esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### In-Site Downhole Testing Results  
In addition, the conventional in-site downhole testing method was also employed to directly obtain the measurements of compression (P-) and shear (S-) wave velocity as a comparison. Figure 8 shows the prepared borehole with a diameter of 127 mm for downhole testing according to the ASTM standard test method [30]. Four boreholes were drilled 15 m on the ground to put the downhole receivers. A 14-bit ADC measurement equipment (XG-1 type 3-axis receiver manufactured by Langfang Dadi corporation, Ltd. Langfang, China) was employed to acquire the wave velocities through a sampling frequency of 1000 Hz. After the 3-axis receiver were placed at the desired test locations in the downhole, the energy source is activated to generate ground motion, the waves were recorded three times by the receivers to improve the signal-to-noise ratio (SNR). The experimental results are listed in Table 1. There are 3 kinds of soil layers: topsoil layer, silt and clay layer, and sand layer with various depths at different locations. The S-wave velocities of the top two layers are identified in the ranges between 150 to 200 m/s, and the P-wave velocity is in the range of 400-500 m/s, as shown in Table 1. The results of in-site observation provide sufficient evidence to the surface wave identification methods for both the proposed FAWS method and the conventional even-spaced geophone array. As we can see, the results are in good agreement with the previous estimation results obtained by the proposed method. Considering the time-consuming drilling, complicated implement, and redundant observations, the proposed FAWS technique is an effective and efficient game-changing technique with accurate estimation of the wave velocities of soil properties in actual engineering applications.  
Figure 7: Identification results of the soil properties computed by the proposed FAWS approach and random-placed geophone array: (**a**) distribution of the randomly activated geophones; (**b**) f-k spectrum; and (**c**) dispersion image of the soil properties.
## 3 Analytic Methods  
To calculate the equivalent stiffness of each group of boulders in Yuanjue Cave, the distributions of boulders in Yuanjue Cave are different, and there are cases of overhanging crushing and fracturing, etc. The stress conditions in the cave are complicated. In this paper, we proposed an equivalent stiffness method for calculating the stability of boulders [44; 45]. Based on the definition of equivalent bearing stiffness and the main distribution of supporting boulders in the roof of the cave, we equated the boulders in Yuanjue Cave into series and parallel arrangements and established analytical solutions for the effective bearing stiffness of boulders in series and parallel forms under different contact areas on the upper and lower surfaces of the boulders. The effective bearing stiffness of each stone grouping was obtained after the contact relations of the stones in the cave were fully counted. The effect of the number of overhanging, broken, and damaged slates on the force-deformation characteristics and stability of the top slab of Yuanjue Cave was revealed through numerical simulation, which is shown in Figure 4.  
(1) Calculation of equivalent stiffness of the sized slate  
Knowing the strip area \(A\), the strip height \(H\), the strip modulus of elasticity \(E\), and the stress \(S\) on the strip, the equivalence of the support stiffness is calculated as follows (1):  
\[K=\frac{F}{\Delta H}=\frac{SA}{\epsilon H}=\frac{EA}{H} \tag{1}\]  
The slates in direct contact with the roof slab were grouped according to their distribution (as shown in Figure 5), and the overall stiffness of the slate and all its lower supporting slates is taken as the equivalent supporting stiffness of the group. Figure 3: Development of internal slates and fissures in Yuanjue Cave. The method of direct contact between the strips and the top plate can be mainly divided into two basic forms: series and parallel connection, as shown in Figure 6.  
According to the definition of equivalent support stiffness, the calculation formulae are derived as follows:  
1. Tandem arrangement  
\[\Delta H=\frac{F}{K_{1}}+\frac{F}{K_{2}}+\frac{F}{K_{j}} \tag{2}\]  
Substituting Equation (2) into Equation (1), we obtain  
\[K=\frac{K_{1}K_{2}K_{j}}{K_{1}K_{2}+K_{2}K_{j}+K_{1}K_{j}} \tag{3}\]  
If there is no joint, its equivalent elastic modulus is  
\[E=\frac{E_{1}E_{2}}{\alpha_{1}E_{2}+(1-\alpha_{1})E_{1}} \tag{4}\]  
where, \(\alpha_{1}=h_{1}/(h_{1}+h_{2})\), and \(E_{i}\) is the modulus of elasticity of the \(i\)th strip of stone. Figure 4: Flow chart showing the available data and methods used in this study. Figure 5: Schematic diagram of the grouping of supporting slates in the lower part of the roof plate. Figure 6: Sketch of equivalent support stiffness calculation. 2 Parallel arrangement  
It is assumed that when arranged in parallel, the deformation of the two stones below is equal, i.e., only vertical displacement of the upper slate occurs, and no deflection occurs.
