esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation and Experimentation
### Active Control Method with the Piecewise Strategy
The active control approach with the piecewise equivalent stiffness and damping coefficient can not only keep the system have a shorter settling time, but also keep the stability of the electromagnetic isolation system and an isolated object, which can meet the requirements of the electromagnetic vibration isolation system in practical application. Based on the experimental result in Figure 6, the active control method with the equivalent stiffness and damping coefficient can effectively control vibration and provide desired dynamic performance indexes. However, there are overshoot and oscillations in experimental results. In order to reduce overshoot and oscillations to keep the stability of the electromagnetic isolation system and an isolated object, the piecewise strategy is proposed as follows:  
(1) \(k_{2}=540\) N/m, \(d_{2}=30\) N \(\cdot\) s/m, when \(x<4.00\times 10^{-4}\) m; (2) \(k_{2}=540\) N/m, \(d_{2}=70\) N \(\cdot\) s/m, when \(x\geq 4.00\times 10^{-4}\) m.  
The method based on the fixed equivalent stiffness \(k_{2}=540\) N/m and fixed equivalent damping coefficient \(d_{2}=70\) N \(\cdot\) s/m is utilized for comparison purpose in experiments. Figure 9 shows the comparison results from experiments, when the system is controlled by active method with the piecewise equivalent stiffness and damping coefficient, the vibration displacement \(x\) is stable at \(5.54\times 10^{-4}\) m, the rise time \(t_{r}\), the peak time \(t_{p}\) and the settling time \(t_{s}\) are both equal to \(0.44\) s, the overshoot \(\sigma\)% and the number of oscillations \(N\) are both equal to \(0\). When the system is controlled by the active approach with the fixed equivalent stiffness and fixed equivalent damping coefficient, the vibration displacement \(x\) is stable at \(5.54\times 10^{-4}\) m, the overshoot \(\sigma\)% and the number of oscillations \(N\) are both equal to \(0\), however the rise time \(t_{r}\), the peak time \(t_{p}\) and the settling time \(t_{s}\) are both equal to \(0.69\) s. The experimental results verify the simulation results, which demonstrate that the active control approach with the piecewise equivalent stiffness and damping coefficient can not only improve the response speed of the system, but also keep the stability of the system and an isolated object. Figure 8: Simulation results of two active control methods.
# GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks
## 4 Proposed System Overview and Models
### System Overview
#### 4.1.6 Fuzzy Rule Based System  
In order to drive the fitness value of \(Th_{m}\), the fuzzy system considers three inputs: (a) HC, (b) EV, (c) AF, and returns (d) FV. The fuzzy system for re-clustering threshold membership functions, their associated fuzzy sets, and rules are highlighted in Figure 4. The number of fuzzy sets determines the level of granularity or degree of a membership function. Moreover, the range of the fuzzy sets is set based on their importance. For the three input factors, there are two, three, and four fuzzy sets, so there are 24 combinations or rules for fitness value.  
The membership functions, boundary values, and ranges of corresponding fuzzy sets are highlighted with different colors. The vertical height of each membership function is one. The details of fuzzy membership functions, fuzzy sets, and horizontal values are defined below:  
* HC represents the hop count for a report from 0 to 100. It has two fuzzy sets, namely less (L) and enough (E).
* EV is the number of events being generated in the sensor network from 0 to 7. The higher the number, the more the communication overhead is associated, and vice versa. This fuzzy membership function has three fuzzy sets, namely small (S), medium (M), and large (L) from 0 to 100.  
* AF refers to average FTR which has four fuzzy sets; these are very low (VL), low (L), high (H), and very high (VH). This has more fuzzy membership sets due to its relative importance for security to counter different ratios of attacks.
* FV or fitness value has four fuzzy sets of lower (L), normal (N), and upper (U) from 0 to 100.
