esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Status
### Recurrent Neural Network Models  
RNN models and their variants: In the early days, some research [19] combined RNNs and CNNs (convolutional neural networks). The RNN was used to process scalar variables to extract the overall features of the network, while the CNN was used to extract traffic information for dealing with specific network configurations, predicting potential time-series problems, and achieving good air traffic flow management. However, since RNN models are prone to gradient vanishing and gradient explosion, subsequent academic research has mostly used RNN variants such as LSTM and GRU to solve time-series prediction problems. [PERSON] et al. [20] studied a long-short-term memory network model applied to named data networking. They expanded the single traffic feature value to multi-dimensional variables related to link traffic, achieving precise control of the network traffic and avoiding network congestion. [PERSON] first modeled the Internet traffic matrix prediction problem as a video prediction task [21]. Then, a Seq2 Seq model based on a convolutional long-short-term memory network (ConvLSTM-TM) was proposed to predict the traffic matrix in the next time slot, completing the time-series prediction of network traffic and providing a new idea for the academic community. [PERSON] et al. [22]proposed a gated recurrent unit (GRU) neural network traffic prediction algorithm based on transfer learning and realized the expression of the nonlinear, self-similar, and long-range dependent characteristics of satellite network traffic. By combining the transfer learning method, they solved the problem of insufficient online traffic data. They also adopted a particle filter online training algorithm to reduce the training time complexity and achieve an accurate prediction of the satellite network traffic.
## 3 Numerical Simulation
### Rapid Combined Support  
Different from active measures that stress release hole and advanced stress relief blasting relieving and transferring the stress of surrounding rock, rapid combined support is also a passive way to prevent and control the rockburst under high geostress. Rockburst is prevented through providing radial support stress to rock, controlling the deformation of rock, improving the stress condition of rock and reducing the break of rock. Meanwhile, the harm of a rockburst is controlled by shortening support operation time, increasing support early strength. In this simulation, the same anchor and different thickness (0.05 m, 0.1 m, 0.2 m, 0.5 m, 1.0 m and 1.5 m) of shotcrete was adopted to study on the failure range of surrounding rock and the failure state of shotcrete. The support parameters are shown in Tables 3 and 4.  
Distribution of the plastic zone in surrounding rock with different thickness of shotcrete is shown in Figure 13. With the increase of shotcrete thickness, the plastic zone in the surrounding rock of the sidewall and vault decreased gradually, while the plastic zone in the surrounding rock of the bottom changed little. Support could improve the stress condition of surrounding rock, and the thicker the shotcrete is, the better the effect is. As we can see from Figure 14, increasing the thickness of shotcrete could reduce the damage zone of shotcrete itself, effectively controlling the occurrence of a rockburst.  
Based on the existing engineering experience and the simulation results in this section, we know that the timely construction of high-strength support structure was an important part of rockburst prevention and control. In order to achieve that better, this paper introduced two measures: Water swelling anchor and nano-admixture shotcrete.
## 7 Simulation
### Selecting Quality Coefficient  
The _quality coefficient_ parameter affects the value of each element in quantization luminance matrix [34]. As mentioned in Section 4.1, an M-Frame block is prepared for transmission by dividing blocked DCT matrix with the quantization luminance matrix. Hence, _quality_ influences the value of each pixel in M-Frame blocks. Choosing an appropriate value for _quality coefficient_ is so important, since small value for it leads to a greater number of bits required for transmitting the data of each block pixel. On the other hand, high value for _quality_ results in a greater number of zeros in each M-Frame block. This means more data loss in M-Frame compression method. As a result, defining _quality coefficient_ encounters the bit-loss trade off.  