Recently, polyvinylidene fluoride (PVDF) has gained ample attention in self-powered piezoelectric devices due to its incredible electroactive properties such as piezoelectricity, ferroelectricity and pyroelectricity with high chemical stability [24]. PVDF is an inexpensive, highly nonreactive and a flexible polymer [25] with four crystalline phases, namely \(\alpha\), \(\beta\), \(\gamma\) and \(\delta\), based on the chain formation [26; 27; 28]. Among these phases, \(\alpha\) is non-polar and does not show any piezoelectricity. In order to enhance the piezoelectricity, the \(\beta\) and \(\gamma\) phases can be generated by nanoparticle doping [29], 2D metal dichalcogenides [7], electrospinning process [30] etc. In this regard, graphene oxide (GO) has shown extensive applicability due to its superior property to induce the nucleation of \(\beta\) and \(\gamma\) phases by the electrostatic interaction and/or hydrogen bonding between the oxygen functionalities of GO and PVDF as well as its superior mechanical, thermal and electrical properties [31]. Herein, we demonstrate the efficacy of an in situ poled highly flexible piezoelectric PVDF GO hybrid nanofibers in different alignment modes for multifaceted applications from locomotion sensor to signal monitoring. Here we exploited the simplest and most versatile method, called electrospinning, to fabricate the in situ poled nanofibers by transforming non-polar \(\alpha\)-phase of PVDF to polar \(\beta\)-phase structures for piezo-electricity under high bias voltage [32; 33; 34]. The flexible piezoelectric device fabricated using the aligned mode generates a highly enhanced output voltage of 2.1V at a mechanical vibration frequency of 8 Hz. The effective piezoelectric transduction exhibited by the proposed system enables its multiple efficacies as a locomotion detector, bio-e-skin, smart chairs and so on.
# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 5 Performance Evaluation
### Study on the Nnumber of SPs  
As we mentioned above, we first calculate the number of SPs according to the transmission range and network size. However, other numbers of SPs are also feasible and they will result in different impacts. We use the same network to execute TRP-MC with different numbers of SPs. We first focus on the coverage rate and overlapped coverage rate of sensors. The simulation result is demonstrated in Figure 13. We can see that with the number of SPs increasing, the coverage rate also raises. When the number of SPs exceeds 15, the coverage rate almost does not change. The overlapped coverage rate hardly changes when the number of SPs is under 15. Therefore, 15 is a suitable number of SPs for the applied network model.
# Design of a Technique for Accelerating the WSN Convergence Process
## 3 Theoretical Principles Used in Research
### Implicit Backup Routing-ADDV (IBR-AODV)  
Like the previous mechanism, this one also extends the functionality of the AODV routing protocol. This mechanism works on the principle of overhearing transmitted data by neighboring nodes. If a node supporting such a mechanism is within the range of three nodes of the primary route, it creates an alternative routing table after interception and stores the \"overheard\" data in a buffer. The following shows the principle of creating a backup node. The primary route is S -> R1 -> R2 -> D (Figure 5), and node R3 is a backup node because it is within range of nodes S, R1, and R2, from which it listens for data communication [67; 68].  
In the case when a failure occurs on the primary route, for example, one of the nodes moves away or fails completely, the backup node detects this and initiates the start of route recovery. The backup node sends a Route Change (RC) packet to the source node or to a node trying to transfer data. This node updates its routing table and sends an Acknowledgment (Ack) of receiving the RC to the backup node. After receiving the Ack, the backup node loads the data from the alternative routing table into the main routing table and deletes the alternative. Subsequently, the backup node sends the data to the next node, which updates its routing table. After this step, the data routing is fully moved to the backup route. Such a case is shown in Figure 6, when node R1 fails and the primary route S -> R1 -> R2 -> D changes to the backup S -> R3 -> R2 -> D after recovery.
# A Mobile-Based System for Detecting Ginger Leaf Disorders Using Deep Learning
## 2 Related Work
This work adopted traditional computer vision and image-processing techniques for leaf disorder identification. Farmers can capture plant leaves using the deployed system connected to a digital or web camera. Furthermore, image-processing techniques are used to determine the affected part. Farmers are informed of the disease type via a global system for mobile communications (GSM) interface. The relay then activates the device's pump, releasing the appropriate medication to treat the affected plant's condition. The implementation results show that SVM and k-means algorithms produce better results than traditional methods. However, the used dataset is insufficient to generalize the technique, and ginger diseases with pest attacks are not considered.
