DH5\(\alpha\) and _E. coli_ BL21 (DE3) were purchased from Tiangen Biotech (Beijing) Co., Ltd., Beijing, China, and were used as cloning and expression host cells, respectively. The plasmids pUC18 and pET-32a (+) were purchased from Novagon Co., Ltd. and Takara Biomedical Technology Co., Ltd., Beijing, China, and were used as cloning and expression vectors, respectively. For genomic library construction and protein expression, a mineral salt medium (MSM) containing (g/L) 1.0 NH4 NO3, 0.5 NaCl, 0.5 (NH4)2 SO4, 0.5 KH2 PO4, and 1.5 K2 HPO4, and Luria-Bertani (LB) medium (g/L) containing 10.0 peptone, 5.0 yeast extract, and 10.0 NaCl were used. pH was adjusted by using 10% NaOH.
###### Abstract  
The tribomechanical properties of the wear-resistant ultrahigh molecular weight polyethylene (UHMWPE)-based composites loaded with wollastonite microfibres silanized with various coupling agents (\"KH-550\", \"Penta-1006\", and \"OTS\") were investigated. It was demonstrated that the mechanical properties of UHMWPE-based composites filled with various amounts of wollastonite (7-23 wt. %) increased by 1.3 times (yield strength) and by 1.8 times (elastic modulus), while the wollastonite silanization further improved yield strength by 9% in some cases. It was demonstrated that the composite loaded with 23 wt. % wollastonite silanized with the \"KH-550\" coupling agent possessed the maximum wear resistance under \"moderate\" conditions of tribological loading. Under \"severe\" conditions, the composites containing 23 wt. % wollastonite silanized with the less efficient \"OTS\" and \"Penta-1006\" agents showed the greatest wear resistance during dry sliding friction. Wear resistance significantly depended on filler weight fraction and the load-speed mode of the tribological tests. Based on the obtained experimental data on the mechanical (including impact toughness) and tribological properties of the UHMWPE-based composites loaded with wollastonite, the optimal compositions (the filler content and the type of the coupling agent) for two load-speed modes were designed using the developed computer algorithm. The composites provided the predefined high tribomechanical properties for operation in the metal-polymer friction units compared to neat polymer.  
Keywords:ultra-high molecular weight polyethylene; wollastonite; silane coupling agent; strength; adhesion; wear resistance; permeacular structure +
Footnote †: journal: Applied Soft
# A Survey on Blockchain-Based Search Engines
## 5 Blockchain-Based Search Engines in Smart Environments  
A smart environment is a sophisticated setting with dynamic devices that create an \"intelligent entity\". In these environments, because computational power is incorporated in the space, it is critical to explore its perspective and interactions with the surrounding world [54; 55]. In this context, edge computing provides sound solutions to support analytic data applications, high-performance computing, and big data storage. Edge computing is a new distributed computing paradigm that promises to bring processing and data storage closer to where they're needed, saving network traffic and lowering request latency [56].  
Search engines for smart environments are generally different from standards. Heterogeneity, interacting with other apps, a wide range of usage scenarios, a vast number of data exchange protocols, and data privacy are the critical challenges in smart settings [57]. Therefore, we need a distributed search system with unique capabilities to provide context-aware and personalized search results.  
Blockchain-based search engines can provide an excellent solution to support personalized search results to the privacy principles. In edge computing, an emerging distributed computing paradigm in smart environments, considering the continuous increase in edge  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Nebulas** & **BitClave** & **Presearch** & **DeSearch** \\ \hline \multirow{2}{*}{**System Scale**} & Decentralized & Decentralized & Decentralized & Decentralized & Decentralized Crypto \\  & ecosystem & marketplace & ecosystem & Search Engine \\ \hline \multirow{3}{*}{**Searching Content**} & DApps, smart & Customer, businesses, & & & Cryptocurrencies, Web \\  & contracts, and info. & Customer, businesses, & & & pages, Telegram \\  & contracts, and info. & and ads. & & & networks, BT, company, \\  & & & & & and influencer medium \\ \hline \multicolumn{2}{c}{**Cryptocurrency**} & Not reported & Not reported & PRE & Not reported \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of existing blockchain-based search engines.  
capabilities such as processing power and storage space, the approach transfers the processing from the center of the cloud to the edges. More context-aware and personalized search results can be provided by processing the user's background data and the information about the user context. In context-based services, the disclosure of user's data and privacy violations are the fundamental problems. In the blockchain-based search engines, encrypting the data, storing it on edge, and processing locally, user privacy will be provided. Additionally, using the edge processing power and storage capacity, the overall search performance will be improved.