The approach used for defining _quality_ is examining different values for it. By analyzing average Peak Signal-to-Noise Ratio (PSNR) [42] and required bit in different _qualities_ in various video frames, it is shown that some _quality_ values require \(8\) bits for transmitting each pixel of M-frame blocks. Since \(8\) bits are earmarked for each pixel, the value with negligible loss (proper PSNR) is selected. As Figure 16 shows, _0.5_ is the best value for bit-loss trade off which also used in our simulations.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Bit Rate & 250 k\({}_{\text{bps}}\) \\ Listen Power & 60 mJ/sec \\ Receive Power & 63 mJ/sec \\ Transmission Power & 57 mJ/sec \\ Setup Time & 1 msec \\ Communication & 300m \\ Distance & \\ \hline \end{tabular}
\end{table}
Table 1: CC2420 transceiver parameters
###### Abstract  
The use of biogas slurry as an alternative to chemical fertilizers for supplying phosphorus to plants is gaining increasing attention. However, the mechanisms by which biogas slurry activates soil phosphorus and influences phosphorus-metabolizing microorganisms are not yet fully understood. This study characterized the effects of controlled biogas slurry application gradients (0, 13, 27, 40, and 53) on the soil phosphorus structure, camellia oleifera (CO) phosphorus content, microbial phosphorus metabolism functional gene abundance, and phosphorus transformation functions in CO plantation soils. Increasing the dosage of biogas slurry effectively enhanced soil phosphorus levels and significantly increased the proportions of aluminum-bound phosphorus (Al-P) and iron-bound phosphorus (Fe-P). Under simulated conditions, the contents of soil Al-P, Fe-P, and organic phosphorus significantly decreased and transformed into occluded phosphorus (O-P) and calcium-bound phosphorus (Ca-P), while under field conditions, due to spatial heterogeneity, the changes in soil phosphorus and its forms were not distinctly evident. The application of biogas slurry did not significantly alter the major phyla of phosphorus-metabolizing microorganisms in the soil, but significant changes in the abundance of different microorganisms were observed. The abundance of dominant bacterial communities such as _Chlorofexi_bacterium_ increased, while the abundance of communities such as _Actinomycteia_bacterium_ decreased. By influencing the expression of soil microbial functional genes related to inorganic phosphorus solubilization, organic phosphorus mineralization, phosphorus deficiency response regulation, and phosphorus transport, the solubility of inorganic phosphorus and the mineralization rate of organic phosphorus in the soil were enhanced. Additionally, it may weaken microbial phosphorus uptake by inhibiting intercellular phosphorus transport in microorganisms, thereby improving the utilization of soil phosphorus by CO.  
biogas slurry; phosphorus cycle; camellia oleifera; microbiota; genes +
Footnote †: journal: _Article_  
Article
## 4 Discussion
### Strut-Wall Thickness (SWT) Design  
Table 7 and Figure 14 summarize the results on the performance metrics for the cellular structure with a SWT design configuration. To understand the meaning of these findings, Figures 15-17 will be used.  
From Figure 15 (_interaction plots_), it is evident that there are no interaction effects between the design factors and performance metrics. Meaning, these design factors are independent of each other. As stated earlier in the interaction plots if these lines are parallel it indicates that there are no interaction effects. This finding allows us to safely interpret the _mean plots_ for each response based on the design  
Figure 13: Example of force-displacement and deformation modes for (**a**) \(L=50\) mm, (**b**) \(L=100\) mm, (**c**) \(L=150\) mm. The shaded area indicates the energy absorbed before densification.  
factors as shown in Figure 17. From Figure 17, it is evident that the change in peak force is mainly contributed by \(T_{2}\) (_outer wall thickness_). Once the peak force is achieved, the progressive deformation mean force is dominated by both \(T_{1}\) and \(T_{2}\). These forces are not affected by the length of the structure. The energy absorption is affected by all three factors, including length. This is obvious since there is more deformation distance before densification is reached. From these performance metrics, the mean force and \(E_{SEA}\) are most crucial to be looked at. Hence, it can be concluded that the length of the structure is not a crucial factor to be considered. This will have an implication on the design space and manufacturing requirements. Figure 16 depicts a typical force-displacement graph along with the deformation mode for these structures. It can be seen that the deformation mode is stable indicating good crash absorber characteristics. This figure also depicts that the average force and peak force is almost similar for all lengths, indicating once again that the length is not a crucial factor.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Type**} & **T1** & **T2** & **L** & **FP** & **\(F\mu\)** & **EA** & **ESEA** \\ \cline{3-10}  & & **[mm]** & **[mm]** & **[mm]** & **[km]** & **[km]** & **[kJ]** & **[kJ]/kg]** \\ \hline S10 & & 0.2 & 0.4 & 50 & 45 & 18 & 0.5 & 14.9 \\ S11 & & 0.2 & 0.6 & 50 & 71 & 36 & 1 & 22.5 \\ S12 & \multirow{2}{*}{SWT (\(50\times 50\times 50\))} & 0.4 & 0.2 & 50 & 24 & 23 & 0.7 & 15.5 \\ S13 & & 0.4 & 0.6 & 50 & 101 & 58 & 1.7 & 27.3 \\ S14 & & 0.6 & 0.2 & 50 & 35 & 42 & 1.2 & 21  
Figure 16: Example of force-displacement and deformation modes for (**a**) \(L=50\) mm, (**b**) \(L=100\) mm, (**c**) \(L=150\) mm. The shaded area indicates the energy absorbed before densification.  
Figure 15: Interaction plots between design parameters and performance metrics for the SWT design.
## 4 Discussion  
While the methodological procedures applied in the previous chapter can be modified to achieve a direct response to the actual situation in the locality, the practical implementation is limited in several ways. The changes outlined in this chapter summarise how to effectively supplement the digital model of the area and improve its serviceability based on online information.  
The most important part of the project, which currently contains the most data, is the digital model of the location. To be able to talk about a real virtual copy, which is similar in its characteristics to the vision of the Twin City model [1], it is necessary to add elements and systems capable of real-time monitoring of the city's traffic processes thanks to sensors directly connected to this virtual model. This includes, for example, data from sensors used to calculate the current occupancy of parking places [1]. If it is clear that the capacity of the parking places is not fully utilised, there will most likely be no inappropriate parking of vehicles at crossroads and no significant disruption to the flow or throughput of traffic through the site. In the event that the parking capacity is fully occupied, inadequate parking can be expected even at locations that are crucial for passability of the IRS (crossroads, fire roads, etc.). In these cases, the use of camera monitoring and subsequent systematic control seems to be an ideal solution [24]. When monitoring a lot of similar locations, it is possible to use induction loops or detectors working on a similar principle as a support tool that automatically informs the control centre of a potential issue.  
Another approach is the potential utilisation of drones, which can be effectively used for detailed and periodical analysis of the assessed locations in a very short time. Verification of critical places or areas could be done similarly to the small parcel delivery system introduced by Amazon in recent years. Figure 13 illustrates a docking station mounted on a lamppost to serve as both a take-off point, charging station, and a connection point for the dispatch centre controlling the area. The drones provide an ideal means to scan a selected area in a short time frame and send the acquired data to a central control station before the actual firefighting operation takes place. In this way, it would also be possible to optimise in advance the route option during the intervention, thus avoiding any significant time delays [8]. However, the possibility of using drones in built-up areas is limited. Currently, such a data collection option cannot be implemented, as legislation and data protection do not allow the use of drones over built-up areas [25; 26].  
Another way to obtain the necessary data for the assessment and analysis of selected locations can be through lidar or ultrasonic detectors. These can be mounted on garbage trucks that visit the sites several times a week. The collection of these data could be used to update the input data and to update the area model itself.
## 4 The 5G Use Cases and Spectrum Requirements
### ITU-Defined Generic 5G Use Cases
#### 4.1.3 mMTC  
The mMTC applies to scenarios where many network devices requiring a moderately low volume of delay-tolerant data need to be connected. This scenario is dominated by completely automated applications, such as devices and machines requiring little or no human intervention [61]. One important focus of the mMTC is the possibility of having a completely digital society where every user's communication demands are met with desired high service requirements for high-density connectivity [1]. Smart services, such as smart cities, smart agriculture, business communication, manufacturing, and transportation, are practical examples that fall under the mMTC UCs.  
The 5G networks must deliver more flexibility to take care of future use cases and their associated requirements to accommodate other unanticipated UCs that may arise soon. The use cases are built around three KPIs: throughput, device proliferation, and low latency/reliability, as summarized in Table 5. The eMBB use case scenario must guarantee a high peak data rate, user experience data rate, mobility, area traffic capacity, spectrum efficiency, and energy efficiency. The most crucial KPI for the uRLLC scenario is low latency, as it has to provide services for essential applications, such as emergency medical services. High connection density is one of the most crucial capabilities for the mMTC scenario to handle large network users. While some use cases require just a single KPI, others may require multiple KPIs to satisfy the user's requirements. It is, thus, very essential for 5G networks to be able to support a diverse range of applications with their varying KPI requirements in an efficient, reliable, and flexible way.
## 2 Methods
### Search Strategy  
The search strategy aimed to identify both published and unpublished studies. A preliminary search was conducted in the MEDLINE database and Google Scholar to locate articles relevant to the topic. Terms found in article titles, abstracts, and index terms were then used to develop a comprehensive search strategy for English-based search engines, including CINAHL, MEDLINE, Embase, Scopus, Web of Science, ACM Digital Library, IEEE Xplore Digital Library, Compendex, ProQuest, and Google Scholar (first 200 results). The search strategy followed logical expressions adjusted for each database's specific search features, including auto-stemming, wildcard usage, truncation, and quotation marks. The search terms used were Title/Abstract/Keywords/Subject Headings (older adult OR senior OR elderly) AND (arontherapy OR essential oil OR fragrance OR odor OR olfactory OR scent OR smell) AND (virtual reality OR mixed reality OR VR). A Cochrane Library search using these keywords yielded no relevant systematic reviews on this topic.  
In the next phase, electronic databases were searched for relevant articles using identified keywords in the titles, abstracts, and index terms. The following databases were included: ACM Digital Library (1985 to 20 May 2024); IEEE Xplore Digital Library (1988 to 20 May 2024); Compendex (via Engineering Village, 1884 to 20 May 2024); CINAHL (via EBSCOhost, 1982 to May 20, 2024); MEDLINE (via EBSCOhost, 1946 to May 20, 2024); Scopus (1788 to 20 May 2024); EMBASE (via Ovid, 1974 to 20 May 2024); Conference Proceedings Citation Index--Science (via Web of Science, 1990 to 20 May 2024); Conference Proceedings Citation Index--Social Science & Humanities (via Web of Science, 1990 to 20 May 2024); Emerging Sources Citation Index (via Web of Science, 2015 to 20 May 2024); ProQuest Dissertations and Theses Global (via ProQuest, 1861 to 20 May 2024); and Google Scholar (up to 20 May 2024). The search strategies applied keywords and subject headings to gather the relevant literature on aromatherapy and VR program development for older adults. No articles required contacting original authors for additional identification. Finally, the reference lists of all included articles were screened for additional relevant studies. A full description of the search strategy can be found in Table S2.  
We collaborated with two medical librarians to further refine the search strategy, ensuring comprehensive inclusion of all relevant and key articles. As this is a scoping review, we aimed to include all potentially relevant studies. For instance, studies involving participants both younger and older than 65 years were included, with relevant data manually extracted during the data extraction stage. The inclusion criteria covered studies in any language with English abstracts, with a cut-off date of May 2024. However, only full-text articles in English and Chinese underwent full-text screening due to the language proficiencies of the research team. Unpublished studies and grey literature were also explored through Google Scholar.
