esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Experimental Analysis of CC over DC Without Obstacle  
The following experimental analysis is based on the performance evaluation of DC with CC. In this setup, no obstacle is placed in either direct or relay path.  
Figure 7 shows the experimental results of DC and CC without any obstacle. The performance of CC dominates the DC. From Table 3, the average channel capacity gain of CC over DC is 14.44% for BPSK, 14.59% for QPSK, and 14.75% for 8 PSK, respectively.  
\begin{table}
\begin{tabular}{c c} \hline Transmit Antenna Gain & 0–30 dB \\ Operating Frequency & 915 MHz \\ Bandwidth & 400 kHz \\ Data type & Text message \\ Message & 128 bits \\ Guards & 30 bits \\ Synchronization & 20 bits \\ Packets & 46 \\ Modulation & BPSK, QPSK, 8 PSK \\ Channel & Real time \\ Relaying mode & AF \\ Transmission protocols & Two time slot \\ Amplification Gain & 10 dB \\ Number of relays & 1 \\ Combining Technique & MRC \\ \hline \end{tabular}
\end{table}
Table 2: System parameters.  
Figure 7: Performance analysis of CC over direct communication (DC) without obstacle.
# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 4 Failure Prediction and Container Migration Strategies
### Problem Formulation
#### 4.1.2 GRU  
A GRU is a temporal RNN model that can remember the influence of historical input information on subsequent output results, as well as specific information at a particular time. It is very suitable for processing time-series data and extracting relevant information about adjacent features in the time dimension [31]. As shown in Figure 2, the large-capacity deep network obtained through the layer-by-layer connection of GRU neurons can be used to process complex sequence data, further improving prediction accuracy. Therefore, considering the complexity of fault symptoms, we applied a multilayer GRU deep network to extract the temporal characteristics in the time window before failure, as shown in Algorithm 2.  
However, multilayer GRU deep networks have certain drawbacks. First, owing to a high level of abstraction, some features that are important for distinguishing the operational state of a node and the type of failure are lost, contrary to our initial goal of obtaining more feature details. Second, with the deepening of the GRU network, the training efficiency of the DNN is reduced, which is not suitable for online failure prediction. Finally, the tanh activation function is prone to gradient vanishing problems, resulting in wandering at a certain point and an inability to determine the optimal solution.  
Input: Trained GRU model: A set of time-sequence data from edge devices.  
Output: Edge-device failure prediction results at the next moment  
Procedure:  
1: while (there exists new measurement data x)  
2: do  
3: result = use the GRU model to predict the next failure situation;  
4: if the result is failure  
5: output the result and break;  
6: end while  
**Algorithm 2** GRU-based fault-prediction algorithm.
## 4 Results and Discussion
### System COP as the Objective Function  
Taking the optimization objective as the highest system COP, the simulation results are shown in Figures 9-11.  
Figure 8: Electricity consumption in each period when the objective function is operating costs.  
Figure 9: Buried pipe flow in each period when the objective function is system COP.  
Figure 9 illustrates the flow rate in buried pipes in each period after optimization, from which it can be seen that the flow rate in buried pipes fluctuates constantly, but always maintains a high level, with a maximum of 14 m\({}^{3}\)/h and a minimum of 11.75 m\({}^{3}\)/h.  
Figure 10 compares the heat storage and the consumers' heat load of each period after optimization. It can be seen that the variation trend in the heat storage is basically decreasing first and then increasing as time goes by. Therefore, the working conditions of the heat storage tank are mainly divided into two stages: 1:00-10:00 is the heat release stage, and 10:00-20:00 is the heat storage stage. The heat storage capacity first changes from approximately 250 kW to 0 in 1:00-10:00, and then from 0 to approximately 460 kW in 10:00-20:00. While the heat load of the consumers at 1:00-10:00 is larger and increasing, while the heat load at 10:00-20:00 is small and decreasing, which means that the working modes of the heat storage tank are determined by the heat load of the consumers at this time.  
Figure 11 shows the power consumption of each period after optimization. It can be seen that the power consumption of the heat pump changes very little and the total power consumption and water pump power consumption fluctuate within a small range. Therefore, in order to obtain a high system COP, the buried pipe flow rate should be  
Figure 11: Electricity consumption in each period when the objective function is system COP.  
