esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Rock Physics Workflow
#### 3.1.1 Geophysical Well-Log Analyses  
The first step is to edit the logs and remove erroneous data. In this instance, a trend is established by plotting each log against depth. Data values that significantly deviate from this trend are treated as outliers and are edited accordingly. Next, qualitative and quantitative methods are used to identify hydrocarbon-bearing reservoirs (Figure 3). In the qualitative analysis, the well logs are scanned for indications of hydrocarbon reservoirs, such as low gamma-ray responses and high resistivity. The quantitative approach also involves computing parameters like lithology volume, water saturation, and effective porosity. Using rock physics, reservoir zones were identified and analyzed. Volume concentrations are determined from a statistical solution utilizing the neutron, density, photoelectric factor, compressional, and shear velocities. The mass-balance equation is used to compute the porosity from the density. [PERSON]'s equations are used to compute the water saturation (\(S_{w}\)). Sands are characterized as having \(\text{V}_{quartz}\) greater than 60%, oil sands as having \(\text{V}_{quartz}\) greater than 60% and \(S_{w}\) less than 60%, and shale as having \(\text{V}_{clay}\) greater than 60%. Overall, the quartz volume appears consistent in all of the wells, with an average of 70%.
# Underwater-Acoustic-OFDM Channel Estimation Based on Deep Learning and Data Augmentation
## 4 Experiments and Evaluations  
In this section, we commence by presenting the parameter configurations for the UWA channel and the UWA-OFDM system. We conducted simulation experiments, to assess the authenticity of the channel estimates, the MSE, and the BER performance of our proposed algorithm within the OFDM system.  
Figure 7: The-channel-attention-denoising module.
## 4 Methodology
### Knowledge Distillation Module  
Figure 3 shows the knowledge distillation module framework. The teacher model is a complex and highly accurate GNN, from which knowledge is distilled using distillation techniques and transferred to the student model MLP; in particular, the soft label outputs from the teacher GNN serve as the supervisory signals to train the student MLP.  
Given a pre-trained teacher GNN, for any label node \(v\in V^{L}\), the real label value is \(y_{v}\), and the output of node v from the teacher GNN is a soft label \(z_{v}=Softmax\Big{(}\frac{d^{2}}{\tau}\Big{)}\). The purpose of knowledge distillation is to use the output of the teacher GNN \(z_{v}\) and node true label \(y_{v}\) to train a lightweight student model MLP. The objective function is formally defined as follows:  
\[L=\sum_{v\in V^{L}}L_{SL}(\hat{y}_{v},y_{v})+\lambda\sum_{v\in V}L_{KD}(\hat{y }_{v},z_{v}) \tag{5}\]  
Here, \(L_{SL}\) is the cross-entropy loss between the student model prediction \(\hat{y}_{v}=Softmax\Big{(}\frac{d^{2}}{\tau}\Big{)}\) and the real label \(y_{v}\) (i.e., the supervision loss), \(L_{KD}\) is the KL divergence loss between the student model prediction output \(\hat{y}_{v}\) and the output \(z_{v}\) of the teacher model GNN (i.e., the teacher model to the distillation loss of the student model with respect to the teacher model), and \(\lambda\) is a factor that balances the supervision loss and the distillation loss. The purpose of the supervision loss here is to prevent the teacher model from passing incorrect knowledge to the student model.  
During the knowledge distillation process, the knowledge mastered by the student model grows progressively, so maintaining a constant distillation temperature for knowledge distillation will result in suboptimal distillation results. Similar to how teachers teach students knowledge, they tend to transition from simple knowledge to complex knowledge, known as the process of progressive learning. Inspired by this learning process, we simulated the difficulty of the knowledge distillation process by varying the distillation temperature. Notably, changing the distillation temperature does not introduce additional computational costs into the distillation process.  
In order to ensure that the distillation temperature is non-negative and remains within a certain range, the distillation temperature \(\tau\) is determined by the output of the teacher model and the student, as shown in Figure 4. It is scaled using the following formula:  
\[\tau=\tau_{init}+\tau_{r}\left(\sigma\Big{(}T_{pred}\Big{)}\right) \tag{6}\]  
Here, \(\tau_{init}\) is the initial temperature, \(\tau(r)\) represents the range of \(\tau\), \(\sigma(\cdot)\) is the activation function, and \(T_{pred}\) is the predicted value (here, \(\tau_{r}\) is set from 1 to 15).