## 1 Introduction
And (4) such approaches are mostly designed for single-system snapshots, making it challenging to coordinate control actions across multiple time steps while considering practical constraints, i.e., capacitors should not be switched on and off too often during one operating day. To address the above issues, this paper presents key perspectives for an innovative control framework of training reinforcement learning-based agents to provide real-time data-driven control strategies. Quasi-steady-state voltage control, line flow control, and transmission loss control problems can be formulated as Markov decision processes (MDPs) so that they can take full advantage of flexible reinforcement learning (RL) algorithms that are proven to be effective in various real-world control problems in highly dynamic and stochastic environments. Recent research efforts applied RL-based methods to solve different power grid control problems, including damping control under uncertainties [11], cyber-physical security assessment [12], load-frequency control [13], short-term load forecasting [14], dynamic economic dispatch [15], emergency control for transient behavior enhancement [16], real-time intelligent topology control considering practical constraints [17], voltage security control [18; 19; 20; 21; 22; 23], frequency regulation [24], real-time optimal power flow control [25], multienergy management in microgrids [26], optimal trading strategy [27], voltage management in distribution networks [28], MPPT control for PV systems [29], reactive power optimization of distribution networks [30], and cooperative control for hybrid electric vehicles [31]. Preliminary results on small-to-medium test systems demonstrate the feasibility of using RL agents to provide effective controls. Inspired by these efforts, an RL-based multi-objective framework for deriving real-time voltage control and line flow control strategies is proposed in this paper, which adopts and extends the state-of-the-art reinforcement learning algorithm to achieve robust control performance considering practical constraints. The decision-making is purely data-driven, without the need for accurate real-time system models once AI agents are well trained. Thus, live supervisory control and data acquisition (SCADA) or phasor measurement unit (PMU) data streamed from a wide-area measurement system (WAMS) can be used to enable sub-second controls, which is valuable for scenarios with fast changes like renewable resource variations and significant disturbances. The remainder of this paper is organized as follows. Section 2 introduces the principles of the Markov decision process and reinforcement learning. Section 3 provides the problem formulation and explains the architecture of the proposed RL-based control framework. Section 4 details the implementation of two types of control, namely voltage control and line flow control. The case studies and a discussion are provided in Section 5. Finally, key findings and conclusions are drawn in Section 5 with directions for future work identified.
# Dual-Indicator Micro-Electro-Mechanical System Monitoring Method for Rock Instability Early Warning
## 3 Experimental Validation and Performance Evaluation
### Experimental Setup and Modeling
#### 3.1.2 Data Sensing Instrumentation  
As shown in Figure 6a, the triaxial MEMS acceleration sensor with built-in amplifier (model INV9832-50) developed by Dongfang Institute of Technology (Hangzhou, China) was selected for this experiment. The MEMS sensing device can monitor very small impact signals with a sensitivity of 100 mV/g, and the weight of the sensing device is about 12 g, which imposes a negligible gravitational load on the rock mass, and is suitable for acquiring the dynamics index of the rock mass. As shown in Figure 6b, the INV3062C high-precision distributed data acquisition instrument of Dongfang Institute was employed in this experiment to convert the signals collected by the MEMS sensing device, and the final data were transmitted to the computer through Ethernet cables for subsequent data analysis and processing. At the same time, as shown in Figure 6c, the accuracy of the data collected by MEMS sensing was verified by the laser vibration-measuring instrument model RSV-150 from Polytec GmbH (Waldorf, Germany). The main technical parameters of the data acquisition equipment are summarized in Table 2.
## 2 Methodology
### Lower Inclination Orbit Mission Definition  
The satellite will be designed based on the Lean satellite standard, which will be orbiting in the LEO. The mission is to use an IoT terminal for satellite communication to cover remote or hard-to-reach areas in the near-equatorial regions. The payload is a LoRa receiver onboard the satellite. It is capable of receiving LoRa message packets from the ground IoT terminal and decoding the messages before being sent down to the ground station (GS) via S-band. The GS is located in the DEWA's Research and Development Centre in Dubai-UAE. The aim of the mission is to cover the entire area of interest daily and maximize the revisit time. Additionally, to increase the number of passes of the satellite above the GS, so that we can get updated data within a few hours. The targeted areas are the cities/countries confined within latitude bands between, which are at the 0\({}^{\circ}\) to \(\pm\)27\({}^{\circ}\) latitudes. The elevation angle between the satellite and GS should be at least greater than 30\({}^{\circ}\) at the MidPass [32] to ensure useful data through a favorable link margin.  
In the simulation, the orbital parameters of DEWASAT-1 will be used as SSO. On the other hand, the satellite in the mission scenario with a lower inclination angle will use the same parameters except the inclination angle which will be changed to determine the optimum angle that will optimize the mission requirements. Table 1 below shows the orbital parameters of the two satellites used in the simulations.