esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater Biomimetic Covert Acoustic Communications Mimicking Multiple Dolphin Whistles
###### Abstract  
This paper presents an underwater biomimetic covert acoustic communication system that achieves high covertness and a high data rate by mimicking dolphin group whistles. The proposed method uses combined time-frequency shift keying modulation with continuous varying carrier frequency modulation, which mitigates the interference between two overlapping multiple whistles while maintaining a high data rate. The data rate and bit error rate (BER) performance of the proposed method were compared with conventional underwater covert communication through an additive white Gaussian noise channel, a modeled underwater channel, and practical ocean experiments. For the covertness test, the similarity of the proposed multiple whistles was compared with the real dolphin group whistles using the mean opinion score test. As a result, the proposed method demonstrated a higher data rate, better BER performance, and large covertness to the real dolphin group whistles.  
underwater communication; biomimetic covert underwater communication; degree of mimic +
Footnote †: journal: Electronics Electronics
## 4 The Proposed Protocol
### The Protocol Design
#### 4.2.1 Creation of the Smart Contract  
Figure 3 shows that the manufacturer releases a new firmware update. It stores the hash of update \(U=(U_{id},f_{v},M_{id},dtype,purl)\) that is signed with the manufacturer's secret key \((SK_{m})\) to the smart contracts. \(U_{id}\) is the update identifier. It is simply the hash of the firmware update's binary file. The smart contract will verify this request. If it is valid, the update \(U\) will be stored to the smart contract \((M_{sc})\), and it will be published to the blockchain network. Before the manufacturer releases a new firmware update, the trusted party (for example, by certified cybersecurity companies) must already check the new firmware update. They will check the firmware update as to whether it is free of bugs and malicious attacks. In our protocol, since the IoT gateway is a blockchain node, it will receive a notification when the manufacturer stores the metadata to the smart contract.
## 5 The Concept of IoV
### Comparison of IoV and VANETs  
IoV emerged as a result of the integration of the IoT and VANETs. As such, it is a more advanced version of VANETs. The primary reason for the development of IoV was to strengthen and enhance VANETs' capabilities. While the two technologies share some similarities in terms of objectives, they also have differences that are worth discussing [65].  
* Goal: Both VANETs and IoV aim to enhance traffic safety and efficiency. However, while VANETs focus more on cost and pollutant emission efficiency, IoV focuses on commercial infotainment. Infotainment is one of the most crucial components of IoV because it helps passengers access services such as online video streaming and file downloading.
* Network specification: IoV has a diverse network framework. The network is used for collaboration and entails communication types such as 4G, Wi-Fi, WAVE, and satellite [81].
* Communication types: IoV has five types of communication, with each type relying on specific wireless communication technology. The five types of communication are vehicle to sensors (V2S), vehicle to road side units (V2R), possible vehicle to vehicle (V2V), vehicle to personal devices (V2P), and vehicle to the infrastructure of cellular networks (V2I) [82]. Figure 3 illustrates the types of communication used in IoV.
* Processing competence: IoV is capable of handling large packets of global data. The system incorporates intelligent computing platforms such as fog computing, cloud computing, and edge computing, which enable it to process large amounts of data at a fast speed [83].
* Compatibility: IoV is easy to use since all the devices used are compatible with the network, thus making it easier for information to be disseminated among the nodes in the most efficient way possible. As such, an interactive environment is created, making it possible to detect hazards early.
* Range of usage: IoV can be used globally; as such, it is an effective technology that can sustain a wide range of applications with different communication and computing capabilities [14; 60].
* Network connectivity: Communication is a critical component of IoV networks; for this reason, IoV operates in an environment with the best communication. Moreover, it can easily switch to a stronger and more efficient network in case the current one fails.
* Internet facilities: IoV environments enable vehicles to be connected to the internet at all times. The reliability of IoT networks depends on the speed of the internet and a high bandwidth.
* Cloud computing: Massive quantities of data are processed on a daily basis in an IoV environment. As such, cloud computing is often regarded as the most effective approach for managing vast quantities of data. Cloud computing makes it easier for information to be collected, stored, and analyzed [84].