# Preparation and Physicochemical Properties of Tannin-Imobilized Membrane Adsorbent
## 3 Results and Discussion
As shown in Figure S4a,b, the M-TANs were immersed in pure water at 20 \({}^{\circ}\)C and 50 \({}^{\circ}\)C and shaken for a week to examine their stability at different temperatures. PA-BWT, PVDF-BWT, and PA-AA-BWT did not change significantly, while the color of CELL-BWT was slightly lighter. This indicates that the PA-BWT, PVDF-BWT, and PA-AA-BWT prepared by the covalent cross-linking method have a high stability, and the tannins will not fall off even under long-term oscillating soaking, while the tannins in CELL-BWT, combined by hydrogen bonding, can easily fall off the membrane under long-term shaking and soaking. The results of further measuring their water flux are shown in Figure 6a. The water flux of PA-BWT, PVDF-BWT, and PA-AA-BWT did not change significantly, but the water flux of CELL-BWT increased slightly. This is because the shedding of tannins on the membrane increases the hydrophilicity and pore size of CELL, thereby increasing the water flux. Corresponding to the degree of tannin grafting, the adsorption capacity of PA-BWT, PVDF-BWT, and PA-AA-BWT remained basically unchanged, while the adsorption capacity of CELL-BWT decreased significantly (Figure 6b), which was attributed to the shedding of tannin. The M-TANs were immersed in pure water with pH = 2 and pH = 9 at room temperature and shaken for a week to investigate their stability at different pH values. As shown in Figure S4c,d, the morphology of the M-TANs after oscillating immersion in different pH solutions did not change, and there was no damage or corrosion phenomenon, indicating that they could still maintain their original structure under alkaline/acidic conditions. Figure 6c,d shows the pure water flux and adsorption capacity of UO\({}_{2}\)\({}^{2+}\) of M-TANs before and after oscillating immersion in different pH solutions. PA-BWT, PVDF-BWT, and PA-AA-BWT had no change in water flux and UO\({}_{2}\)\({}^{2+}\)adsorption capacity after being immersed in different pH solutions for a long time, which indicates that they have good stability and anti-pollution performance. However, for Cell-BWT, the adsorption capacity of UO\({}_{2}\)\({}^{2+}\) decreased greatly after a long period of oscillating soaking, indicating that the tannin on the surface of CELL-BWT fell off seriously. This indicates that M-TANs prepared by the covalent cross-linking method have better stability and firm tannin immobilization. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Temperature (K)**} & \multicolumn{3}{c}{**Thermodynamic Parameter**} \\ \cline{2-4}  & \(\Delta\)G\({}^{\circ}\) & \(\Delta\)S\({}^{\circ}\) & \(\Delta\)H\({}^{\circ}\) \\  & **(kJ/mol)** & **(kJ/(mol K))** & **(kJ/mol)** \\ \hline
303 & \(-\)9.20 & 0.163 & \\
313 & \(-\)7.33 & 0.152 & \\
323 & \(-\)6.08 & 0.143 & \\
333 & \(-\)6.23 & 0.139 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Langmuir parameters and thermodynamic parameters.
## 4 Discussion
### Analysis of Parameter Selection
\begin{table}
\begin{tabular}{c c c c c} \hline
**SCOPE** & **Available Edge Pixels** & **Edge Pixels** & **Standard Deviation** & **Entropy** \\ \hline
100 & 6763 & 93,328 & 26.7462 & 6.4968 \\
110 & 6727 & 87,341 & 26.4216 & 6.4758 \\
120 & 6658 & 79,495 & 25.6472 & 6.4137 \\
130 & 6562 & 70,542 & 24.5879 & 6.3843 \\
140 & 6430 & 61,587 & 23.0124 & 6.2648 \\
150 & 6217 & 51,569 & 21.6932 & 6.1786 \\
160 & 5794 & 44,325 & 21.0219 & 6.1125 \\
170 & 4623 & 38,224 & 20.6348 & 6.0644 \\
180 & 3976 & 34,156 & 20.2591 & 6.0031 \\
190 & 3477 & 33,269 & 20.1446 & 5.9412 \\
200 & 3025 & 32,596 & 20.0536 & 5.8903 \\ \hline \end{tabular}
\end{table} 
Table 6: The values of standard deviation, entropy, available edge pixels and edge pixels corresponding to the values of different parameter SCOPE. Figure 11: (**a**) The number of available edge pixels corresponding to the values of different parameter SCOPE. (**b**) The number of edge pixels corresponding to the values of different parameter SCOPE. (**c**) The values of standard deviation corresponding to the values of different parameter SCOPE. (**d**) The values of entropy corresponding to the values of different parameter SCOPE. According to the comparison of these evaluation data, we should make sure that the number of available edge pixels are enough, and the standard deviation and the entropy should be as small as possible; therefore, the value of MD equal to 10 is selected. In order to determine the parameters C and S, we choose Acc as an indicator to evaluate the quality of parameters. Figure 7 shows the effect of the selection of different C, S values on the final extraction accuracy. From Figure 7a, it can be seen that, when parameter C = 140, the value of _Acc_ reaches the maximum value, and as the value of C continues to increase, the _Acc_ decreases slightly. From Figure 7b, we can see that the value of _Acc_ decreases as the parameter S increases. However, when the value of S is too small, the computational complexity will increase. Therefore, the value of S in this experiment is chosen to be 8 by considering things comprehensively.