## 6 Discussion  
In order to illustrate the effectiveness of the proposed approach, a controller for a simple system responsible for packing three components coming from three different conveyors (\(A\), \(B\), and \(C\)) is used. Referring to Figure 10, conveyor \(X\) will be moving if the output \(ConvX\) (with \(X\) belonging to \(A\), \(B\), and \(C\)) is active (places \(p1\), \(p2\), and \(p3\) marked). The movement of each conveyor will stop whenever transitions \(t1\), \(t2\), or \(t3\) fire. These transitions have an \(Up\) event associated with three dedicated events (generated by the activation of a limit switch). Whenever the three components arrive at the packing area, the operator activates a pedal associated with signal \(d\), which in turn generates the \(UpDownTrigger\) event associated with transition \(t4\), launching the packaging mechanism and returning afterward to the initial state after releasing the pedal.  
Figure 9: The operational models associated with the events listed in Table 2 to be executed concurrently with the model specified by the designer: (**a**) Up event, (**b**) UpTrigger event, (**c**) UpHyst event, (**d**) UpHystTrigger event, (**e**) Down event, (**f**) DownTrigger event, (**g**) DownHyst event, (**h**) DownHystTrigger event, (**i**) UpDown event, (**j**) UpDownTrigger event, (**k**) UpDownHyst event, (**l**) UpDownHystTrigger event, (**m**) DownUp event, (**n**) DownUpTrigger event, (**o**) DownUpHyst event, and (**p**) DownUpHystTrigger event.  
It is also important to note that if the use of events is not available, the model of the system would be similar to the one presented in Figure 11a, composed of twelve places and ten transitions. By using events, the model of the system can be described employing seven places and five transitions, as presented in Figure 10 and the top left of Figure 11b. In this sense, the user benefits from the compactness of the model and improved readability. However, from the point of view of the execution of the model, the five models presented in Figure 11b are executed concurrently (even the user only sees the one in Figure 10).  
The proposed event modeling strategy is foreseen to be supported by the IOPT-Tools framework, freely available at [[http://gres.uninova.pt/IOPT-Tools/](http://gres.uninova.pt/IOPT-Tools/)]([http://gres.uninova.pt/IOPT-Tools/](http://gres.uninova.pt/IOPT-Tools/)) (accessed on 3 June 2024).
## Materials and Methods
### Characterization Techniques  
The crystal structure of the powders was examined at room temperature using a Bruker X-ray diffraction instrument (XRD, D\({}_{2}\) Phaser, Billerica, MA, USA) with Cu-K\(\alpha\) radiation of 1.54059 A wavelength. The nanostructure and surface morphology of the powders were studied using a JEOL2100F field emission transmission electron microscope (TEM, JEOL, Tokyo, Japan) at 200 kV and also an Ultraplus field-emission scanning electron microscope (FESEM, Zeiss, Oberkochen, Germany) at 1 kV. The specific surface area was measured by the Brunauer-Emmett-Teller (BET) N\({}_{2}\)-gas adsorption method using a TriStar II 3020 system (Micromerities, Norcross, GA, USA). Prior to BET measurements, the powders were degassed under vacuum at 150 \({}^{\circ}\)C for 24 h. The bandgap energy of samples was estimated from UV-Vis optical absorption spectra taken using a Cary 60 spectrophotometer (Agilent, Santa Clara, CA, USA). Optical transmittance of the samples coated on a quartz plate was measured and the absorption spectra were converted into the Tauc plot of (hv\(\alpha\))\({}^{2}\) versus hv, where \(h\) is the Planck's constant, \(\
u\) is the photon's frequency, and \(\alpha\) is the optical absorption coefficient. The linear zone of the Tauc curve was extrapolated to the energy axis to estimate the direct bandgap. X-ray photoelectron spectroscopy (XPS) was conducted using a SPECS PHOIBOS 100 Analyser installed in a high-vacuum chamber with a base pressure below 10-8 mbar. An incident monochromated X-ray beam of Al-K\(\alpha\) radiation with a photon energy of 1486.6 eV was used at 12 kV and 144 W. XPS binding energy spectra were recorded at a pass energy of 20 eV and a step width of 0.05-0.1 eV in the fixed analyser transmission mode. To increase the signal/noise ratio, multi-scans were conducted and the signal intensity was accumulated. Broad survey scans were recorded at a pass energy of 60 eV and a step width of 0.5 eV. XPS data analysis was carried out using the commercial CasaXPS software package.
