esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Carbon sequestration Rate Comparisons  
Mean carbon sequestration rates of the four mangrove hydrogeomophic settings studied here ranged from 47 \(\pm\) 4 to 162 \(\pm\) 5 g-C m\({}^{-2}\). year\({}^{-1}\). These are compared to other rates published in the literature in Table 3. [PERSON] [9] suggests the global average carbon sequestration rate for mangrove ecosystems is 174 g-C m\({}^{-2}\). year\({}^{-1}\). Our carbon sequestration rates ranged from 25% to 75% of that global average. Our rates are similar to other sequestration studies measured around the Gulf of Mexico. [PERSON] _et al._[24] found rates on the order of 69-99 g-C m\({}^{-2}\). year\({}^{-1}\), in Rookery Bay (immediately south of this study location) and 137 g-C m\({}^{-2}\). year\({}^{-1}\) in Terminos Lagoon, Mexico (Table 3). These lower rates, when compared to the world average, are not surprising given that the climate of the Southern Florida coastline, according to the Holdridge Life Zone classification, is both subtropical and warm temperate [61]. The mangroves on some of Florida's coastline actually experience and survive occasional frosts that are rare in subtropical regions. In the Florida Everglades, 100 km east of our study site, [PERSON] _et al._[58] found similar rates of carbon sequestration (123 \(\pm\)19 g-C m\({}^{-2}\). year\({}^{-1}\)) rates comparable to our reference tidal Creek, which averaged \(113\pm 19\) g-C m\({}^{-2}\). year\({}^{-1}\). Their study represented a relative homogenous landscape and similar hydrogeomorphic conditions (coring sites were spaced within 200 m) while ours represented a gradient of hydrologic energies in differing hydrogeomorphic environments. Our reference riverine and fringe mangrowes near Naples Bay exhibited higher rates of carbon sequestration. Our study further provides evidence for the high variability of carbon sequestration in different geomorphologies.
# A Miniaturized Circularly-Polarized Antenna for In-Body Wireless Communications
## 3 Antenna Analysis
### CP property of the Antenna  
As shown in Figure 2, perturbation slots are introduced in the patch to strengthen the CP performance. With reference to Figure 8, it can be seen that the small slots take critical role in the impedance matching and circular polarization. For the purpose of visualizing how the circular polarization is generated, the simulated surface current distributions on the patch at 2.45 GHz for four moments of 0T, T/4, T/2, and 3/4T are demonstrated in Figure 9. With the increment of time by a step of T/4, the currents rotate clockwise, transmitting LHCP waves in the boresight direction.  
Figure 8: Performance of the proposed antenna with/without perturbation slots: (**a**) Reflection coefficient; and (**b**) AR.  
Figure 9: Current distributions on the patch at: (**a**) \(t=0\)T; (**b**) \(t=T/4\); (**c**) \(t=T/2\); and (**d**) \(t=3\)T/4.
## 3 Description of System Performance Indicators
### Probability of Packet Timeout for Information Transmission between Single Node  
A packet transmitted from sensor node \(N_{k}\) to the BS may face a timeout or transmission error due to the complexity of the channel state. We use \(T_{k,succ}^{(v)}\) to denote the successful packet transmission event [22], which happens when the time of packet transmission \(T_{k}^{(v)}\) is less than the threshold time \(t_{k,out}\). Here, \(t_{k,out}\) is the transmission threshold of a packet, and \(T_{k}^{(v)}\) is defined in (6). According to the system definition, the information about the channel condition during both energy harvesting and packet transmission determines the time of packet transmission, which is a random variable. We can rapidly determine the CDF of sensor node \(N_{k}\) transmission time employing the definition of probability as follows:  
\[F_{T_{k}^{(v)}}(t)=\Pr\Bigl{\{}T_{k}^{(v)}<t\Bigr{\}}. \tag{12}\]  
Figure 3: The allocation of energy harvesting time and packet transmission time in the HHT protocol.  