# Space Charge Accumulation and Decay in Dielectric Materials with Dual Discrete Traps
## 5 Conclusions  
The space charge dynamics in LDPE with two discrete trapping centers were studied analytically and numerically. We simplified the charge trapping/de-trapping equations and obtained the analytical solutions of trapped charge densities, quasi-free charge density, and effective carrier mobility. Then, we utilized the BCT model consisting of charge injection from electrodes, charge migration in shallow traps, charge trapping, de-trapping, and recombination processes to simulate the distributions of space charges and electric fields in the material. The charge dynamic equations, charge advection equation, and Poisson's equation in the BCT model were numerically solved by the NSFD, finite difference WENO, and BEM methods, respectively. The outputs of the BCT model showed the spatial distributions of free charge densities, trapped charge densities, and net charge densities. Charge densities at the same position in the sample as a function of time were achieved by the BCT simulations. The simulation results showed that the DC poling duration could affect the energy distribution of accumulated space charges. The charges in the relative shallower trapping centers would transfer to the relative deeper trapping centers for long poling durations. It was found that coupling dynamic processes would increase the time needed for the relative deeper trap centers to reach steady states. Therefore, experimental results for long poling duration are required to analyze the charge trapping and de-trapping properties of polymeric dielectric materials.  
Conceptualization, D.M.; Resources, Z.X. and Q.W.; Funding acquisition, D.M.; Writing--Original draft, D.M., H.C. and Y.H.; Writing--review & editing, Z.X., C.Z., D.M. and H.C.; Formal analysis, Z.X., C.Z.; Supervision, Q.W. and D.M.  
This work was supported by State Key Laboratory of Advanced Power Transmission Technology (Grant No. GEIRI-SKL-2018-010), the National Basic Research Program of China (Grant No. 2015 CB251003), the National Natural Science Foundation of China (Grant Nos. U1830131 and 51507124), and State Key Laboratory of Intense Pulsed Radiation Simulation and Effect (Northwest Institute of Nuclear Technology) (Grant No. SKIPR1709).  
The authors declare no conflict of interest.
# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
## Abbreviations  
The following abbreviations are used in this manuscript:  
LEO Low Earth Orbit  
SFC Service function chain  
VNF Virtual network function
# Effects of Glyoxylic Acid on Metabolism and Ripening of 'Rocha' Pears Treated with 1-MCP
## 5 Conclusions  
So far, no studies have investigated the effects of GLA on the postharvest ripening of 'Rocha' pears under 1-MCP. This study confirms that GLA influences metabolic processes in 'Rocha' pears under 1-MCP treatment, but its effects are not directly linked to ethylene-driven ripening. Instead, GLA primarily influenced respiration rates and the biosynthesis of stress-associated VOCs, indicating an impact on metabolic pathways related to energy metabolism and redox homeostasis rather than ethylene-mediated ripening mechanismsthat need further exploration. An interesting point worth further investigation in this experiment is the unexpected ripening observed in the CTRL group, which may justify the low impact of GLA on pears. Future research should focus on analyzing the genetic expression of ethylene receptors to better understand how receptor renewal and turnover influence the recovery of ethylene sensitivity and the overall ripening process in 1-MCP-treated pears. Additionally, these findings highlight the importance of the fruit's physiological state at the time of treatment application, suggesting that GLA's impact on ethylene metabolism may depend largely on the residual effects of 1-MCP.  
Future research should focus on the optimal timing of GLA application during storage, particularly in relation to the waning effect of 1-MCP at the time of treatment. Additionally, investigating how GLA modulates amino acid metabolism and other biochemical pathways will be crucial to optimizing its role in postharvest management strategies. Understanding these mechanisms could provide valuable insights into the modulation of fruit metabolism, particularly in climacter species, where balancing ripening and shelf-life extension is a key postharvest challenge.  
Conceptualization C.D., A.F. and M.P.; methodology C.D.; validation, A.F., M.W.V. and M.P.; formal analysis, C.S., A.F., M.W.V. and M.P.V; investigation, C.D.; writing--original draft preparation, C.D.; writing--review and editing, C.S., M.W.V., A.F. and M.P.; supervision, M.W.V., A.F. and M.P. All authors have read and agreed to the published version of the manuscript.  
This research was co-supported by the European Fund for Regional Development (FEDER) through the Internationalization and Competitiveness Operational Program (POCI), within the project RE-EAT ROCHA PEAR (POCI-01-0247-FEDER-040016). We would also like to thank the scientific collaboration under the FCT project UIDB/50016/2020 and FCT individual Ph.D. grant (SFRH/BD/143560/2019).  
The raw data that support the conclusions of this article will be made available upon request to the authors.  
We thank the Rocha Center Institute for supplying the pears.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results and Discussion
### Calculation Results  
In the case study, six regions from testing samples are chosen randomly as examples for exhibition, which are shown in Figure 11. Their region IDs are 1100, 1302, 7015, 8125, 12,610 and 14,072, respectively. From the results, the bounds of reasonable intervals can adjust adaptively according to multiple input factors, especially in Region No. 1100 and Region No. 8125. Some outliers that are far away from benchmarks are efficiently picked out, although those outliers may stay between \(-1\%\) and \(5\%\). Hence, the results of reasonable intervals are better than a fixed interval between \(-1\%\) and \(5\%\). Besides, the benchmark values show less fluctuation the actual line loss rate values, indicating a high reliability to estimate daily line loss rates. Rather than a mean value or a median value calculated from the original dataset, benchmark values are able to adaptively reflect the daily operating conditions in transformer regions according to the changes of relevant factors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyper-Parameters** & **Searching Space** & **Result** \\ \hline \(\sigma\) (standard error of noise in DAE) & [0.00:0.05:0.50] & 0.05 \\ \(\delta\) (coefficient in Huber loss) & [0.00:0.10:0.50] & 0.10 \\ \(\lambda\) (penalty coefficient of L2) & \(1\times 10^{[-4:1:0]}\) & \(1\times 10^{-3}\) \\ Number of nodes in a sub-path & [16, 32, 64, 128] & 64 \\ Number of sub-paths & [2:1:5] & 3 \\ Training optimizer & \{[PERSON], [PERSON]\} & Adam \\ Number of epochs & [50:50:200] & 100 \\ Batch size & [64, 128, 256, 512, 1024, 2048] & 1024 \\ Learning rate & \(1\times 10^{[-5:1:0]}\) & \(1\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The searching spaces of selected hyper-parameters in RNN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
\begin{tabular}{c} Node number: 1 \\ Activation: ReLU \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: The architecture and hyper-parameters of the proposed robust neural network (RNN).  
Based on the proposed RNN, the pass percentage results of line loss rates can be analyzed, which are shown in Figure 12. For the data point analysis of line loss rates, the number of outliers are more than that in Figure 3, as the proposed method is able to precisely identify those outliers that are greatly different from the benchmark values. Furthermore, although the percentages of missing values and outliers are not relatively big in all data points, which are 6.72% and 13.06%, respectively, the regions that possess no missing and abnormal values within a month only occupy 19.84% of the whole dataset in the study, indicating a low reliability of the current acquisition equipment.
# Positioning Methods and the Use of Location and Activity Data in Forests
## 2 Types of Positioning Technologies
### Video Object Detection and Relative Positioning Methods  
Similar to the map-matching methods described earlier in the context of SLAM, object detection using video imagery can also be thought of as a positioning method for moving objects or other resources. The sequential video frames for a known landscape field of view provide a reference map, and manual (visual) observation or automated detection of people, wildlife, or other phenomena within that scene can be used to estimate location. A variety of algorithms and techniques exist for identifying and delineating the background scene from one or more foreground objects based on shapes, color, contrast, and other image features [152; 153; 154; 155]. In forest management, video object detection methods have received the majority of attention in the context of detecting wildland fire [156; 157; 158] and smoke [159; 160; 161]. As unmanned aerial vehicles (UAVs) are used more widely in fire management, fire and smoke detection from UAV-mounted video is becoming more prevalent (e.g., [PERSON] et al. [162]). Positioning of logging workers based on field safety vest characteristics detected by cameras mounted on the skyline carriage was proposed by [PERSON] et al. to increase situational awareness for equipment operators [163].  
In addition to the various first-order positioning methods described thus far, it is important to note the existence and growing importance of technologies that use either sensors, video imagery, or other methods to position themselves _relative_ to other objects, whether stationary or in motion. This concept is important in automation and also in recreation applications. Co-location relative to other mobile devices is distinct from independently positioning multiple phones or other mobile devices [164]. A simple example of relative positioning is the ability of follower drones to maintain a moving position at a constant distance and orientation relative to a person, animal, or piece of equipment being monitored (Figure 10) [165; 166]. Visual relative localization is also used to configure swarms of UAVs flying in formation or in other coordinated activities [167]. Acoustic signaling is another form of relative localization that has also been used to collocate UAV swarms based on follower positions relative to a leader or peer [168]. This approach may also have applications in recreational use of UAVs and equipment automation in forests.  