# Impact of Satellite Attitude on Altimetry Calibration with Microwave Transponders
## 4 Results
### Jason-3 Attitude Effects on Transponder Results
The attitude angles of pitch and roll seem not to significantly influence the datation as shown in Figure 10.  
Figure 9: Time series of Jason-3 attitude effects on range (**top**) and datation (**bottom**) biases. Another spectral analysis on \(\delta B\) and \(\delta T\) is also performed to examine any potential propagation of periodic effects of attitude angles to biases. The spectral analysis is shown in Figure 11, and it can be seen that the periods with the greatest amplitude (in descending order of magnitude) are: 39, 23 and 117 days. These periods have already been revealed by the spectral analysis of the angles shown in Figure 8. This resemblance in periodicities corroborates the existence of effects on biases coming from attitude variations.
## 3 Methodology
### Evaluation Criteria  
This study employed quantitative evaluation criteria to evaluate both SPPs and simulated discharge based on the observation data. The selected indicators were the correlation coefficient (CC), bias (\(BIAS\)), mean absolute error (\(MAE\)), root mean square error (\(RMSE\)), and Nash-Sutcliffe Efficiency (\(NSE\)) [Equations (7)-(11)]. In this study, the evaluation of precipitation was calculated in the basin-scale.  
\[\text{CC}=\frac{\sum_{i=1}^{n}\left(X_{i}^{\text{o}}-\overline{X^{\text{o}}} \right)\left(X_{i}^{\text{s}}-\overline{X^{\text{s}}}\right)}{\sqrt{\sum_{i=1} ^{n}\left(X_{i}^{\text{o}}-\overline{X^{\text{o}}}\right)^{2}}\sqrt{\sum_{i=1} ^{n}\left(X_{i}^{\text{s}}-\overline{X^{\text{s}}}\right)^{2}}} \tag{7}\]  
\[BIAS=\frac{\sum_{i=1}^{n}\left(X_{i}^{\text{s}}-X_{i}^{\text{o}}\right)}{n} \tag{8}\]  
\[MAE=\frac{\sum_{i=1}^{n}\left|X_{i}^{\text{s}}-X_{i}^{\text{o}}\right|}{n} \tag{9}\]\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\bigl{(}X_{i}^{\mathrm{s}}-X_{i}^{\mathrm{o}}\bigr{)} ^{2}}{n}} \tag{10}\]  
\[NSE=1-\frac{\sum_{i=1}^{n}\bigl{(}X_{i}^{\mathrm{S}}-X_{i}^{\mathrm{O}}\bigr{)} ^{2}}{\sum_{i=1}^{n}\bigl{(}X_{i}^{\mathrm{O}}-\overline{X}_{i}^{\mathrm{O}} \bigr{)}^{2}} \tag{11}\]  
where \(X^{\mathrm{O}}\) is observed data (precipitation or discharge), \(X^{\mathrm{S}}\) is simulated data (original and bias-corrected SPPs or simulated discharge), \(i\) is the number of gauge stations or time series of runoff, \(n\) is time steps in the evaluation period.