## 2 Materials and Methods
### Equipment
#### 2.2.1 Electromyography  
Electromyography (EMG) was measured for five major muscles (Figure 2) of the right leg (rectus femoris (RF), medial gastrocnemius (MG), lateral gastrocnemius (LG), tibialis anterior (TA), and peroneus longus (PL) muscles) in relation to operating the accelerator pedal [6; 9; 16; 17]. The RF is involved in hip flexion and knee extension. Both the MG and LG relate to knee flexion and ankle extension. The TA and PL are involved in ankle flexion/inversion and ankle extension/version, respectively. It could be noted that the ankle flexion and extension are also well known as dorsiflexion and plantarflexion, respectively. A TeleMyo DTS surface EMG system (Noraxon Inc., Scottsdale, AZ, USA) was utilized for the EMG measurements using disposable EMG electrodes (10 mm diameter; 3M Korea Ltd., Seoul, Korea). The attached sensors on the muscles of the right leg are shown in Figure 3. In addition, the sampling rate was set to 1500 Hz. All raw EMG data were root mean squared (RMS) with a window span of 100 msec, and rectified.  
Maximum voluntary contraction (MVC) was measured in accordance with the Caldwell protocol [18] to calculate %MVC. The MVCs for each muscle were recorded while the participants were maintaining their maximum right leg forces. For instance, the MVC of the RF muscle was measured when a participant fully extended his leg. Each participant was requested to maintain his maximum force for approximately 5 seconds, and the EMG data for 3 seconds in the middle was used in calculating the MVC.
# Calculation and Verification of the Real-Time Working Characteristics of a Viscous Coupling
## 3 The Temperature Calculation of the VC at Working Course
### The Convective Heat Transfer Coefficient for the Parts of VC
#### 3.1.3 The Coefficient of Average Convectional Heat Transfer between the Outside Air and the Shell  
Regarding \(\alpha_{3}\) as the coefficient of the average convectional heat transfer, we see:  
\[\alpha_{3}=\frac{N_{u}\gamma_{A}}{D_{ho}} \tag{13}\]  
where  
\[N_{u}=K_{3}\Big{[}\Big{(}0.5R_{e}^{2}+G_{r}\Big{)}P_{r}\Big{]}^{0.315} \tag{14}\]  
\[R_{e}=\frac{\omega_{1}D_{ho}^{2}}{\
u_{A}} \tag{15}\]  
\[G_{r}=\frac{g\beta_{A}D_{ho}^{3}(t_{ho}-t_{A})}{\
u_{A}^{2}} \tag{16}\]  
\[P_{r}=\frac{C_{pA}\rho_{A}v_{A}}{\gamma_{A}} \tag{17}\]  
In the above, \(D_{ho}\)--the outer diameter of the shell; \(t_{ho}\)--the outer surface temperature of the shell; \(t_{A}\)--the temperature of the air; \(\beta_{A}\)--the thermal expansion coefficient of the air; \(v_{A}\)--the kinematic viscosity of the air; \(C_{pA}\)--the specific heat coefficient of the air at constant pressure; \(\rho_{A}\)--the density of the air; and \(\gamma_{A}\)--the thermal conductivity coefficient of the air.  
3.1.4. The Coefficient of Average Convectional Heat Transfer between the Outside Air and the Axle  
Regarding \(\alpha_{4}\) as the coefficient of the average convectional heat transfer, we see:  
\[\alpha_{4}=\frac{N_{u}\gamma_{A}}{D_{ao}} \tag{18}\]  
where  
\[N_{u}=K_{4}\Big{[}\Big{(}0.5R_{e}^{2}+G_{r}\Big{)}P_{r}\Big{]}^{0.315} \tag{19}\]  
\[R_{e}=\frac{\omega_{2}D_{ao}^{2}}{\
u_{A}} \tag{20}\]  
\[G_{r}=\frac{g\beta_{A}D_{ao}^{3}(t_{ao}-t_{A})}{\
u_{A}^{2}} \tag{21}\]  
\[P_{r}=\frac{C_{pA}\rho_{A}\
u_{A}}{\gamma_{A}} \tag{22}\]  
In the above, \(D_{ao}\)--the outside diameter of the axle; and \(t_{ao}\)--the outer surface temperature of the axle.  
3.1.5. The Coefficient of Average Convectional Heat Transfer between the Silicone Oil and the Cap  
Regarding \(\alpha_{5}\) as the coefficient of the average convectional heat transfer, we see:  
\[\alpha_{5}=\frac{N_{u}\gamma_{s}}{(D_{e}/2)} \tag{23}\]  
where  
\[N_{u}=K_{5}\Big{(}R_{e}^{2}+G_{r}\Big{)}^{0.25} \tag{24}\]  
\[R_{e}=\frac{\omega_{1}(D_{e}/2)^{2}}{\
u_{s}} \tag{25}\]  
\[G_{r}=\frac{g\beta_{s}(D_{c}/2)^{3}\pi^{3/2}(t_{s}-t_{ci})}{\
u_{s}^{2}} \tag{26}\]  
In the above, \(D_{c}\)--the diameter of the cap; \(t_{ci}\)--the temperature of the inside face of the cap (which contacts the silicone oil).  
3.1.6. The Coefficient of Average Convectional Heat Transfer between the Cap and the Air  
Regarding \(\alpha_{6}\) as the coefficient of the average convectional heat transfer, we see:  
\[\alpha_{6}=\frac{N_{u}\gamma_{a}}{(D_{c}/2)} \tag{27}\]  
\[N_{u}=K_{6}\Big{(}R_{e}^{2}+G_{r}\Big{)}^{0.25} \tag{28}\]  
\[R_{e}=\frac{\omega_{1}(D_{c}/2)^{2}}{\
u_{a}} \tag{29}\]  
\[G_{r}=\frac{g\beta_{a}(D_{c}/2)^{3}\pi^{3/2}(t_{co}-t_{A})}{\
u_{a}^{2}} \tag{30}\]  
In the above, \(D_{c}\)--the diameter of the cap; \(t_{co}\)--the temperature of the outside face of the cap (which contacts the air).
# On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN
## 4 Experimental Results
### Single-Device Maximum Throughput  
Throughout is defined as the rate at which a successful message is delivered through a communication channel. It is commonly measured in bits per second (bit/s or bps), data packets per second (p/s or pps), or data packets per time slot (data packets per time slot). The throughput of a communication system can be affected by a variety of factors, including the limitations of the underlying analog physical medium, the available computing capacity of system components, and end-user behavior. In the case of WiFi, the ISP and devices upstream of the APs limit the amount of data that may be sent to the Internet. Furthermore, because 802.11 is a shared medium, it is constrained by other wireless devices. The testing here in this paper was to assess the maximum throughput of a single device can be achieved in a RoLa module (just focus on analog physical medium). It is obviously shown in Figure 20 that the results depend on the payload size being sent. Consistent experimental results were found with [39] as well as the same result description.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 2 Technologies for LV Smart Metering--Background Information
### Wireless Technologies  
#### 2.2.1 ZigBee  
A wireless technology that is widely used in smart meter applications is ZigBee, which has its physical and MAC layers based on the IEEE 802.15.4 standard. Similarly to the NB-PLC, when it comes to smart meters, it is usually preferred for the telecommunication link between the smart meter and the data concentrator rather than the link between the data concentrator and the control centre. It operates in the 2.4 GHz band (worldwide) and its range is between 10 and 100 m, depending on the output power and the environmental conditions [27; 28].  
It is suitable for applications with low data rates and low power consumption and it is appropriate for wireless control and monitoring applications. Some typical applications include building automation, home automation, wireless sensor networks, smart lighting, Advanced Meter Reading (AMR), _etc._ ZigBee entails a low cost of deployment as well as low complexity. Regarding the AMR applications, one of the advantages of Smart meters, into which the ZigBee protocol is embedded, is that they can easily communicate with smart in-home devices, like smart appliances and home displays. On the other hand, ZigBee experiences a high interference ratio from other applications that use the same bandwidth, which could cause system degradation [6; 27].  
The ZigBee protocol can be also chosen for building a wireless mesh. This is a communication network made up of radio nodes in a mesh topology. The nodes can be all connected to each other, leading to a fully meshed topology or partially connected, leading to a partially meshed topology. The concept of a meshed network indicates that when a node fails, then the information data can be forwarded through an alternative path to the destination. Every smart meter in a mesh network is considered as a node and data is routed through them [6; 29]. Figure 2 depicts a mesh network that consists of smart meters. It is shown that in the case of a fault, an alternative route is found for the information to be transmitted.
## 3 Undifferentiated Cells (Stem Cells)
### Embryonic Stem Cells (ESCs)
#### 3.1.2 Application in BTE  
Human ESCs are a suitable source for bone repair and regeneration applications since they can differentiate to unlimited numbers of all specialized lineages of the healthy bone [73]. The possibility of derivation of osteoblasts from hESCs has been under the influence of ascorbic acid, \(\beta\)-glycerophosphate, and dexamethasone [74]. In addition, it is possible to generate osteogenic cell lineages from ESCs without the need to prepare embryonic bodies [75]. The potential of these cells regarding the endochondral bone regeneration in vivo has also been shown in a study conducted by [PERSON] et al. in 2008 [76]. However, the risk of tumor formation (tumorigenicity) in the recipients is the main drawback regarding the use of hESCs for the clinical setting. Thus, the differentiation of them into progenitors is suggested as an alternative to overcome the problem mentioned above. In this regard, [PERSON] et al. produced hESCs-derived mesodermal progenitors (hES-MPs) and compared their osteogenic potential to BM-MSCs [77]. They found that transcriptional modifications occurring during hES-MP derivation lead to a gene expression profile, which is highly similar to that of hMSCs. The authors concluded that the hES-MPs with the immune privileged characteristics (low expression of HLA class II) and increased proliferation properties could serve as a valuable cell source for tissue engineering application. In another study, [PERSON] et al. produced murine ESCs-derived osteoblasts for using in impaired fracture healing in the mouse [78]. For this aim, they loaded the prepared cells in a  
Figure 4: Schematic representation of embryonic stem cells (ESCs) differentiation. Human ESCs could differentiate into various cell types with origins of endoderm, mesoderm, and ectoderm. Reproduced with permission from Reference [72].  
3D collagen I gel and implanted the cells into the ovariectomized fractures. The authors observed the stem cell-derived osteoblasts at the fracture site after four weeks of the implantation through in situ hybridization histochemistry. The in vivo micro-computed tomography (uCT) imaging and histology analyses confirmed that ES cell-derived osteoblasts loaded on collagen I matrix can improve bone formation and restore fracture healing in vivo.
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 4 Results and Discussion
### Evaluation of Remote Sensing Mapping Methods for Tea Plantation Distribution  
This study comprehensively explored the high-precision mapping of Anji tea plantations through the cross-combination of three different semantic segmentation deep learning methods and three distinct remote sensing image datasets. Accuracy verification methods based on sample points and quadrats were employed to thoroughly compare and analyze the mapping capabilities of these methods and datasets, providing valuable references for spatial mapping of tea plantation distribution at a regional scale.  
In terms of classification methods, it is evident that the SE-UNET and SWIN-UNET models, which incorporate attention mechanisms, exhibit superior tea plantation recognition capabilities compared to the standard UNET model. Notably, the SWIN-UNET model achieved the highest classification accuracy for tea plantations in most scenarios. SWIN-UNET combines the advantages of Swin transformers and UNet deep learning structures, enhancing the extraction of contextual and global features, which is beneficial for high-precision recognition of tea plantations in complex scenes. The Swin transformer is an improved version of the ViT (vision transformer), introducing a hierarchical structure similar to CNNs, allowing the transformer to better handle local features. Through inter-window partitioning and linear transformation, the Swin transformer facilitates information interaction at different scales, reducing computational complexity.
# A Fully Automatic DEXP Method for Gravity Data and Its Application on a Potash Salt Deposit
## 2 Methodology
### Theory of the Automatic DEXP Method
#### 2.2.3. Hypothetical Observed Surface Method  
This paper proposes the hypothetical observed surface method as a means of constructing half-space field data. The primary objective of this method is to suppress the influence of undulating terrain while ensuring the automatic calculation and reliability of the DEXP method.  
\(S_{obs}\) is the observed surface, \(S_{obs}(x_{i},y_{j},z_{obs})\) is the observed data that are located on the undulate surface, \(S_{max}\) surface is built to go through the highest height point of \(z_{obs}\), and \(S_{min}\) is through the lowest height point of \(z_{obs}\). Therefore, the \(S_{min}\) surface is horizontal. Furthermore, with the upward continuation of \(S_{max}\) in different heights, we can obtain a series of surface values (\(S_{1}\), \(S_{2}\),..., \(S_{k}\)). This means that \(S_{k}\) can be achieved with an upward continuation process with horizontal surface \(S_{max}\). Therefore, it can achieve the Fourier form of point \(S_{k}(x_{i},x_{j},z_{k})\), which locates in the half-space (shown in Figure 1).  