In accordance with the channel assumption, we set \(t_{out,k}=t_{k}T=t_{out}\)[22], which denotes that the packet transmission time established by the system is equal to the timeout threshold. In particular, the probability that the packet transmission time is longer than the timeout threshold \(t_{out}\) is defined as the packet timeout probability, and the expression is as follows:  
\[\mathscr{O}_{k}^{(v)}=\Pr\Bigl{\{}T_{k}^{(v)}\geq t_{out,k}\Bigr{\}}=1-F_{T_{k} ^{(v)}}\bigl{(}t_{out,k}\bigr{)}. \tag{13}\]
# A Novel Method of Local Anode Effect Prediction for Large Aluminum Reduction Cell
## 5 Experimental Verification  
### Data Acquisition  
To predict the local anode effect, it is necessary to obtain the current data of aluminum electrolysis anode rod. The research group has designed a distributed anode current monitoring system for aluminum reduction cell for data acquisition, transmission, storage, and display. The system block diagram is shown in Figure 8. The anode current collector can collect the anode current in real time and monitor the ambient temperature in real time to ensure its good operating conditions, and it supports RS-485 communication. Each aluminum reduction cell contains 48 anode rods, and each anode rod is equipped with an anode current collector, which can monitor the current of each anode rods in real time. The intelligent gateway is the core part of the whole system, on the one hand, it receives and processes the instructions issued by the monitoring platform, coordinates each collector to complete the data collection, on the other hand, collects and stores the data collected by each collector and uploads it to the monitoring platform for real-time display. At the same time, the intelligent gateway has the function of interface conversion, and supports GPRS, 4G, WIFI, RS-485 and other communication methods. The monitoring platform is a man-machine interaction platform, which modifies the measurement parameters and receives the data sent by the intelligent gateway and displays them on the screen of the electric control cabinet. After many improvements and long-term debugging, it has been running stably for half a year in an electrolytic aluminum plant in southwest China, with good monitoring effect. The anode current collector takes into account the inherent noise of the circuit and external interference noise. After calibration, the measurement error in the actual field is 1.4% \(\sim\) 3.2%.  
Figure 7: Levels of local anode effect.  
Nine groups of global anode effect data of #2832 aluminum reduction cell were collected from 27 September 2020 to 1 April 2021, including the occurrence time, and anode rod current data 40 min before and 20 min after the occurrence. The occurrence time of anode effect is shown in Table 1, where the local anode effect area and the position of fluctuating anode rod are determined by current fluctuation detection.
## 2 Biological Activities of Monoterpenes
### Cyclic Monoterpens
Research done on cuminaldehydes effects on human colorectal adenocarcinoma (COLO 205) cells showed that this compound induces cell death through topoisomerase I and II inhibition [61]. An in vitro evaluation of cuminaldehyde treatment of COLO 205 cells demonstrated a notable decrease in cell growth and increase in cytotoxicity at \(\upmu\)M concentrations. After 24 h of cell treatment with 40 \(\upmu\)M cuminaldehyde there was a 50% decrease in cell viability. This compound was further tested against topoisomerase I and II to show the inhibitory relationship between these molecules. The degradation of monomeric DNA in the presence of 5-20 \(\upmu\)M cuminaldehyde indicates the interaction between this monoterenoid and the nuclear proteins. Furthermore, tumor reduction capacity was demonstrated using a COLO 205 xenograft in a nude mice model. Daily subcutaneous injection of 10 and 20 mg/kg cuminaldehyde resulted in 48 and 69 % tumor reduction respectively [61] with no notable loss in body weight. Although these findings are promising the lack of comparative data with healthy cell lines makes it difficult to evaluate this compound for its efficacy as a potential therapeutic. Hinokitiol (**12**, Figure 4) is a tropolone related, cyclic monoterenoid that has been reported to show activity in anti-inflammatory and antioxidant processes while simultaneously promoting apoptosis and autophagy in cancer cells [62; 63]. Western blot analysis of B16F10 murine melanoma cell line and 4T1 murine mammary carcinoma cell line showed this natural products potential of decreasing expression of heparanase, an enzyme known for its pro-metastasis effects in concentrations of 1 to 1250 nM. Furthermore, this research conducted in vivo studies on C57 BL/6J and BALB/c mice. The results showed an inhibition of tumor metastasis and prolonged survival [62]. While these results are promising, the  
Figure 4: Selected cyclic monoterpenes with reported antioxidant or anticancer activities. study did not analyze the effects of the monoterpene as a form of administered medication and instead injected B16F10 and 4T1 cells that had been pretreated in 1250 nM concentrations of hinokitiol into the mice. An independent study analyzed the cellular response of human osteosarcoma cell lines to treatment of hinokitiol in vitro. Cell viability assays of U2 OS and MG-63 human osteosarcoma cell lines showed a concentration dependent decrease in cell viability with hinokitiol treatments the 10-80 uM concentration range [63]. Furthermore, this research conducted western blot analysis of MG-63 cells showing that with treatment of hinokitiol results in PARP and caspase-3 cleavage both of which being known mediators of apoptosis. Cleavage of PARP was noted after treatment of hinokitiol in concentrations as low as 20 uM and demonstrated a concentration dependent increase through 80 uM [63] suggesting this compound induces programed cell death. Unfortunately, the study did not analyze any of the aforementioned phenomena on healthy cell lines.
