esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In addition to the protective coal pillar between the coalface and roadway, there was also a protective coal pillar between the two reused roadways. The complicated conditions and stress superposition inevitably caused serious roadway deformation, but relevant studies were lacking. Furthermore, with the gradual maturity of grouting technology, it has been found that reasonable grouting technology can improve the strength and stability of roadway-surrounding rock, and control the roadway deformation effectively. More and more scholars began to study grouting materials, and the grouting technology was widely applied in coal mines or other engineering applications [25; 26; 27; 28]. By using experimental means, it was revealed that grouting could raise the residual strength of broken rock [29]. Various grouting materials emerged. For example, a polymer foam grouting material for strengthening broken coal was developed [30]. Besides, a new grouting material based on magnesium phosphate cement was developed [31]. In recent years, great progress has been made in the combination of grouting and roadway support technology. For example, a yielding bolt-grouting support applied to a soft-rock roadway was designed [32]. In addition, a backfilled chemical grouting material consisting mainly of cement and sodium silicate was used to improve the support effect of traditional U-shaped steel sets. Based on this, the applied effect of cement and sodium silicate was evaluated by numerical simulation [33]. As can be seen from the above, there has been great progress in the research of grouting reinforcement of roadway-surrounding rock, but the existing literature mainly focuses on the grouting materials, performance improvement and universal grouting mechanism. However, different field projects have their special site conditions and engineering problems. The temporal and spatial evolution of roadway deformation is vital in grouting projects [34]. There are still many issues to be further explored. For a field project, the grouting control technology, such as material, grouting opportunity, and technical parameters, all should be adjusted to satisfy specific engineering requirements. Meanwhile, the geological environment of underground engineering is complex and diverse. The selection of grouting materials and technology is also affected by different geological conditions. Obviously, faults, collapsed columns, water-bearing strata and so on all put forward higher requirements of grouting materials and technology. This paper takes the reused roadway in Sihe coal mine as the background. The remainder of this paper is organized as follows. The engineering background is describedin Section 2. The analysis of deformation characteristics and forced mechanism of the reused roadway are discussed in Section 3 using field monitoring and FLAC\({}^{3\text{D}}\) numerical simulation. The grouting control technology including reasonable material properties, grouting opportunities, technological parameters and the engineering applications is stated in Section 4. The conclusions are finally summarized in Section 5.
## 4 Experiment
### Model-Switching Architecture with Classifiers  
In the experiment, we used four driving scenarios: rainy, sunny, cloudy, and rainy at night. Figure 7 illustrates the topology of the architecture. Two classifiers, namely, the day-and-night classifier and the weather classifier, were used to select the CNN model for performing semantic segmentation in various scenarios. The model-switching architecture used in this study is depicted in Figure 7. It used VGG16 as the day-and-night classifier (blue) and the weather classifier (green) to select a suitable CNN for semantic segmentation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration** & \\ \hline GPU & RTX2080 TI \\ CPU & Intel Xeon E5-2620 \\ Motherboard & X79 \\ Memory & DDR3 64 GB 1333 Hz \\ Operation system & Ubuntu 18.04 \\ Programing language & Python 3.6 \\ Machine learning library & TensorFlow 1.14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment computer configuration.  
Figure 6: Convolution with a \(2\times 2\) kernel using convolutional stride 2 work on a \(4\times 4\) feature map.  
Identifying weather conditions and determining whether it is day or night is challenging. For these reasons, we employed a CNN classifier to realize model switching. Figure 8 presents the operation of the CNN classifier in scenario detection. Figure 9 depicts the model selection mechanism. By using the day-and-night classifier, the network selected an appropriate model for the time of the day; subsequently, by using the weather classifier, the network selected an appropriate model for the given weather scenario to perform road semantic segmentation.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Optimization Based Methods
#### 3.3.3 [PERSON]'s Method  
In Reference [133], the restart PSO (RPSO) is used to design the FIR filter. Compared with PSO, the RPSO algorithm depends on the two following criteria:  
**Criterion 1:** Terminate if the fitness's standard deviation of swarm is smaller than \(10^{-3}\). In this case, the particles are randomly redistributed in the search space with a probability of \(1/D\).  
**Criterion 2:** Terminate if the change in fitness of the objective function is below \(10^{-8}\) for certain generations, then the particles are restarted by calculating derivatives to _gbest_.  