## 3 Distributed Clustering Techniques  
### The Wireless Sensor Network Architecture  
The proposed WSN consists of the three basic types of wireless sensor nodes shown in Figure 1: (1) Sink Nodes; (2) Anchor Nodes; and (3) Mobile Nodes. Sink Nodes are equipped with a powerful processing unit, an RF transceiver unit, a high capacity memory unit, and a long-lasting power supply unit to support two key agent types: (1) a Mediator Agent (MA) that is responsible for cluster formation and management, as well as inter cluster communication; and (2) Locator Agents (LA) that are responsible for Mobile Node tracking. For our proposed approach, a unique LA is assigned to each Mobile Node tracked by the cluster.  
Anchor Nodes are responsible for sensing the distance to a particular Mobile Node and transmitting the distance estimations to a Sink Node. It is generally assumed that the location of these nodes with respect to a global frame of reference is either known _a priori_ or can be acquired at any point in time. The processing capabilities of these nodes are much less than those of Sink Nodes since only a single agent, the Anchor Node Agent (ANA), is required to interface these nodes with the agent system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Network Type** & **Localization** & \begin{tabular}{c} **Clustering** \\ **Technique** \\ \end{tabular} & **Notes** \\ \hline [PERSON] _et al._[10] & heterogeneous & range-free & \begin{tabular}{c} pre-defined \\ (static) \\ \end{tabular} & \begin{tabular}{c} Use of software agents \\ for clustering \\ \end{tabular} \\ \hline [PERSON] and & homogeneous & range-based & \begin{tabular}{c} pre-defined \\ (static) \\ \end{tabular} & Energy efficiency \\ \hline [PERSON] _et al._[12] & homogeneous & range-based & \begin{tabular}{c} pre-defined \\ (dynamic) \\ \end{tabular} & Energy efficiency \\ \hline [PERSON] _et al._[13] & homogeneous & range-based & \begin{tabular}{c} pre-defined \\ (static) \\ \end{tabular} & Security \\ \hline [PERSON] _et al._[15] & heterogeneous & range-free & \begin{tabular}{c} pre-defined \\ (dynamic) \\ \end{tabular} & Voronoi cells \\ \hline [PERSON] _et al._[16] & heterogeneous & range-based & \begin{tabular}{c} pre-defined \\ (static) \\ \end{tabular} & Voronoi cells \\ \hline [PERSON] and & heterogeneous & range-free & \
\begin{tabular}{c} pre-defined \\ (dynamic) \\ \end{tabular} & Voronoi cells \\ \hline [PERSON] _et al._[18] & heterogeneous & range-based & _ad hoc_ & map-based \\ \hline [PERSON] [19] & homogeneous & range-based & _ad hoc_ &
\begin{tabular}{c} Weight-based criterion \\ to determine CH \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recent work on wireless sensor node clustering strategies.  
Finally, the third type of wireless sensor node, the Mobile Node, is very similar in capabilities to Anchor Nodes, but by definition, can move from one location to another. Like the Anchor Node, a single interface agent, the Mobile Node Agent (MNA) in this case, resides on the computing platform. Additional details on the design and implementation of the agent architecture are provided in [8] and [20].
# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 3 Downlink Scheduling in LTE
### Exponential/Proportional Fairness (EXP/PF)  
The EXP/PF technique has been formulated to support multimedia services for systems using multiplexed time. It can reduce package transmission times by combining the PF algorithm's features and the exponential function. The PF algorithm [22] is used to promote the infinite buffer flow, whereas the EXP/PF can be used to serve real-time traffic, as in Equation (3).  
\[M_{i,K}^{EXP/PF}=\exp\left(\frac{\alpha_{i}D_{HOL,i}-x}{1+\sqrt{x}}\right) \cdot\frac{d_{k}^{i}(t)}{R^{i}(t-1)} \tag{3}\]  
where  
\[x=\frac{1}{N_{rt}}\ \sum_{i=1}^{N_{rt}}\alpha_{i}D_{HOL,i}\]
# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
## 1 Introduction  
### Motivation and Contributions  
As the Food and Agriculture Organization (FAO) of the United Nations projected, the global population will reach nine billion by 2050 [1]. A report from the U.S. Geological Survey underscores agriculture's substantial consumption of ground and surface water in the US [2]. In 2015, irrigation alone accounted for a staggering 42% of the nation's total freshwater withdrawals. To accommodate the surging 60% increase in food demand, securing supplementary water resources becomes necessary in the agricultural application domain [1]. However, the wide use of chemical fertilizers has caused excessive nutrient loading, soil degradation, and surface and groundwater contamination. The impacts of climate change (e.g., floods, droughs, and heat waves) also disrupted water supplies. Over the period from 2000 to 2019, available land per capita dwindled by 22% [1].  
The Internet of Things (IoT) has been widely deployed into numerous smart systems, including irrigation systems, to optimize water utilization. The benefits of adopting IoT irrigation systems include but are not limited to the following. (i) Cost Efficiency: most sensors used in smart irrigation systems are relatively small and cheap. (ii) Mobility and Flexibility: The integration of interconnected sensors and devices enables remote accessand data retrieval, thereby enhancing the irrigation system's mobility and adaptability. (iii) Rapid Responsiveness: The majority of data consists of numerical values within the context of irrigation. The rapid transmission of these data across networks facilitates timely responses to inquiries.  
An assessment of seventeen survey articles [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19] within the smart agriculture system revealed that although smart irrigation constitutes a subset, it remains under-represented in the literature. Most publications focus on technological aspects within the IoT system, often sidestepping user-centric evaluations. In contrast, our study aims to formulate a generic problem space and evaluate the most recent research works related to IoT irrigation systems, highlighting the contributions and drawbacks of existing research works while pointing out the challenges and future trends.  
We make three key contributions: (i) a comprehensive and systematic review of irrigation technology, history, and irrigation methods; (ii) a generic problem space to review IoT technologies in irrigation systems in three aspects: _\"IoT layers dimension\"_, _\"Environment factors dimension\"_ and _\"Cost efficiency dimension\"_, since a well-designed system should incorporate cutting-edge technologies, adapt to external environmental factors, and remain cost-effective; and (iii) proposed future study trends on how to develop an advanced and comprehensive irrigation system.
# Preliminary Propulsion and Power System Design of a Tandem-Wing Long-Range eVTOL Aircraft
## 5 Results and Discussion of the Power System Design
### Powertrain Sizing  
The required energy for a full flight of the Wigeon is 301 kWh, and the maximum power is 1.8 MW [3]. Using these values and Equations (14) and (15), it was found that the battery mass required for energy storage is 886 kg, and that the battery mass required for maximum power is 409 kg. Thus, for the Wigeon the batteries design is energy driven and not power driven, achieving a total battery mass of 886 kg. With Equation (16), the volume is computed at 0.443 m\({}^{3}\).  
Based on values from Calnetix [21] and magniX [17], a combined specific power for the motor controllers and the electric motors was found using Equation (17), this being 6800 W/kg. To prevent an overestimation of the power density, which would have a major effect on the MTOM, the calculations were performed with a lower power density: 5000 W/kg. Using this power density resulted in a total mass for the combined motor controllers and electric motors of 503 kg.
# Stochastic Network Calculus-Aided Delay Analysis of Wireless-Power Line Mixed Networks
## 2 Network Model  
We consider a wireless-PLC mixed cooperative communication system in this work. We assume that data are sent by the source to the relay first through a wireless channel. The relay is equipped with a hybrid capability for interfacing a wireless link with the PLC link. Then, data are transmitted from the relay to the destination over a PLC link. The wireless link between the source and relay is characterized by Rayleigh fading and additive white Gauss noise (AWGN). The instantaneous SNR over the wireless link is  
\[\gamma_{w}(t)=\frac{P_{w}l_{w}(t)}{\sigma_{w}^{2}}, \tag{1}\]where \(h_{w}\) incorporates the Rayleigh fading of the wireless link, which is given by an exponential random variable with a unit mean for mathematical tractability. \(P_{w}\) is the transmit power, and \(\sigma_{w}^{2}\) is the noise power of the wireless link.  