Thus, with the process of the Fourier transform and the upward continuation, we obtain \(\widetilde{F}(S_{obs}(x_{i},y_{j},z_{obs}))\), as stated in Equation (12), and then \(S_{k}\) can be obtained as stated Equation (13):  
\[\widetilde{F}(S_{k}(x_{i},y_{j},z_{k}))=\frac{\widetilde{F}(S_{obs}(x_{i},y_{ j},z_{obs}))\cdot e^{(z_{k}-zmax)\sqrt{\widetilde{x_{i}}^{2}+\widetilde{y_{j}}^{2}} )}}{e^{(z_{obs}-z\sqrt{\widetilde{x_{i}}^{2}+\widetilde{y_{j}}^{2}}}} \tag{12}\]  
\[S_{k}(x_{i},y_{j},z_{k})=F^{-1}(\widetilde{F}(S_{k}(x_{i},y_{j},z_{k})) \tag{13}\]  
In summary, the imaging process of the proposed improved DEXP method consists of three main steps, as described in this paper. Firstly, the elevation grid and G/M (gravity data or magnetism data) grid are read and defined. The first step involves using the hypothetical observed surface method to construct the 3D half-space field data. Next, the second step involves calculating the depth of extreme points using the adding constant method. Finally, the last step is the normalization process, as stated in Equation (8). Following these imaging steps, the imaging result can be analyzed to extract target characteristics, combine the other  
Figure 1: \(S_{obs}\) is the observed surface, \(S_{obs}(x_{i},y_{j},z_{obs})\) is the observed value at the location \((x_{i},y_{j},z_{obs})\). \(S_{max}\) is the surface through the highest height of \(z_{obs}\) and \(S_{min}\) is the surface through the lowest heightinformation (geophysics data, logging, metallogenic theory, geology evolution, and so on), and give the proper geological inference. Figure 2 illustrates the overall workflow of these steps.
# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 3 Background: iSDVN System
### Routing in a Multi-Access Environment with Learning Approaches  
Routing is an important aspect of SDN and VANETs, as it determines how packets are routed from their source to their destination. VANETs may utilize heterogeneous wireless technologies in multi-access environments, including DSRC, IEEE 802.11p, cellular networks, and wireless LANs [94].  
SDN and VANET routing usually uses learning approaches to improve efficiency and adaptability based on varying conditions and dynamic network conditions. Routing in SDN and VANETs commonly uses the following learning techniques:  
1. _Reinforcement Learning_--In reinforcement learning (RL), agents learn by interacting with the environment to make decisions. According to the observed performance of different routes over time, RL can be used in SDN and VANETs to adapt routing decisions. As a result, the network can learn which routes are more reliable, have lower latency, or have a higher throughput.
2. _Deep Learning_--By using deep-learning techniques, such as neural networks, we can identify patterns and correlations in SDN and VANET data that traditional routing algorithms would not detect. The trained models can support better routing decisions, especially in dynamic and complex SDN and VANET scenarios.
3. _Context-Aware Routing_--The concept of context-aware routing involves making routing decisions based on various contextual factors, such as vehicle speed, traffic density, and link quality. Routing metrics can be dynamically adjusted using machine-learning algorithms based on the current context, improving route selection.
4. _Federated Learning_--The privacy and security of SDN and VANETs are crucial considerations. A federated learning system enables vehicles to train a model without sharing raw data with a centralized entity [95]. This approach can enhance routing decisions while protecting individual vehicle privacy.
5. _Online Learning_--In SDN and VANETs, routing algorithms have to be capable of quickly adapting to changing conditions due to their dynamic nature. Since online learning can be applied to VANETs, where the network topology can change rapidly, online learning techniques are suitable for continuously updating routing decisions.  
Using these learning approaches, multi-access VANETs can significantly improve routing efficiency, reliability, and adaptability. Learning-based routing algorithms pose a number of challenges, including computational overhead, scalability, and security concerns. Real-world testing and validation are also crucial for these approaches to be effective and robust in dynamic vehicular scenarios.