Figure 10: Heat storage capacity and heating load in each period when the objective function is system COP.  
maintained at a high level, and the heat storage tank should store or release heat according to the user's heat load.
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 4 Analysis and Comparison
### Security Analysis
#### 4.3.2 Security Connectivity  
The security connectivity is defined as the probability that two nodes successfully establish a session key. Since authentication and key distribution in our proposal are cluster based, we define \"inter-cluster connectivity\" as the probability that a CH shares a pairwise key with the sensors in its cluster.  
In our deterministic key distribution scheme, each authenticated sensor can always successfully share a session key with the present cluster head. Compared to the probabilistic key distribution approaches in [30; 31; 41], the inter-cluster connectivity in our scheme is 100%. Those random schemes, like AP [30], can only achieve higher security connectivity by increasing the amount of key storage. Figure 7 depicts the comparison of secure connectivity and key pool size in the AP. As the number of pre-loaded keys increases, the performance of the secure connectivity gradually improves. For fixed parameters [_l_, _M_], the security connectivity decreases significantly as the key pool increases.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Du [30]** & **Erfani [40]** & **Our Scheme** \\ \hline Sensor & 32\(l\) & 32 (\(\alpha\) and \(\beta\)) & 64 \\ \hline Cluster Head & 32\(M\) & 32 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key storage overheads (bytes) in different schemes.
## 3 Optimization Strategy
### Equilibrium Forces  
An essential point for the local frequency constraint transformation is to establish the static load case.  
Since the gained weight of reinforcement structural is small relative to the weight of original structure, there is little to no impact in the element mass matrix, which corresponded to Assumption 1. However, structural modification has a major impact on the element stiffness and the change in it should not be overlooked [17]. Therefore, the stiffness matrix is assumed to be a linear function of design variables in optimization problem.  
\[K=K(X) \tag{2}\]  
when analyzing the dynamics of an undamped structure, the characteristic equation can be expressed in the following matrix form  
\[\left[K-\omega_{i}^{2}M\right]\varphi_{i}=0,i=1,2,\ldots,n \tag{3}\]  
where \(\omega_{i}\) is the natural frequency or eigenvalue of the \(i\)-th mode, and \(\varphi_{i}\) is the corresponding natural modal shape or eigenvector.  
The mode of the normalization of the mass matrix can be expressed as  
\[\varphi_{i}^{T}M\varphi_{i}=1 \tag{4}\]  
According to Equation (3), considering the local mode of initial design, it can be written as  
\[K\left(X^{0}\right)\varphi_{local}^{0}=\left(\omega_{local}^{0}\right)^{2}M \varphi_{local}^{0} \tag{5}\]  
where \(K(X^{0})\) is the element stiffness matrix of the initial design. Similarly, considering the local mode of current design, the equation can be written as  
\[K(X)\varphi_{local}^{0}=\left(\omega_{local}\right)^{2}M\varphi_{local}^{0} \tag{6}\]  
where \(K(X)\) is the element stiffness matrix of the current design and the mass matrix \(M\) remains constant.  
According to Assumption 2, Equation (6) can be expressed as  
\[K(X)\varphi_{local}^{0}=(\omega_{local})^{2}M\varphi_{local}^{0} \tag{7}\]  
Combining Equations (5) and (7), and removing the common item from both equations, we can further relate the element stiffness matrix in the initial and current design as  
\[K\left(X^{0}\right)\varphi_{local}^{0}=\frac{\left(\omega_{local}^{0}\right) ^{2}}{(\omega_{local})^{2}}K(X)\varphi_{local}^{0} \tag{8}\]To further transform the modal frequency information, reading the specified local modal shape for reference, we can define a virtual equivalent external load \(F_{local}\) as  
\[F_{local}=K\Big{(}X^{0}\Big{)}\varphi_{local}^{0} \tag{9}\]  
when the modal analysis of the initial structure is conducted, the equivalent external load can be obtained as well.
## 3 Materials and Methods  
### Study Area and Data  
3.1.1. Location, Hydrology, and Climate  
The study area is in Aminteo, a municipality in Western Macedonia, Northern Greece, encompassing a hydrologically active system of four interconnected lakes: Zazari, Chimaditida, Petron, and Vegoritida. The region is predominantly rural, with a landscape that combines agricultural fields, wetlands, and forested uplands.  
A map of the watershed is presented in Figure 1, showing the stream network, major lakes, and the location of the hydrological monitoring station used in this study. This map provides a spatial overview of the catchment, visualizing the flow dynamics modeled in this work.  