The PLC link is modeled by the log-normal distributed channel gain and Bernoulli-Gaussian noise. Similarly, the instantaneous SNR of the PLC link can be written as  
\[\gamma_{p}(t)=\frac{P_{p}h_{p}(t)}{n}, \tag{2}\]  
where \(h_{p}\) is the PLC channel gain whose amplitude is distributed as  
\[f(a_{h_{p}})=\frac{1}{a_{h_{p}}\sqrt{2\pi\sigma_{h_{p}}^{2}}}\exp\left(-\frac{ (\ln a_{h_{p}}-\mu_{h_{p}})^{2}}{2\sigma_{h_{p}}^{2}}\right), \tag{3}\]  
with \(\ln a_{h_{p}}\) being a Gaussian random variable with mean \(\mu_{h_{p}}\) and variance \(\sigma_{h_{p}}^{2}\). For the Bernoulli-Gaussian noise, the probability distribution function (pdf) is written as  
\[f(n)=(1-\rho)\mathcal{CN}(0,\sigma_{g}^{2})+\rho\mathcal{CN}(0,\sigma_{g}^{2} +\sigma_{i}^{2}). \tag{4}\]  
Herein, \(\mathcal{CN}(0,\sigma^{2})\) is the complex Gaussian distribution with mean 0 and variance \(\sigma^{2}\). \(\rho\) is the probability of the impulsive component of the Bernoulli-Gaussian noise. The background noise power of the PLC link is \(\sigma_{g}^{2}\). When considering both the background and impulsive noise, the total noise power is given as \(\sigma_{i}^{2}=\sigma_{g}^{2}K\), where \(K=\sigma_{i}^{2}/\sigma_{g}^{2}\) is the impulsive noise index.  
With the above system and channel model, we proceed to analyze the delay bound for our wireless-PLC mixed network via SNC in the SNR domain.
## 5 Proposed Adaptive Beaconing System  
We present the design of an intelligent high-awareness and channel-efficient adaptive beaconing protocol (HACEAB). Figure 3 shows beaconing in WAVE, while the HACEAB flow chart is shown in Figure 4.  
In our protocol, we assume that each vehicle is equipped with a Global Positioning System (GPS) and a half-duplex transceiver, enabling it to have access to its position information. Furthermore, it is assumed that each vehicle appends its identification, speed,  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Scenario 1** & **Scenario 2** & **Scenario 1** & **Scenario 2** \\ \hline Set 1 & ✓ & & & \\ (SINR, number of neighbors, mobility) & & & & \\ \hline Set 2 & & ✓ & & \\ (Mobility, CBT, PDR) & & & & \\ \hline Set 3 & & & ✓ & ✓ \\ (SINR, number of neighbors, mobility, CBT, PDR) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of experimental findings.  
Figure 4: HACEAB flow chart.  
Figure 3: Beaconing in WAVE.  
and coordinates to the transmitted beacon packet (Figure 1). The receiving vehicle utilizes these data to compute the input values.  
First, HACEAB estimates the network density using an adaptive threshold function that depends on the beacon frequency to determine whether the network is dense or not dense. If the network is dense, it uses the dense conditions' fuzzy rules (we explain it in detail later on). If the network is not dense, based on the exchanged position information, it uses the quadrat method to determine the type of distribution, whether uniform or clustered. Based on the determined environment, HACEAB uses the appropriate fuzzy rules (i.e., in case of a nondense network, if based on the quadrat method, nodes are clustered (scenario 1), and HACEAB uses the fuzzy rules of Set 1. Otherwise, it uses the fuzzy rules of Set 2. After that, HACEAB calculates the next beacon rate utilizing the rate control function.  
Now, we explain how the flow-chart activities work in details. The first step is to receive beacons from other vehicles. Then, the protocol determines the local density (number of direct neighbors). After that, the protocol calculates the density threshold and compares it with the local density as is explained in the following subsection.
## 7 Applications in SDN-based VANETs
### On Demand Reconnaissance Service  
On-request reconnaissance is another help which is made accessible for the approved customer of a SDN-based VANET. This administration can be utilized in a crisis or examination situation. To utilize this administration, an approved hub (e.g., a squad car) must send an observation demand. The controller benefits from this solicitation. In answer to this solicitation, the controller implants the stream rules for the observation information to arrive at the requesters. When there is more than one solicitation for the indistinguishable data, for example, numerous requester hubs demand for video observation feed, the controller makes passages of rules in the stream table with the goal that a similar duplicate is sent to numerous requesters. This is likewise appropriate for infotainment administrations like video spilling, video conferencing, and media sharing [135,136].