Figure 9: Quick response (QR) codes printed at the jobsite with log load information are scanned again at multiple waypoints, such as truck weight scales on a state highway and the sawmill. Chain of custody information, like the weight of the load, can be monitored, and, insofar as the time and place of the truck is recorded on a map multiple times, this method serves as a form of positioning.
## 2 Sensing Method
### Capacitive Sensing and Inductive Sensing
#### 2.3.1 Applied Technologies
Beyond determining the configuration of the inductance coil, researchers are delving into multi-coil sensing systems. [PERSON] employs a three--coil coupling system, composed of one implanted coil and two external coils [62]. The external coils are classified as a transmitting coil and a relay coil. The three--coil inductive link technology has the potential to optimize the readout system by leveraging the geometric characteristics of each coil and the distance between the transmitting coil and the relay coil. In this topological configuration, an increase in the mutual inductance between the transmitting coil and the relay coil is anticipated. This, in turn, will result in an augmentation of the mutual inductance between the readout coil and the implanted sensor. The enhanced mutual inductance can elevate the efficiency of the readout system in detecting the sensor. Experimental results demonstrate that when the distance between sensors in the readout system is zero, the relay sensor can function as a signal amplifier, thereby optimizing the detection of the sensor by the transmitting sensor. Among the devices previously mentioned, several incorporate mutual inductance sensors as their fundamental components. A mutual inductance system necessitates a minimum of two distinct coils. To mitigate system complexity, [PERSON] employed an origami structure to fabricate a self-inductance sensor [63]. The key advantage of this sensor lies in the fact that it can fulfill its function with only a single coil, thereby reducing the complexity of the overall system. The substrate of this device is composed of ordinary paper material, on the surface of which a planar inductance coil is fabricated. When the paper undergoes bending, the deformation of the planar spiral coil in three-dimensional space can be exploited to measure angles (Figure 2c). The structural features of this sensor render it extremely suitable for measuring the flexion, extension, and rotation of human joints. Furthermore, the article conducted comprehensive mathematical calculations and electromagnetic simulations to identify the optimal structure of the inductance coil under the prerequisite of high sensitivity. These efforts provide a crucial design direction for future folded coils.
## 1 Introduction  
Some authors have found that one of the most common facial lesions are mandibular fractures due to traffic accidents, violence, falls or sports [1, 2, 3]. Currently, advances in the ability to treat mandibular fractures have been gained and, typically, titanium screws are used to hold a plate which fixes the fracture. Titanium is chosen because of its high stiffness and strength. However, this property makes the implant to support most of the mechanical load and cause a loss of bone mass in the vicinity of it, this effect is called stress shielding [4, 5]. Other problems reported with titanium implants include palpability, pain and the need for subsequent removal. For these reasons, absorbable plate and screw systems are commonly used for maxillofacial surgery, as they prevent the stress shielding, reduce the risk of infection and avoid the need of secondary surgery for removing implants. However, there are also problems related with the use of absorbable systems such as incomplete screw insertion and screw breakage [6, 7, 8, 9]. Therefore, the correct application of the insertion torque prevents the complication of screw breakage [10]. While this insertion torque is applied, a tensile stress named pretension is generated in the screw body and, once the implant is in place, this is maintained. Thus, it is important to define the optimal pretension in plate and screw absorbable systems, which depends on the geometry of the screw, the contact angle, friction coefficient and mechanical properties of the parts [11].  
Most studies related to the optimization of screw design focused only on one or two parameters and do not take full advantage of finite element method (FEM) software. On the other hand, the total number of simulations to investigate all possible combinations of parameters can be very large, hence, the use of the design of experiment (DOE) layout is feasible to have an adequate number of them, in order to reduce experimental efforts [12].  
Moreover, artificial neural networks (ANNs) are now commonly used in many areas, such as engineering and medical science, as they are helpful models for, among other things, prediction and optimization. The ANNs function similar to a human brain, imitating the adjustments to the synaptic relationship between and among neurons in the learning process. An advantage of ANNs is the easy to use and accurate models obtained [13].  
Consequently, the aim of this paper is to find the optimal screw pretension (SP) for a mini plate and screw absorbable system for fracture fixation when desired value for maximum von Mises strain (MVMS) in absorbable screw is set and its geometric parameters are known. To achieve this, the methodology proposed in [14] is applied. The inverse artificial neural network (ANNi) is a variant of the ANN that has been implemented in several processes to determine the optimal parameters successfully [15]. The inputs of the data base used to train the ANN were constructed based on a DOE, whereas the output, MVMS, was calculated by the means of FEM.