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 5 Conclusions and Future Work  
To enhance the bandwidth utilization of 5G mobile networks across various scenarios, ensure fairness among different protocol flows, and minimize link delay, we propose an SDN-based in-network congestion control algorithm. Our approach employs OpenFlow to  
Figure 12: Queuing delay test results.  
gather bottleneck bandwidth information within the network and integrates a congestion detection module into the SDN controller to handle burst traffic and rapidly alleviate congestion. We introduce an SDN-based centralized bandwidth allocation scheme to promote fairness among diverse protocol flows. By centrally controlling each flow at the bottleneck bandwidth and rationally allocating bandwidth, our method reduces queuing delay and packet loss caused by routing buffer overflow while maximizing bandwidth usage and improving flow convergence speed. Simulation results demonstrate that our BRF protocol outperforms commonly used CUBIC and BBR protocols in terms of bandwidth utilization, fairness, convergence speed, packet loss rate, and queuing delay. Specifically, the throughput of the BRF protocol is 25.6% higher than that of the BBR protocol and 912% higher than that of CUBIC flow (buffer size = 100,000 packet, packet loss = 2%, RTT = 20 ms). The fairness index of BRF flow when competing with CUBIC flow is close to 1, while the fairness index of BBR flow when competing with CUBIC protocol flow is 0.625, showing the better fairness of the BRF protocol. The convergence time of BRF flows is 30 percent of that of BBR flows.  
In future work, we will explore more complex simulation scenarios to analyze our algorithm's performance further while testing it in real-world settings. We also need to explore the efficiency and security issues of the BRF protocol in the 6G scenario [34]. In addition, we will improve our design to achieve better bandwidth allocation strategies in the case of multiple different competing CC algorithms.  
D.Y.: methodology, software, writing--original draft, writing--review and editing, data curation, validation. W.-T.T.: resources, supervision, project administration, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China (No. 2018 YFB1402700), the Beijing Municipal Natural Science Foundation (No. 9142012 and 9152009) and the National Natural Science Foundation of China (No. 61472032, No. M1450009, No. 71271013 and No. 61462003).  
The authors are unable or have chosen not to specify which data have been used.  
The authors declare no conflict of interest or personal relationships that could have appeared to influence the work reported in this paper.
# Actuation Mechanisms and Applications for Soft Robots: A Comprehensive Review
## 2 Actuation Materials and Mechanisms for Soft Robots
### Electroactive Polymer (EAP)
#### 2.3.2 Liquid Crystal Polymer  
Liquid crystal polymers are materials that combine the properties of polymer elastomers and liquid crystals. They exhibit large, rapid, and reversible changes in shape after applying certain external stimuli (an example is illustrated in Figure 7[128]). Meanwhile, they are between ordered crystalline solids and isotropic liquids. While maintaining a certain degree of molecular order, liquid crystals have similar fluidity as liquids[128]. These external stimuli include temperature, light irradiation, or electronic devices (heating by resistance or direct piezoelectric response)[129; 130].  
Figure 6: (**a**) A DEA robot[120]. (**b**) The movement mechanism. (**c**) The moving states of each robotic part in the time intervals.  
Figure 7: A liquid crystal polymer-based soft robot actuated via NIR light to mimic a human arm to grasp, lift up, lower down, and release an object[128].  
The most important application of liquid crystal elastomers for soft robot design is light-driven robots. [PERSON] et al. presented a millimetre-scale snail robot with liquid crystal elastomer materials [131]. The position where the elastomer material deforms and bends changes with the position of laser irradiation. After one cycle of laser irradiation, the elastic body moves to a certain distance at a speed of 0.3 mm/s, and then it will proceed to the next cycle. Therefore, the snail can continue to wriggle. A wave-shaped robot, which was reported in [132], curls and crawls like a caterpillar under the light. After the robot absorbs the green light, thermal actuation is triggered and the length shrinks to 20% of the original length, resulting in a total tensile stress of 2.5 kPa. The uniqueness of the robot is its small size (14.8 mm in length) and it can pass through a gap of 0.9 mm. In addition, with a CW power of 2.5 W and 0.4 Hz scanning beam frequency, the robot walks for a distance of 14.2 mm at an average speed of 0.24 mm/s within 59 s. A transporter robot presented in [133] fuelled by light consists of four light-driven liquid crystalline polymer films that play the roles of legs. The robot has an overall length of 2 cm and a weight of 20 mg. In one motion cycle, it can move forward by 4 mm at a speed of 0.5 mm/s. It is a fully light-responsive transporter robot with controlled multi-freedom locomotion and the capability to deliver loads (about 50% of its weight). [PERSON] et al. designed a bionic hand by assembling a photonic film in a liquid crystal elastomer material. It can trigger finger bending and deformation under visible light [134]. It can be imagined that a soft robot that can move quickly and continuously through light-triggered photonic actuators and liquid crystal elastomer photonic films.