Hydrologically, the system follows a connected lake sequence. Overflow from Lake Zazari is directed into Lake Chimaditida through a connecting canal. When Lake Chimaditida reaches capacity, excess water is discharged via a small overflow structure into the main drainage ditch, which serves both irrigation and flood control purposes. The area is influenced not only by natural rainfall-runoff processes but also by irrigation return flows and water management infrastructure, similar to other agricultural regions in Western Macedonia [8]. Along this ditch, near the village of Rodonas, part of the water is temporarily stored or diverted for agricultural use. The hydrological monitoring station used in this study is positioned just downstream of these inflows, providing a representative measurement of the combined natural and anthropogenic contributions before the water continues toward Lake Petron.  
Figure 1: Satellite map of the study area in Western Macedonia, Northern Greece, showing the interconnected lake system (Zazari, Chimaditida, Petron, Vegoritida) and the location of the hydrological monitoring station (red dot), along with two additional meteorological stations (yellow and green dots). Blue lines represent the delineated stream network derived from the DEM, while black lines outline the watershed boundaries. This spatial overview highlights the flow path and key hydrological features considered in the hybrid modeling framework.  
Climatically, the region exhibits a Mediterranean continental pattern, with wet winters and dry summers. Annual precipitation ranges from 550 to 600 mm, with the bulk occurring between October and April, while the summer months are typically dry and characterized by peak irrigation activity.  
Temperature varies widely throughout the year, with winter lows frequently below 0 \({}^{\circ}\)C and summer highs exceeding 30 \({}^{\circ}\)C. These seasonal dynamics are visualized in Figure 2, which presents an ombrothermic diagram summarizing the monthly distribution of temperature and precipitation.
## 2 Materials and Methods
### Contact Angle and Surface Free Energy Measurement  
To assess changes in the surface free energy resulting from the plasma treatment, the Owens, Wendt, Rabel and Kaelble method (OWRK method) was employed. This method utilizes the geometric mean value to combine polar and dispersive components, represented by the following equation.  
\[\sigma_{l}(1+\cos(\theta))=2\bigg{(}\sqrt{\sigma_{s}^{d}\sigma_{l}^{d}}+\sqrt{ \sigma_{s}^{p}\sigma_{l}^{p}}\bigg{)} \tag{1}\]  
where \(\sigma_{l}\) is the surface tension of the liquid, \(\sigma_{s}\) is the surface tension of the solid, and \(\theta\) is the contact angle between liquid, surface and atmosphere. Superscripted letters indicate the polar or dispersive component. The work method requires at least two liquids with known surface tensions, with a required polar component of near zero for one of the liquids. In this research, demineralized water and diiodomethane were utilized. The polar and dispersive values used in this research are detailed in Table 3[31].  
Up to 5 \(\mu\)L water and 3 \(\mu\)L diiodomethane were each deposited on the surface using a micro-syringe. Ten readings for each test liquid were taken at various locations on the treated surface. The ImageJ plugin for contact angles was employed to determine the values of the different readings. The mean wetting angle of diiodomethane was used to calculate the dispersive component of the surface free energy. With the value of the dispersive component and the wetting angle of water, the total surface free energy of the surface could be determined. The measurements were conducted at room temperature with approximately 50% humidity. A schematic of the contact angle measurement setup is illustrated in Figure 5.  
Figure 4: SmartPlasma 2 by plasma technology GmbH[30].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Liquid**} & \(\sigma_{l}\) & \(\sigma_{l}^{d}\) & \(\sigma_{l}^{p}\) \\  & \(\mathbf{[mN\ m^{-1}]}\) & \(\mathbf{[mN\ m^{-1}]}\) & \(\mathbf{[mN\ m^{-1}]}\) \\ \hline Water & 72.8 & 51 & 21.8 \\ Diiodomethane & 50.8 & 50.8 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Surface tensions of the used test liquids.
## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Service Provisioning
#### 4.1.2 Resource Sharing  
Resource sharing is an important concept in a KDN as multiple devices/clients may rely on the same resources, such as computational and memory resources. In [239], blockchain has been utilized along with deep reinforcement learning for secure resource sharing by developing a content caching system. Furthermore, in [240], network resources are allocated and shared using artificial intelligence, network function virtualization, and consortium blockchain in a KDN with the aid of time prediction and resource allocation algorithms. Additionally, blockchain has been utilized as a bridge to record user transactions and broadcast knowledge-based terminals' resource demands in sixth-generation networks with the aid of smart contracts, and artificial intelligence has been utilized to improve pattern recognition in Dynamic Resource Sharing (DRS) [241]. Moreover, using blockchain and self-executing contracts, an end-to-end framework utilizes the permissioned nature of the blockchain, allows service level agreements to share infrastructure, and performs federated learning among different parties involved in future 6G-Internet of Vehicles (IoV) KDNs [242]. Likewise, in portable network edges with UAVs allowed, deep reinforcement learning was successfully used for flexible compute transferring and assigning resources, while blockchain has been used for protecting and improving the offloading tasks [243]. In contrast, in IIoT networks, a framework known as ManuChain for manufacturing planning and resource sharing utilizes permissioned blockchain for decentralization, while a two-level intelligence model in which the lower tier uses self-regulating cognition while the upper tier uses holistic optimization for cognition [244].  
Figure 8: Graphical illustration of blockchain applications in knowledge-based intelligent networks.
# Toward Long-Term Implantable Glucose Biosensors for Clinical Use
## 2 Materials and Methods
### Biocompatibility  
Biocompatibility of invasive sensors is one of the most important parameters to make sensors survive in a body, resulting in less sensor failure, long lifespan, high accuracy, and good usability [30]. Since approved CGM sensors are implanted under the skin, the sensors cannot avoid the foreign body responses. If the sensors have good biocompatibility, inflammation is mild, reducing cellular biofouling and less sensor failure. Also, mild inflammation forms thin fibrous capsule surrounding sensors [31]. Then, glucose diffusion and perfusion at the implantation site can have less variation in time domain and concentration domain. Thus, biocompatibility is one of the main concerns when researchers develop CGM sensors, especially for long-term use. In addition, implanted sensors can induce skin irritation, tissue damage, and severe foreign body reaction. These symptoms contribute to dislike of having implantable sensors. Highly biocompatible CGM sensors can lower critical barriers to CGMS use: high running cost followed by short-term lifespan, low accuracy, and discomfort of having implanted sensors.  
To increase biocompatibility, we can optimize shape, size, texture, material, surgery, and properties of biointerface of CGM sensors. These parameters determine the degree of biocompatibility. For example, small-sized sensors give less damage to subcutaneous tissues, consequently reducing the foreign body response. PEGylation is a promising way to form highly biocompatible sensor-tissue interface [32; 33; 34]. PEGylated biointerface reduces inflammation and facilitates wound healing process. Such biointerface increased survival ratio of implanted sensors. Surgery procedure is also carefully considered. Small wound opening and minimal tissue damage during surgery causes less foreign body responses, thereby increasing stability and accuracy of implanted sensors. An anti-inflammatory agent can mitigate inevitable reaction to the foreign body. When dexamethasone, a strong anti-inflammatory agent, was released for a month from the implanted polylatic-co-glycolic acid (PLGA) microspheres, the acute and chronic inflammation were inhibited [35]. The commercially available, fully implantable CGMS sensor, Eversense(r) (Senseonics), also employs dexamethasone to reduce inflammation for 90 days use [36].  
Depending on a sensor scheme, biocompatibility issues can vary and efforts have to be optimized to deal with all potential matters in a particular sensor design. Adhesives used for needle-type CGM sensors can cause skin irritation that increase dislike of wearing sensors. Such discomfort is one of common reasons of stopping CGMS-based therapy. A recent study proposed process to attach the transmitter housing to the fabric patch using heat-staking with a specialized assembly apparatus. Skin under the heat-staked patch showed less skin irritation compared to skin under the patch made with the cyanoacrylate-based adhesive [37].