# Autonomous Environment Generator for UAV-Based Simulation
###### 1 Introduction  
In recent years, the use of Unmanned Aerial Vehicles (UAV) has continued to rise due to increased applications in security, transportation, package delivery, geographic mapping, photography, and more [1]. While demand for UAVs has increased, the ability to efficiently test these UAVs repeatedly has remained an obstacle. Currently, UAVs are tested with in-person flight demos or 3D simulations. However, in-person flight demos can often be expensive and time-consuming, as testers may have to locate and arrive at a large area that is safe to test. Current 3D simulations allow for effective ways to test drone mobility and function but lack capacity for efficient environmental variance. Current simulations also require testers to manually place 3D models in the simulator, resulting in a increased downtime in repetitive testing cycles.  
This paper proposes a testbed that would address this 3D simulation problem. This platform directly uses satellite images to procedurally generate the entire 3D environment for the user, using image processing algorithms. With a single click, real-life streets and neighborhoods can become replicated entirely in the 3D simulation, resulting in a limitless variety of simulated environments. This will allow users to not only generate more accurate environments faster, but they will also be able to test their UAVs in specific real-world locations to more accurately replicate conditions for UAVs after the testing process. Thiscombination of components (online platform, machine learning, satellite images, robotics) has never been done before and offers novel levels of automation and environmental freedom.  
The organization of this paper is as follows. After this introduction, related works on this topic are presented. Following, the experimental setup and results are presented along with specific platform implementation details. Finally, the simulation results are presented, and the resulting data is analyzed.
## 2 Theoretical Background
### The Multiplicative Dimensional Reduction Method  
Consider a performance function \(y(\mathbf{x})\) of an \(N\)-dimensional input variable \(\mathbf{x}=[x_{1},\ldots,x_{N}]^{\mathrm{T}}\). Its logarithmic transformation in real space is defined as:  
\[\varphi(\mathbf{x})=\ln[|y(\mathbf{x})|] \tag{1}\]  
A univariate approximation of \(\varphi(\mathbf{x})\) with respect to an arbitrary reference point \(\mathrm{c}\) takes the following form:  
\[\varphi(\mathbf{x})=\sum_{i=1}^{N}\varphi(c_{1},\ldots,c_{i-1},x_{i},c_{1+1}, \ldots,c_{N})-(N-1)\varphi(\mathbf{c}) \tag{2}\]  
Evidently, it is obtained that  
\[\varphi(\mathbf{c})=\ln[|y(\mathbf{c})|] \tag{3}\]  
and  
\[\varphi(c_{1},\ldots,c_{i-1},x_{i},c_{1+1},\ldots,c_{N})=\ln[|y(c_{1},\ldots,c _{i-1},x_{i},c_{1+1},\ldots,c_{N})|] \tag{4}\]  
The inverse transformation of Equation (2) yields  
\[\begin{split} y(\mathbf{x})&=\exp[\varphi(\mathbf{ x})]\approx\exp\left[\sum\limits_{i=1}^{N}\varphi(c_{1},\ldots,c_{i-1},x_{i},c_{1+1}, \ldots,c_{N})-(N-1)\varphi(\mathbf{c})\right]\\ &\qquad=\exp[(1-N)\varphi(\mathbf{c})]\exp\left[\sum\limits_{i=1} ^{N}\varphi(c_{1},\ldots,c_{i-1},x_{i},c_{1+1},\ldots,c_{N})\right]\end{split} \tag{5}\]Substituting Equations (3) and (4) into Equation (5) yields the M-DRM approximation of \(y(\mathbf{x})\)[20], i.e.,  
\[y(\mathbf{x})\approx[y(\mathbf{u})]^{1-N}\prod_{i=1}^{N}y(u_{1},\ldots,u_{i-1},x _{i},u_{i+1},\ldots,u_{N}) \tag{6}\]
## 5 Discussion  
Different routing protocols are designed to enhance different performance metrics in WSN. Some of the major metrics are  
* Network Lifetime: The precious resource of WSN nodes is energy, and the lifetime of WSNs is completely dependent on the amount of energy the nodes contain. The network lifetime is defined as the total time the network is fully operative until the last nodes run out of energy. It depicts the total time of network operation.