## 6 Timing Constraints
}\left[\frac{\Theta_{i}^{w-1}(j)}{P_{h}}\right]\times T _{TXDh},\quad\text{for $w>1$}\]  
Note that interference \(\Theta_{i}\) is modelled as function of the active period of cluster-head \(CH_{j}\), because message \(M_{i}^{k}\) suffers interferences of multiple message streams \(\mathcal{S}_{bdow}^{j}\) every time it is forwarded along the cluster-tree path. The \(w\) iterations are performed until \(\Theta_{i}^{w}=\Theta_{i}^{w-1}\). Based on the interference time imposed by the highest priority message load, we derive the local worst-case response time (\(W_{i}\)) for message \(M_{i}^{k}\) as:  
\[W_{i}=\gamma_{i}+\Theta_{i}^{w} \tag{23}\]  
In addition, the message must traverse all the cluster-tree path from the source cluster until the PAN coordinator. Thus, the transmission delay towards the sink node takes into account the depth of the node that generated the message. Then, the total probabilistic worst-case response time (\(R_{i}\)) for message \(M_{i}^{k}\) as function of the network depth (regarding the PAN coordinator) of the generation node of this message stream is derived as:  
\[R_{i}=\gamma_{i}+\sum_{d=depth_{i}-1}^{0}\Theta_{i}^{w}(CH_{j}(d)), \tag{24}\]  
where \(CH(d)\) corresponds to cluster-head \(CH_{j}\) of depth \(d\) that is responsible for forwarding message \(M_{i}^{k}\) along the cluster-tree path. Moreover, the response time analysis is also dependent on the active period scheduling scheme. For this reason, it is added an additional delay that is dependent on the used scheduling scheme. For the bottom-up scheduling scheme, where the upstream traffic is prioritised, this worst-case delay corresponds to the sum of active periods of all clusters. For the top-down scheduling scheme, where the message needs to use multiple beacon intervals before reaching the PAN coordinator, we consider a pessimistic worst-case delay that corresponds to the difference between one beacon interval and the superframe duration of the responsible cluster for forwarding the message at each depth of the cluster-tree network until reaching the PAN coordinator. These assumptions are appropriate, because they encompass worst-case scenarios for the scheduling schemes. Therefore, the probabilistic worst-case response time (\(R_{i}\)) can be derived as follows:  
\[R_{i}= \begin{cases}\gamma_{i}+\sum_{d=depth_{i}-1}^{0}\Theta_{i}^{w}(CH_{j}(d ))+\sum_{j=1}^{N_{CH}}SD_{j},&\text{bottom-up scheme}\\ \gamma_{i}+\sum_{d=depth_{i}-1}^{0}\Theta_{i}^{w}(CH_{j}(d))+\sum_{d=depth_{i}- 1}^{0}(BI-SD_{CH_{j}(d)}),&\text{top-down scheme}\end{cases}  \tag{25}\]Therefore, a boundary equation (timing constraint) that must be satisfied by the cluster-tree network scheduling can be defined as follows:  
\[R_{i}\leq P_{i},\quad\text{for }\forall S_{i}\in\mathbb{S} \tag{26}\]
## 4 Results of The Analysis
### Spatial Configuration Analysis Results  
#### 4.2.1 City-Level Analysis Results  
At the city level, the analysis results presented visually in Figure 3 refer to the measured values determined by applying the reciprocal of the \(MD\) that was obtained from applying a weight to the turning angle of the segments that make up the network (segment angular integration). The measurements are visualized on a spectrum scale: the redder the segments that form a street network in our analysis, the higher the measured value of integration; and the bluer the segments, the lower the measured value of integration.  
Most of the traditional markets in Cheongju, including Yukgeori Market, are located along the north-south and east-west main roads. Spatial network analysis of the whole city at the city level showed that the two road axes are composed of segments with low mean depths and thus are highly integrated within the city's spatial system. Yukgeori Market is located in an area with a particularly high degree of integration, originating from a spatial configuration functioning as a transportation hub where the four main roads leading to all areas of Cheongju intersect. Along the four roads branching out from the entrance to Yukgeori Market to the north, east, southeast, and southwest, intra-city buses run routes crisscrossing the entire city of Cheongju. Around the market entrance, bus stops congregate, also serving as transfer stops.  
In particular, the bridge where one of the above four roads passes, connecting spaces of the city separated from each other and divided into eastern and western regions by the long Musimcheon stream over which the bridge passes, plays a major role in creating the spatial configuration in which the market is central and highly integrated.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Categories** & & & & & & **Contents** & & \\ \hline
**Sale Item** & **Agricultural** & **Livestock** & **Aquatic** & **Processed** & **Clothing** & **and Shoes** & **Housewares** & **Restaurant** & **Others** \\ \hline Number of & & & & & & & & \\ stores & 420 (35\%) & 120 (10\%) & 300 (25\%) & 84 (7\%) & 36 (3\%) & 120 (10\%) & 60 (5\%) & 60 (5\%) \\ (Percentage) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of stores by sale item in Yukgeori Market.  
Table 4 presents a comprehensive summary of the spatial network configuration and land-use pattern supported by this configuration for the entire city of Cheongju.  
At the city level, the characteristics of Yukgeori Market were as follows: it functioned as a transportation hub because of its location at the intersection of roads flowing into all parts of Cheongju and its proximity to the bridge over the Musimcheon stream that flows through the city. The market focuses on commerce and works as a connection point between the east and west of Cheongju, separated by the stream.
# Biosynthesis of Natural Rubber: Current State and Perspectives
## 2 Biosynthesis of Natural Rubber and the Involved Genes/Proteins
### Biosynthesis and Physiological Roles of Natural Rubber
Enzymes in MEP pathway: DXS, DXP synthase; DXR, DXP reductoisomerase; MCT, CDP-ME synthase; CMK, CDP-ME kinase; MDS, ME-cPP synthase; HDS, HMBPP synthase: HDR, HMBPP reductase; IDI, IPP isomerase. Intermediates in MVA pathway: A-CoA, acetyl-CoA; AA-CoA, acetoacetyl-CoA; HMG-CoA, hydroxymethylglutaryl-CoA; MevP, mevalonate-5-phosphate; MevPP, mevalonate pyrophosphate; IPP, isopentenyl pyrophosphate; DMAPP, dimethylallyl pyrophosphate. Enzymes in MVA pathway: AACT, A-CoA C-acetyltransferase; HMGS, HMG-CoA synthase; HMGR, HMG-CoA reductase; MK, mevalonate kinase; PMK, MevP kinase; MDC, mevalonate pyrophosphate decarboxylase. GPP, geranyl pyrophosphate; GPS, geranyl pyrophosphate synthase; FPP, farnesyl pyrophosphate; FPS, farnesyl pyrophosphate synthase; GGPP, geranylgeranyl pyrophosphate; GGPS, geranylgeranyl pyrophosphate synthase; TPT, _trans_-prenyltransferase; CPT, _cis_-prenyltransferase. Natural rubber seems like a \"dead-end product\" because it remains inside plant till its death and no enzyme that could degrade latex was found in plants. Then why would plants convert so much matter and energy into rubber biosynthesis? The physiological role of latex and rubber molecules remains a mystery. Latex was supposed to have functions in wound healing, disease-resistance and stress-tolerance, because defense and stress-related genes expand in the genome of rubber-producing plants and are expressed abundantly in latex compared with in other tissues [22; 23; 24]. Some studies demonstrated a lot of chemicals and proteins in the latex are toxic to herbivores and latex may capture and immobilize the mouth parts of insects due to its sticky nature [25; 26]. In _P. argentatum_ and _H. brasiliensis_, which have no isoprene synthase to eliminate excess photosynthate, rubber was suggested to serve as an excess assimilate sink to prevent damage to the photosynthetic apparatus under conditions of cold temperature, high light or other environmental stimuli [23; 27]. Additionally, signaling molecules such as ethylene and jasmonates, which play a major role in plant stress regulation, can induce laticifer differentiation and transient increase in latex production, also implying the involvement of latex in stress responses [28].