# A Noise-Tolerant Carrier Phase Recovery Method for Inter-Satellite Coherent Optical Communications
## 4 Simulation Results and Analysis  
The logarithmic root-mean-square error (LRMSE) of the estimated phase and the BER are employed to evaluate the performance of the proposed noise-tolerant method. For comparative analysis, the Diff-4 th methods and the Cramer-Rao lower bound (CRLB) are considered. While the CRLB provides a theoretical lower limit for the variance of an estimator, it cannot be directly applied to phase estimation due to the phase noise model being the integral of a Gaussian process. Therefore, the modified Cramer-Rao lower bound (MCRLB) is utilized as a benchmark for performance evaluation [30]. Key parameters, including the symbol rate \(1/T\), laser linewidth \(\delta_{v}\), wavelength \(\lambda\), and residual Doppler FO \(\Delta f\) are detailed in Table 2. To ensure sufficient oversampling, the symbol rate was set to 2.5 Gsps, aligning with the capabilities of commercial discrete ADC devices, which operate at sampling rates slightly above 5 Gsps. The laser wavelength was chosen as 1550 nm, a standard in optical communication, while the laser linewidth of 10 kHz reflects the specification of mature, commercially available device. A Doppler FO of 20 MHz was selected, which is feasible within short time intervals. Additionally, the RS (255, 223) coding method, recommended by the Consultative Committee for Space Data Systems (CCSDS) for satellite communications, was employed for encoding and decoding. The system's performance is evaluated through Monte Carlo simulations. Without the loss of generality, the SNR is denoted as \(E_{b}/N_{0}\), where \(E_{b}\) represents the energy per bit (with \(E_{b}=E_{s}/2\) for QPSK). The LRMSE for phase estimation is defined by the following formula:  
\[\text{LRMSE}_{\theta}=20\log_{10}\sqrt{\frac{1}{K}\sum_{k=1}^{K}(\theta_{\text {err}}[k])^{2}}, \tag{13}\]  
where \(\theta_{\text{err}}[k]=\theta[k]-\hat{\theta}[k]\) represents the phase estimation error.
## 3 Metabolomics Applied to Marine Organisms in Cancer Studies
### Structure Elucidation of Bioactive Molecules from Sponges
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sponge** & **Compound/Extract** & **Cell Lines** & **Metabolomics** & **Reference** \\ \hline _S. carteri_ & Stylissamide A and Stylissoside A & MCF7 and HepG2 & LC-MS & [93] \\ \hline \multirow{2}{*}{_C. siphonella_} & 5-bromotrisindoline and & \multirow{2}{*}{HT29, OVCAR3 and MM.1S} & \multirow{2}{*}{LC-MS} & [94] \\  & 6-bromotrisindoline & & & \\ \hline _H. rosen_ & 3-alkyl pyridine alkaloids & SKBR3 & MS & [95] \\ \hline _Cascioderma_ sp. & Crude extract & HepG2, MCF7 and Caco2 & LC-MS & [97] \\ \hline _G. macandreuvii_ & Geoditaturine & A2058 & UHPLC and MS & [98] \\ \hline \multirow{2}{*}{_L. biformis_} & \((-)\)–discorhabdin L, & \multirow{2}{*}{HCT116} & \multirow{2}{*}{1H-NMR and MS} & [99] \\  & \((-)\)–1-acetyl-discorhabdin L and & & & \\ \end{tabular}
\end{table} 
Table 1: Sponge species, compound/extract, cell lines tested, metabolomics techniques and corresponding reference are reported. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sponge** & **Compound/Extract** & **Cell Lines** & **Metabolomics** & **Reference** \\ \hline \multirow{3}{*}{_T. sxinhoai_} & Theopalaumide, & & & \\  & 5-cis-Apoa-theopalaumide and & HCT116 & 1D and 2D NMR; MS & [100] \\  & Theonellamide K & & & \\ \hline \multirow{3}{*}{_P. ficiformis_} & Petrocidin A and & & & \\  & 2,3-Dihydroxybenzamide & HL60 and HT29 & LC-MS & [105] \\ \hline \multirow{3}{*}{_Callyspongia_ sp.} & Nocardiide A & CT26, HeLa and MM.1S & MS & [106] \\ \hline _Amphimedon_ sp. & Crude extract & HepG2, MCF7 and Caco2 & MS & [107] \\ \hline \multirow{3}{*}{_Callyspongia_ sp. and _S. oragbunda_} & \multirow{3}{*}{_N-(2-hydroxyphenyl)-acetamide_} & HCT116, HepG2 and MCF7 & LC-MS & [108] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 5: Examples of chemical structures of natural compounds from sponges: _N_-(2-hydroxyphenyl)-acetamide [108], Petrocidin A and 2,3-Dihydroxybenzamide [105], 6-Bromotrisindoline [94], Geodi-ataurine [98] and 5-cis-Apoa-theopalaumide [100].