* Network Throughput: It is an important parameter that estimates the overall successful data transfer rate.
* Residual Energy: Every device on the network requires a certain amount of energy to carry out various network activities. The total energy consumption is calculated as the total sum of energy used by all the nodes in the network. If it is assumed that nodes consume energy while sending data, then the total energy consumed is equal to the total number of packets sent in the network. To be more precise, the residual energy represents the overall network energy level after the operation is complete.
* Packet Delivery Ratio (PDR): PDR is the ratio between the total packets sent from the source to the number of packets received at the destination. The network performance is better for higher PDR values.
* Scalability: It is a key criterion of WSN studied to observe the impact of network size on network lifetime. As the routing of data in a large-scale WSN is conducted with nodes having limited resources, the routing process becomes a very challenging issue. Hence, an efficient and scalable routing protocol design becomes essential to constrain such limitations.
## 3 Numerical Validation of the Damping Estimation Methodology
### Validation of the Application of the Prony Method
#### 3.3.1. Cascalheira Bridge 3D Numerical Model  
The Cascalheira bridge is located at km 100.269 of the Northern Line of the Portuguese Railways that connects Porto and Lisbon. It is a filler-beam bridge with a span of 11.10 m length, whose structural details may be found in [PERSON] et al. (2018). The numerical model of this bridge was developed in the finite element software ANSYS(r) (2018). The elements used for the development of the model were: shell elements (SHELL63) to represent the concrete slabs, cantilevers, and the retaining walls; beam elements (BEAM44) to represent the embedded steel girders and the rails; and solid elements (SOLID45) to model the ballast and the sleepers and rail pads. The non-structural elements were modelled with concentrated mass elements (MASS21) and the bearing supports were simulated through spring-dashpot elements (COMBIN14) to consider the vertical and longitudinal stiffness of the pot bearings. Rigid beam elements (MPC184) were used to connect the deck to the ballast, and for the connection between the slabs of the deck and the cantilevers and retaining walls. The model is shown in Figure 7a, while a detailed description of the 3D FE numerical model can be found in (Kal  
stipulated in [2]. The damping coefficient was imposed for the two main global modes of the model according to the Rayleigh damping curve, as shown in Figure 7d.
###### Abstract  
Soil shear strength is an important indicator of soil erosion sensitivity and the tillage performance of the cultivated layer. Measuring soil shear strength at a field scale is difficult, time-consuming, and costly. This study proposes a new method to predict soil shear strength parameters (cohesion and internal friction angle) by combining cone penetration test (CPT) data and soil properties. A portable CPT measuring device with two pressure sensors was designed to collect two CPT data in farmland, namely cone tip resistance, and cone side pressure. Direct shear tests were performed in the laboratory to determine the soil shear strength parameters for 83 CPT data collection points. Two easily available soil properties (water content and bulk density) were determined via the oven-drying method. Using the two CPT data and the two soil properties as predictors, three machine learning (ML) models were built for predicting soil cohesion and the internal friction angle, including backpropagation neural network (BPNN), partial least squares regression (PLSR), and support vector regression (SVR). The prediction performance of each model was evaluated using the coefficient of determination (\(R^{2}\)), the root-mean-square error (\(RMSE\)), and the relative error (\(RE\)). The results suggested that among all the evaluated models, the BPNN model was the most suitable prediction model for soil cohesion, and the SVR model performed best in predicting soil internal friction angle. Thus, our findings provide a foundation for the convenient and low-cost measurement of soil shear strength parameters.  