Figure 7a shows the frequency responses of the PSO and RPSO algorithms, and Figure 7b shows the frequency responses of the PSO and DEPSO algorithms. Note that the filters designed by the three algorithms have similar performance. Compared to the PSO algorithm, the RPSO algorithm can prevent the results from falling into a local optimal solution, while the DEPSO algorithm has better convergence performance.
## 1 Introduction
Frequency splitting is the deviation in the resonant frequency of the system due to overcoupling between the Tx and Rx coils. In [29], the auto-tuning of the impedance matching circuit has been discussed to minimize the effects of frequency splitting for the single Tx and Rx system. In case of multiple Rx system which is operated at different resonant frequency employing the impedance matching circuit increases the design complexity and requires a wide range of capacitance and inductive elements. Therefore, to minimize the effects of frequency splitting with simple control approach a non-identical resonant coil structure was studied for different distance in single Tx and Rx system [30; 31]. However, in case of lower power multiple, Rx WPT system the size of the Rx coils is restricted which requires the optimal design of non-identical Tx and Rx coil by considering the Rx coil size constraints. Based on the above discussion, it is observed that for the multiple Rx WPT system under dynamic environment, the cross-coupling, frequency splitting, uniform power distribution to all the Rx with improved PTE are a major challenge. It is also noted that all these issues are interrelated to each other and requires an optimal design in multiple Rx's by considering all the challenges. This paper proposes a selective resonance WPT to multiple Rx's using Tx frequency tuning circuit. Further to improve the PTE, an additional relay coil is added to each Rx's which can reduce the effect of load port resistance and improve the quality factor. The cross-coupling effect is minimized by determining the optimal value of the compensation reactance and connecting across each load coil. Frequency splitting under the over-coupling condition is eliminated by choosing a non-identical resonant Tx coil structure at the minimum possible coupling distance. The paper is organized as in Section 2 the basic structure of the multiple Rx's WPT system at different configurations is presented. In Section 3, the performance improvement and design strategies of the proposed method is discussed. The validation of the proposed method using finite element simulation and experimental measurements are presented in Sections 4 and 5 respectively.
## 2 Related Works and Contribution
### Data Aggregation  
Data aggregation methods were designed for multi-hop transmissions, where intermediate nodes can merge several messages received from neighbouring sensor nodes, and send them towards the sink node as a single packet [7; 8; 9]. As a result, the number of packets transmitted in the network is reduced. Moreover, in case when the sensor nodes are densely deployed, and can observe the same phenomenon, the aggregation methods allow the intermediate nodes to eliminate redundant messages. This approach increases transmission delay as the data have to be buffered before aggregation and transmission, when the intermediate node receives messages from different sources.  
In [10] a data aggregation method was introduced to reduce power consumption in wearable sensor networks for physical movement monitoring and quality evaluation of postural control system during walking. The development of the method was motivated by the fact that larger packets have a lower energy consumption per bit. Based on this observation, a routing protocol was introduced, which finds effective routes for data aggregation. The authors have suggested that by transmitting aggregated data though longer paths, a smaller total energy consumption can be achieved than in case of the shortest path communication. However, the longer transmission path results in larger delay. Thus, this approach is not suitable for real-time applications.  
A data aggregation algorithm for wearable sensor systems with dynamic topology was proposed in [11]. In that work the wearable sensor networks were considered that connect devices worn by many persons in a group, e.g., a tactical unit. According to that approach the data transmission is based on dynamic routing paths. Each sensor node aggregates data collected by itself and transmitted from its children. The data aggregation algorithm was based on distributed sorting and query scheme that allow every sensor node to send only one message during data transmission round.  
Wireless body area networks of smart wearable patches were addressed in [12]. The smart patches can integrate electroencephalography sensors, temperature sensors, electrocardiography sensors, pressure sensors, etc. It was shown that the data aggregation concept can be implemented to combine data from multiple smart patches and transmit them to a base station with reduced energy consumption. The method assumes that nodes are switched over between active and sleep states. The intermediate node in this scheme aggregates data from the sensor nodes that are currently active.
## 3 Results
### Technological Features and Affordances
#### 3.3.3 Media Representation  
In AR-supported instruction, the information carrier received by users had seven main forms, as shown in Table 2. In terms of the total number, 3D object was the most common media representation, especially in health and medicine. For example, in anatomical education and dental morphology, AR was used to construct a 3D model of the skull or dental piece for students to learn about the structure and composition [35; 36]. In addition, video and text were two other important information carriers. In surgical training, the real-time changes in the operating environment were represented in videos for monitoring,  
Figure 4: Input devices of AR-supported instruction from 2001 to 2020.  
and trainees' operating processes were also recorded by videos [37, 38]. Furthermore, text was used as a scaffolding to assist students to complete training or learning in AR systems in engineering and other fields [22, 30, 39]. Moreover, data occurred in health and medicine, recording the operating time, smoothness of the AR simulator, and the needle position [25].
## 3 Blockchain-Enhanced Security and Operational Efficiency in VANETs
### Reputation Models and Calculation
#### 3.2.2 Probabilistic Reputation Framework  
The use of probabilistic models enables our system to better adapt to the dynamic and diverse nature of VANETs, while the reputation-based mechanisms ensure a robust defense against various adversarial behaviors. Together, these elements contribute to a comprehensive security and data management solution that addresses the unique challenges of VANETs.  
The reputation or trust value of each node is algorithmically computed based on the veracity and reliability of their event reports. These trust values are indelibly recorded on a blockchain-enabled \"reputation chain\". In specific edge cases or scenarios, the vehicle node possessing the highest cumulative historical reputation may be accorded priority for specialized service requests.  
The computational formula for updating the reputation value, denoted as \(R_{it}\), is articulated in the simple Equation (2):  
\[R_{it}=\mu R_{t-1}+(1-\mu)T+\mu R_{\text{social}} \tag{2}\]  
where:  
* \(R_{it}\) signifies the newly updated reputation value.
* \(R_{t-1}\) represents the aggregated reputation score from the preceding time interval.
* \(T\) is the quantified trust metric derived from the event report \(W_{i}\).
* \(R_{\text{social}}\) is a measure that incorporates various social factors affecting trust.
* \(\mu\) represents the weighting factors.
## 5 Numerical Results and Discussion
### Network Settings
#### 5.1.3 Simulation Parameters  
The relevant parameters in the simulation can be seen in Table 1.  
Figure 7: Four single-slot networks.(**a**,**b**) Sensing-resource-abundant networks. (**c**,**d**) Sensing-resource-limited networks.
## 2 Mechanical Analysis
### Shaking Force and Shaking Moment  
The mass matrix (**M**; Equation (4)) is notable for being constant, as it is not expressed in terms of the mechanism's movement. Due to this property, it is possible to derive the equations that define the linear momentum (**L**) in both the \(x\)-axis and \(y\)-axis using Equation (5).  
\[\begin{bmatrix}L_{i}\\ L_{j}\end{bmatrix}=\textbf{B}\textbf{M}\textbf{\dot{q}} \tag{5}\]  
where **B** is a matrix composed of identity matrices corresponding to the number of basic points in the mechanism (Equation (6)).  
\[\textbf{B}=\begin{bmatrix}1&0&1&0&1&0&1&0&1&0&1&0&1&0\\ 0&1&0&1&0&1&0&1&0&1&0&1&0&1\end{bmatrix}^{\textbf{T}} \tag{6}\]  
The shaking force (\(ShF_{i}\) and \(ShF_{j}\)) (Equation (7)) can be computed by taking the time derivative of the equations \(L_{i}\) and \(L_{j}\) (Equation (5)).  
\[\begin{bmatrix}ShF_{i}\\ ShF_{j}\end{bmatrix}=\frac{\textbf{B}\textbf{M}\textbf{\dot{q}}}{dt}=\textbf{B} \textbf{M}\textbf{\ddot{q}} \tag{7}\]  
Similarly, the shaking moment (\(ShM\)) (Equation (9)) is derived by differentiating the angular momentum equation (\(H\)) (Equation (8)) with respect to time.  
\[H=\textbf{q}\times\left(\textbf{M}\textbf{\dot{q}}\right)=\textbf{r}\textbf{ M}\textbf{\dot{q}} \tag{8}\]  
\[ShM=\frac{dH}{dt}=\textbf{r}\textbf{M}\textbf{\ddot{q}}+\textbf{i}\textbf{M} \textbf{\dot{q}} \tag{9}\]  
Consider the following:  
\[\textbf{i}= [-A_{Y}\quad A_{X}\quad-B_{Y}\quad B_{X}\quad-C_{Y}\quad C_{X} \quad-D_{Y}\quad D_{X} \tag{10}\] \[\quad-E_{Y}\quad E_{X}\quad-F_{Y}\quad F_{X}\quad-G_{Y}\quad G_{X }]^{\textbf{T}}\] \[\textbf{i}= [-VA_{Y}\quad VA_{X}\quad-VB_{Y}\quad VB_{X}\quad-VC_{Y}\quad VC _{X}\quad-VD_{Y}\quad VD_{X}\] (11) \[\quad-VE_{Y}\quad VE_{X}\quad-VF_{Y}\quad VF_{X}\quad-VG_{Y} \quad VG_{X}]^{\textbf{T}}\]
## 3 Results and Discussion  
### Effects of Salinity on Productivity and Conductivity  
Increasing cultivation salinity negatively impacted plant growth (Figure 2a). Similarly to what had been observed with _M. nodiflorum_ and _I. crithmoides_[2], the species _D. crassifolium_ exhibited a significant decrease (around 50%) in productivity at a salinity of 110 mmol L\({}^{-1}\), compared to samples grown at 35 mmol L\({}^{-1}\). At the highest salinity tested (465 mmol L\({}^{-1}\)), productivity dropped to 95%. _S. maritima_ displayed similar responses, although slightly less pronounced. Its productivity decreased by 40% with the increase from 35 to 110 mmol L\({}^{-1}\), and 86% at 465 mmol L\({}^{-1}\). These findings align with previous results by [PERSON] et al. [30] and [PERSON] et al. [2], who observed reduced growth in _Salicornia persica_, _S. europaea_, and _S. ramossissima_ at salinities exceeding 100 mmol L\({}^{-1}\). Similarly, [PERSON] et al. [12] reported a 57% decrease in relative productivity for _I. crithmoides_ when salinity was increased from 35 to 110 mmol L\({}^{-1}\). However, _M. nodiflorum_ exhibited a contrasting behaviour, achieving maximum productivity between salinities of 35 and 100 mmol L\({}^{-1}\). These results indicate that the synergistic response between crop salinity and productivity is specific to each halophyte plant species. Nonetheless, the increase in salt concentration has undoubtedly resulted in a significant escalation in strategies employed to maintain ion and osmotic homeostasis, which, in turn, resulted in high energy expenditures, explaining the observed decline in productivity [31].  
The conductivity of both species (Figure 2b) showed a slight increase from 110 mmol L\({}^{-1}\) with stabilisation at higher salinities (275 to 465 mmol L\({}^{-1}\)), which shows an acclimation linked with the ability to regulate salt concentration in the tissues.  
Figure 2: Relative productivity (**a**) and conductivity (**b**) of _D. crassifolium_ and _S. maritima_ in salinities between 35 and 465 mmol L\({}^{-1}\). Bars of the same colour labelled with different letters are significantly different (_p_ < 0.05). SEM error bars were used for statistical comparisons between experimental treatments.
# The HD-Domain Metalloprotein Superfamily:
###### Abstract
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Subclasses** & **Protein** & **Nuclearity** & **Active Metal** & **Chemistry** & **Substrate** & **PDB ID** & **Origin** & **References** \\ \hline
**oxygenes** & **PROZ** & **dinuclear** & **Fe** & **Oxygenese** & **myo-inositol** & **2 HUD** & **Mas musculus** & [11] \\  & **PROZ** & **dinuclear** & **Fe** & **Oxygenese** & **OH-AEP** & **4 MLM** & **bacterium HF130, AEP\_1** & [12,13] \\  & **TmpB** & **dinuclear** & **Fe** & **Oxygenese** & **OH-AMEP** & **8 NPA** & _Listingoxin curation_ & [13] \\ \hline  & **VNR** & **mononuclear** & **Co** & **Moverophosphate** & **4 AMP** & **2 PAQ** & **Ezchrictivicicicicicicicicicicicicicicicicicicicicicicic  
The HD-domain superfamily fosters three different enzymatic classes: hydrolases, oxygenases, and lyases, with the hydrolases being the largest and best-characterized group. Hydrolases are further subdivided into (i) phosphatases, including dGTPases [38], RelA/SpoT [9; 17], SAMHD1 [18; 19], EF1143 [20; 21], etc. and (ii) phosphodieserases (PDEs), including exoribonucleases [39], PDEases [27; 28; 29; 30; 31; 32; 33; 34], Cas proteins [23; 24; 25; 26], and HD-GYPs [14; 35; 36; 40] (Figure 1B). Phosphatases hydrolyze a multitude of (deoxy)nucleotide-based substrates that vary in the identity of their base(s) and the extent of phosphorylation (Figure 2) [3; 5; 16; 22]. PDEs, on the other hand, degrade a variety of cyclic signaling molecules and single-stranded nucleic acids (Figure 2) [23; 24; 41]. Oxygenases and lyases are relatively recent additions to the HD-domain superfamily with only a handful of representatives to have been biochemically characterized. All identified oxygenases catalyze the oxidative cleavage of C-C/P bonds [11; 12; 13; 15] (Figure 2), while the single known lyase is a cyanamide hydratase [37]. This functional plurality widens the chemical repertoire of the HD-domain superfamily, which is likely to harbor more oxygenases, lyases, or enzymes with novel chemistries.
## 2 Testbed Setup and Measurement Methodology
### Experimental Setup for Timing Measurements  
System configurations and methodologies of the following experiments were deployed to perform initial tests on timing accuracy in a virtual environment as follows.
# Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection
## 9 Conclusions  
This paper focuses on the application of Internet of Things (IoT) on real networks and in particular on connected bikes. We introduced the \"Internet of Bikes\", IoB-DTN routing protocol being applied  
Figure 46: Performance comparison of IoB protocol variants.  
Figure 47: Performance comparison between five applications.  
to mobile network IoT devices running a data collection application. First, we evaluated the impact of the variation of three parameters on the proposed protocol: buffer management policies, number of copies of a packet sprayed in the network and the transmission power value. Our simulation results show that GPP policy, limiting the number of copies sprayed in the network and using 10 mW as transmission power of sensors offer the best trade-off among simulated metrics. Next, we gave a detailed performance comparison between the multi-hop IoB-DTN protocol with a low-power wide-area network (LPWAN) technology, LoRa type with respect to two metrics: energy consumption and throughput. The obtained results show that using IoB-DTN protocol based on a multi-hop topology offers better throughput while using a long-range technology where there is only bike to bike station communication gives better performance on the energy consumption. Finally, we proposed an efficient solution for the multi-hop IoB-DTN protocol to cope with this result. Data aggregation approach is applied leading the combination of several data packets, according to some conditions, into a single packet. We proposed three variants of IoB-DTN based on this mechanism: IoB based on spatial aggregation (IoB-SA), IoB based on temporal aggregation (IoB-TA) and IoB based on spatiotemporal aggregation (IoB-STA). The simulation results showed that the three variants provide better performances with respect to many metrics, comparing to IoB-DTN without aggregation and a low-power long-range technology, LoRa type.  
**Author Contributions:** Supervision and Validation, H.R. and A.M.; and Writing--original draft, review and Editing, Y.Z.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Results
### Functional Characterization of DoTPS10 Involved in the Formation of Linalool  
Generally, monoterpene volatiles are the main terpen in orchids. These are produced by TPS proteins using GPP as a substrate [6,7]. To further confirm the role of DoTPS10 in the synthesis of monoterpenes, a His-tagged vector ( pET32a) was used to produce the recombinant DoTPS10 protein. The vector was successfully expressed as a soluble protein in _E. coli_ BL21. DoTPS10 protein was purified using a His-trap Ni-sepharose high performance column. His-tagged vector (pET32a) had an Mw of 23.8 kDa, containing 4.8 kDa of six His-tags, and recombinant DoTPS10 protein exhibited an approximate Mw of 88.3 kDa on SDS-PAGE (Figure 10E). After recombinant DoTPS10 protein  
Figure 10: Functional characterization of _DoTPS10_. (**A–D**) Subcellular localization of DoTPS10 fused with yellow fluorescent protein (YFP) and transiently expressed in _A. thaliana_ protoplasts. Bars = 5 μm. (**E**) SDS-PAGE analysis of DoTPS10 recombinant protein expressed in _Escherichia coli_ BL21. Lanes M, 1, 2, and 3 indicate marker, pET32a, crude DoTPS10 protein, and purified DoTPS10 protein, respectively. Red arrow indicates target protein. (**F–G**) Gas chromatograms of products yielded by DoTPS10 using GPP as a substrate. (**H**) Mass spectrum of linalool was identical to the mass spectrum of the linalool standard.  
was incubated with GPP, gas chromatography-mass spectrometry (GC-MS) analysis showed that empty pET32a could not produce linalool while the recombinant DoTPS10 protein singly converted the substrate GPP to the corresponding product linalool (Figure S3, Figure 9G,H).