## 4 Results and Discussion
### Parameter Study
Figure 9 illustrates the effects of the same parameters on the recompression cycle, respectively. The change trend shown in Figure 9a-c are, basically, similar to the above two cycles, the only difference is that with the turbine inlet pressure rising, the increase speed of overall efficiency slows down. For the recompression cycle layout, overall efficiency and the specific work have a similar trend--to intercool the cycle with the rise of the compressor inlet pressure. Figure 7: Effects of the turbine inlet temperature (**a**), turbine inlet pressure (**b**), compressor inlet temperature (**c**), and compressor inlet pressure (**d**) on specific work, overall efficiency, and cost of core equipment (simple recuperated cycle). It can be seen from the results of the parameter study that the performance of the system is obviously affected by several parameters, and the influence of the compressor inlet temperature and  
Figure 8: Effects of the turbine inlet temperature (**a**), turbine inlet pressure (**b**), compressor inlet temperature (**c**), and compressor inlet pressure (**d**) on specific work, overall efficiency, and cost of core equipment (intercooling cycle). Figure 9: Effects of the turbine inlet temperature (**a**), turbine inlet pressure (**b**), compressor inlet temperature (**c**), and compressor inlet pressure (**d**) on specific work, overall efficiency, and cost of core equipment (recompression cycle). pressure on performance are not simply monotone. Furthermore, using the same parameters set cannot maximize the specific work, overall efficiency, and minimize the cost of core equipment at the same time. Moreover, the parameter study is difficult to correct and complete comparison between the cycle layouts because the analysis is calculated on the base condition. Therefore, it is necessary to perform multi-objective optimization and then compare the various cycle layouts.
# A Novel Non-Coherent \(Q\)-Ary TH-PPM Transceiver
## 2 System Model
### New TH-PPM Transceiver
Taking 2-ary TH-PPM as an example, the new TH-PPM receiver includes two parallel matched filters. The output of parallel matched filters of the signal is shown in Figure 8.  
In Figure 8, there are four data symbols (1, 2, 2, and 1) are transmitted, respectively. When the transmitted data symbol is 1, the matched filter (1) will output a correlation peak, while the matched filter (2) will not output a correlation peak. Therefore, it is possible to determine the received data by judging which matched filter outputs a correlation peak. In this way, we can reduce the requirement for time synchronization accuracy. The decision flow of the new TH-PPM receiver is shown in Figure 9.  
Figure 7: Block diagram of the new TH-PPM receiver. Figure 8: Parallel matched filtering of the new TH-PPM receiver. Figure 5.0 Data decision of the new TH-PPM receiver. Start data decision;  
Sample the values of every matched filter from 1, 2, \(\ldots\), \(Q\);  
Determine which matched filter outputs the correlation peak;  
Output data \(k\) (it is assumed that the \(kth\) matched filter is outputting a correlation peak);  
End decision.
# A Wideband Non-Stationary 3D GBSM for HAP-MIMO Communication Systems at Millimeter-Wave Bands
## 3 GBSM Non-Stationary Channel Statistical Properties
### Fcf  
We mainly consider the HAP channel in the mmWave bands, where signals are subjected to stronger attenuation and multipath effects, leading to frequency fading and other phenomena during signal propagation. Therefore, we need to discuss the correlation function and non-stationary characteristics of HAP channels. By setting the time increment \(\Delta t\) and the antenna space increment \(\Delta q\), \(\Delta p\) to zero, FCF can be expressed as [41]  
\[R_{qm,pn}(t,f;\Delta f)=R_{qm,pn}^{\text{LoS}}(t,f;\Delta f)+R_{qm,pn}^{\text {NLoS}}(t,f;\Delta f), \tag{33}\]  
\[R_{qm,pn}^{\text{LoS}}(t,f;\Delta f)=\frac{K}{K+1}E[h_{qm,pn}^{\text{LoS}^{*} }(f)(\frac{f}{f_{c}})^{\text{LoS}^{*}}h_{qm,pn}^{\text{LoS}}(f+\Delta f)(\frac {f+\Delta f}{f_{c}})^{\text{LoS}}e^{i2\pi\Omega_{f,1}}], \tag{34}\]  
\[R_{qm,pn}^{\text{NLoS}}(t,f;\Delta f)=\frac{1}{K+1}\sum_{i=1}^{n}E[h_{qm,pn}^{i} (f)(\frac{f}{f_{c}})^{\gamma_{i}^{*}}h_{qm,pn}^{i}(f+\Delta f)(\frac{f+\Delta f }{f_{c}})^{\gamma_{i}}e^{i2\pi\Omega_{f,2}}]. \tag{35}\]