Soil cohesion; internal friction angle; measuring device; prediction model; machine learning +
Footnote †: journal: Applied Sciences
## 3 Methods
### Modified Mann-Kendall Test  
The Mann-Kendall test is a widely used method for detecting trends in time series data [15]. It does not account for autocorrelation (the correlation between consecutive observations in a time series), which can lead to misleading results. The Modified Mann-Kendall (MMK) test is designed to reduce the effects of autocorrelation [82]. This test constitutes the fundamental principles of the original Mann-Kendall test while considering the presence of autocorrelation. The specific equations of the MMK test are similar to those of the original Mann-Kendall test but include some additions and corrections to account for autocorrelation. Detailed information about MMK can be accessed in Berhanu et al.'s study [83].
## 7 Result and Discussion
### RQ6: What Are the Existing Threats in RPL-Based 6 LoWPAN That the Existing Studies Had Addressed?  
To answer this question, we first classify the attacks using the same taxonomy as shown in Figure 1; we provide a brief description of each type of attack. Then, we present a visualization of the attack's statistics in the existing studies, as discussed in Sections 7.2-7.4. The following subsections illustrate the attacks according to their target.
## 4 Classification of Routing Protocols
### QoS-Aware Routing Protocols
#### 4.1.4 Data-Centric Multi-Objectives QoS-Aware Routing (DMQoS)  
DMQoS proposed in [26] is a module-based multi-objective QoS-aware routing protocol that focuses on meeting the QoS requirements for different categories of the generated data. In DMQoS, the data packets are divided into four classes: Ordinary Data Packets (ODs), Reliability-Driven Data Packets (RPs), Delay-Driven Data Packets (DPs) and Critical Data Packets (CPs). The bio-medical sensor nodes send the sensed data towards the coordinator, which they called as body sensor more in raw form. The body sensor more is a central node acting as a cluster head and having less constraints in terms of energy and computation capability as compared to bio-medical sensor nodes. The network architecture of the DMQoS is shown in Figure 5, redrawn from [26]. After the required data processing and aggregation, the body sensor more forwards the data towards the sink in multi-hop fashion using other body sensor motes.  
Figure 4: System architecture of LOCALMOR, (**a**) On-body network, (**b**) In-hospital network.  
The routing architecture of DMQoS [26] consists of five modules: dynamic packet classifier, energy-aware geographic forwarding module, reliability control module, delay control module, and multi-objectives QoS aware queuing module, as shown in Figure 6, redrawn from [26]. The dynamic packet classifier receives the data packets from the neighbor node or the upper layers then classifies them into one of the four aforementioned categories, and forwards them to their respective module on a First-Come-First-Serve (FCFS) basis. The energy-aware geographic forwarding module decides the next hop node with least distance and comparatively high residual energy using multi-objective  
Figure 5: Network architecture for DMQoS [26].  
Figure 6: Routing architecture for DMQoS [26].  
Lexicographic Optimization (LO) discussed in [36]. The reliability control module determines the next hop with highest reliability, while the delay control module finds the next hop having least delay. The QoS aware queuing module is responsible for forwarding the received data packet to one of the four queues based on the assigned priorities, as shown in Figure 6, redrawn from [26]. The use of the multi-objective LO approach to manage the tradeoff between the geographic information and residual energy ensures a homogenous energy consumption rate for all nodes.
## 3 Results and Discussion
Consequently, the existing solutions need improvement, compared to the new technologies that provide more efficient and economically viable approaches. Thus, modernizing agriculture in Tunisia is an economic and social necessity for the country's future. The concept of precision agriculture involves comprehensive diagnostics of soil nature, humidity levels, fertilization, and plant growth, allowing for the adjustment of fertilizer inputs and nutrient absorption in fruit trees, alongside precise control of the water needed, based on the soil's moisture content. These systems represent a focal point for specialists and entrepreneurs in Tunisia, who are concentrating on several research themes, particularly in developing Wireless Sensor Networks to address these challenges. This project embraces this technology and, leveraging the skills of partner teams, aims to provide solutions that enhance the competitiveness of Tunisian agriculture and improve natural resource management. Further research is needed, to ensure their accuracy for different soil types and to evaluate their performance under subsoil moisture and pressure conditions. Our future research will focus on creating a map of soil resources representative of Tunisia's main natural regions, which will inform the development of methodologies adaptable across the territory. This effort will promote the use of buried Wireless Sensor Networks in precision agriculture and facilitate the adoption of best agricultural practices for improved natural resource management and enhanced production in Tunisia. Table 7 below summarizes the probe specifications: